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Hongxia Jin

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ICLR Conference 2025 Conference Paper

MoDeGPT: Modular Decomposition for Large Language Model Compression

  • Chi-Heng Lin
  • Shangqian Gao
  • James Seale Smith
  • Abhishek Patel
  • Shikhar Tuli
  • Yilin Shen
  • Hongxia Jin
  • Yen-Chang Hsu

Large Language Models (LLMs) have significantly advanced AI with their exceptional performance across a wide range of tasks. However, their extensive computational requirements restrict their use on devices with limited resources. While recent compression methods based on low-rank matrices show potential solutions, they often suffer from significant loss of accuracy or introduce substantial overhead in parameters and inference time. In this paper, we introduce Modular De- composition (MoDeGPT), a new, efficient, and structured compression framework that overcomes these limitations. MoDeGPT jointly decomposes pairs of consecu- tive subcomponents within Transformer blocks, reduces hidden dimensions through output reconstruction on a larger structural scale than conventional low-rank meth- ods, and repurposes three classical matrix decomposition algorithms—Nyström approximation, CR decomposition, and SVD—to ensure bounded errors in our novel decomposition approach. Our experiments show that MoDeGPT, without relying on backward propagation, consistently matches or surpasses the performance of prior techniques that depend on gradient information, while achieving a 98% reduction in compute costs when compressing a 13B-parameter model. On LLaMA-2/3 and OPT models, MoDeGPT retains 90-95% of zero-shot performance with compression rates of 25-30%. The compression process can be completed on a single GPU in a few hours, boosting inference throughput by up to 46%.

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ICML Conference 2025 Conference Paper

RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior

  • Ching Hua Lee
  • Chouchang Yang
  • Jaejin Cho
  • Yashas Malur Saidutta
  • Rakshith Sharma Srinivasa
  • Yilin Shen
  • Hongxia Jin

Denoising diffusion probabilistic models (DDPMs) can be utilized to recover a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass certain useful information about the target clean data distribution. However, existing adoption of the standard Gaussian as the prior distribution in turn discards such information when shaping the prior, resulting in sub-optimal performance. In this paper, we propose to improve conditional DDPMs for signal restoration by leveraging a more informative prior that is jointly learned with the diffusion model. The proposed framework, called RestoreGrad, seamlessly integrates DDPMs into the variational autoencoder (VAE) framework, taking advantage of the correlation between the degraded and clean signals to encode a better diffusion prior. On speech and image restoration tasks, we show that RestoreGrad demonstrates faster convergence (5-10 times fewer training steps) to achieve better quality of restored signals over existing DDPM baselines and improved robustness to using fewer sampling steps in inference time (2-2. 5 times fewer), advocating the advantages of leveraging jointly learned prior for efficiency improvements in the diffusion process.

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ICLR Conference 2024 Conference Paper

AlpaGasus: Training a Better Alpaca with Fewer Data

  • Lichang Chen
  • Shiyang Li
  • Jun Yan
  • Hai Wang
  • Kalpa Gunaratna
  • Vikas Yadav
  • Zheng Tang
  • Vijay Srinivasan

Large language models~(LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce Alpagasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. Alpagasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and the controlled human study. Its 13B variant matches $>90\%$ performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes \footnote{We apply IFT for the same number of epochs as Alpaca(7B) but on fewer data, using 4$\times$NVIDIA A100 (80GB) GPUs and following the original Alpaca setting and hyperparameters.}. In the experiment, we also demonstrate that our method can work not only for machine-generated datasets but also for human-written datasets. Overall, Alpagasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models.

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NeurIPS Conference 2024 Conference Paper

CIFD: Controlled Information Flow to Enhance Knowledge Distillation

  • Yashas M. Saidutta
  • Rakshith S. Srinivasa
  • Jaejin Cho
  • Ching-Hua Lee
  • Chouchang Yang
  • Yilin Shen
  • Hongxia Jin

Knowledge Distillation is the mechanism by which the insights gained from a larger teacher model are transferred to a smaller student model. However, the transfer suffers when the teacher model is significantly larger than the student. To overcome this, prior works have proposed training intermediately sized models, Teacher Assistants (TAs) to help the transfer process. However, training TAs is expensive, as training these models is a knowledge transfer task in itself. Further, these TAs are larger than the student model and training them especially in large data settings can be computationally intensive. In this paper, we propose a novel framework called Controlled Information Flow for Knowledge Distillation (CIFD) consisting of two components. First, we propose a significantly smaller alternatives to TAs, the Rate-Distortion Module (RDM) which uses the teacher's penultimate layer embedding and a information rate-constrained bottleneck layer to replace the Teacher Assistant model. RDMs are smaller and easier to train than TAs, especially in large data regimes, since they operate on the teacher embeddings and do not need to relearn low level input feature extractors. Also, by varying the information rate across the bottleneck, RDMs can replace TAs of different sizes. Secondly, we propose the use of Information Bottleneck Module in the student model, which is crucial for regularization in the presence of a large number of RDMs. We show comprehensive state-of-the-art results of the proposed method over large datasets like Imagenet. Further, we show the significant improvement in distilling CLIP like models over a huge 12M image-text dataset. It outperforms CLIP specialized distillation methods across five zero-shot classification datasets and two zero-shot image-text retrieval datasets.

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TMLR Journal 2024 Journal Article

Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA

  • James Seale Smith
  • Yen-Chang Hsu
  • Lingyu Zhang
  • Ting Hua
  • Zsolt Kira
  • Yilin Shen
  • Hongxia Jin

Recent works demonstrate a remarkable ability to customize text-to-image diffusion models while only providing a few example images. What happens if you try to customize such models using multiple, fine-grained concepts in a sequential (i.e., continual) manner? In our work, we show that recent state-of-the-art customization of text-to-image models suffer from catastrophic forgetting when new concepts arrive sequentially. Specifically, when adding a new concept, the ability to generate high quality images of past, similar concepts degrade. To circumvent this forgetting, we propose a new method, C-LoRA, composed of a continually self-regularized low-rank adaptation in cross attention layers of the popular Stable Diffusion model. Furthermore, we use customization prompts which do not include the word of the customized object (i.e., "person" for a human face dataset) and are initialized as completely random embeddings. Importantly, our method induces only marginal additional parameter costs and requires no storage of user data for replay. We show that C-LoRA not only outperforms several baselines for our proposed setting of text-to-image continual customization, which we refer to as Continual Diffusion, but that we achieve a new state-of-the-art in the well-established rehearsal-free continual learning setting for image classification. The high achieving performance of C-LoRA in two separate domains positions it as a compelling solution for a wide range of applications, and we believe it has significant potential for practical impact.

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NeurIPS Conference 2024 Conference Paper

DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models

  • Shangqian Gao
  • Chi-Heng Lin
  • Ting Hua
  • Tang Zheng
  • Yilin Shen
  • Hongxia Jin
  • Yen-Chang Hsu

Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, including language modeling, understanding, and generation. However, the increased memory and computational costs associated with these models pose significant challenges for deployment on resource-limited devices. Structural pruning has emerged as a promising solution to reduce the costs of LLMs without requiring post-processing steps. Prior structural pruning methods either follow the dependence of structures at the cost of limiting flexibility, or introduce non-trivial additional parameters by incorporating different projection matrices. In this work, we propose a novel approach that relaxes the constraint imposed by regular structural pruning methods and eliminates the structural dependence along the embedding dimension. Our dimension-independent structural pruning method offers several benefits. Firstly, our method enables different blocks to utilize different subsets of the feature maps. Secondly, by removing structural dependence, we facilitate each block to possess varying widths along its input and output dimensions, thereby significantly enhancing the flexibility of structural pruning. We evaluate our method on various LLMs, including OPT, LLaMA, LLaMA-2, Phi-1. 5, and Phi-2. Experimental results demonstrate that our approach outperforms other state-of-the-art methods, showing for the first time that structural pruning can achieve an accuracy similar to semi-structural pruning.

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NeurIPS Conference 2024 Conference Paper

Unleashing Multispectral Video's Potential in Semantic Segmentation: A Semi-supervised Viewpoint and New UAV-View Benchmark

  • Wei Ji
  • Jingjing Li
  • Wenbo Li
  • Yilin Shen
  • Li Cheng
  • Hongxia Jin

Thanks to the rapid progress in RGB & thermal imaging, also known as multispectral imaging, the task of multispectral video semantic segmentation, or MVSS in short, has recently drawn significant attentions. Noticeably, it offers new opportunities in improving segmentation performance under unfavorable visual conditions such as poor light or overexposure. Unfortunately, there are currently very few datasets available, including for example MVSeg dataset that focuses purely toward eye-level view; and it features the sparse annotation nature due to the intensive demands of labeling process. To address these key challenges of the MVSS task, this paper presents two major contributions: the introduction of MVUAV, a new MVSS benchmark dataset, and the development of a dedicated semi-supervised MVSS baseline - SemiMV. Our MVUAV dataset is captured via Unmanned Aerial Vehicles (UAV), which offers a unique oblique bird’s-eye view complementary to the existing MVSS datasets; it also encompasses a broad range of day/night lighting conditions and over 30 semantic categories. In the meantime, to better leverage the sparse annotations and extra unlabeled RGB-Thermal videos, a semi-supervised learning baseline, SemiMV, is proposed to enforce consistency regularization through a dedicated Cross-collaborative Consistency Learning (C3L) module and a denoised temporal aggregation strategy. Comprehensive empirical evaluations on both MVSeg and MVUAV benchmark datasets have showcased the efficacy of our SemiMV baseline.

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NeurIPS Conference 2023 Conference Paper

CWCL: Cross-Modal Transfer with Continuously Weighted Contrastive Loss

  • Rakshith Sharma Srinivasa
  • Jaejin Cho
  • Chouchang Yang
  • Yashas Malur Saidutta
  • Ching-Hua Lee
  • Yilin Shen
  • Hongxia Jin

This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used for a diverse set of tasks in a 0-shot way, similar to Contrastive Language-Image Pre-training (CLIP) and Locked-image Tuning (LiT) that have recently gained considerable attention. Classical contrastive training employs sets of positive and negative examples to align similar and repel dissimilar training data samples. However, similarity amongst training examples has a more continuous nature, thus calling for a more `non-binary' treatment. To address this, we propose a new contrastive loss function called Continuously Weighted Contrastive Loss (CWCL) that employs a continuous measure of similarity. With CWCL, we seek to transfer the structure of the embedding space from one modality to another. Owing to the continuous nature of similarity in the proposed loss function, these models outperform existing methods for 0-shot transfer across multiple models, datasets and modalities. By using publicly available datasets, we achieve 5-8% (absolute) improvement over previous state-of-the-art methods in 0-shot image classification and 20-30% (absolute) improvement in 0-shot speech-to-intent classification and keyword classification.

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ICML Conference 2023 Conference Paper

ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation

  • Kaiwen Zhou 0002
  • Kaizhi Zheng
  • Connor Pryor
  • Yilin Shen
  • Hongxia Jin
  • Lise Getoor
  • Xin Eric Wang

The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments. In this work, we present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC), that transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience nor any other training on the visual environments. First, ESC leverages a pre-trained vision and language model for open-world prompt-based grounding and a pre-trained commonsense language model for room and object reasoning. Then ESC converts commonsense knowledge into navigation actions by modeling it as soft logic predicates for efficient exploration. Extensive experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines, and achieves new state-of-the-art results for zero-shot object navigation (e. g. , 288% relative Success Rate improvement than CoW on MP3D).

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AAAI Conference 2023 Conference Paper

GOHSP: A Unified Framework of Graph and Optimization-Based Heterogeneous Structured Pruning for Vision Transformer

  • Miao Yin
  • Burak Uzkent
  • Yilin Shen
  • Hongxia Jin
  • Bo Yuan

The recently proposed Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks, and they are viewed as an important type of foundation model. However, ViTs are typically constructed with large-scale sizes, which then severely hinder their potential deployment in many practical resources constrained applications. To mitigate this challenging problem, structured pruning is a promising solution to compress model size and enable practical efficiency. However, unlike its current popularity for CNNs and RNNs, structured pruning for ViT models is little explored. In this paper, we propose GOHSP, a unified framework of Graph and Optimization-based Structured Pruning for ViT models. We first develop a graph-based ranking for measuring the importance of attention heads, and the extracted importance information is further integrated to an optimization-based procedure to impose the heterogeneous structured sparsity patterns on the ViT models. Experimental results show that our proposed GOHSP demonstrates excellent compression performance. On CIFAR-10 dataset, our approach can bring 40% parameters reduction with no accuracy loss for ViT-Small model. On ImageNet dataset, with 30% and 35% sparsity ratio for DeiT-Tiny and DeiT-Small models, our approach achieves 1.65% and 0.76% accuracy increase over the existing structured pruning methods, respectively.

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ICLR Conference 2023 Conference Paper

Learning to Jointly Share and Prune Weights for Grounding Based Vision and Language Models

  • Shangqian Gao
  • Burak Uzkent
  • Yilin Shen
  • Heng Huang 0001
  • Hongxia Jin

Transformers have seen growing interest in processing different modalities, including language and image data. As a result, we can process vision and language data using transformers that are architecturally similar. Leveraging this feature of transformers, we propose weight sharing across two transformer backbones and within the same transformer backbone and pruning across two backbones in a unified framework. More specifically, we investigate weight sharing and pruning for two components of the transformers: (1) Multi-Head Attention (MSA) and (2) Feed-Forward Network (FFN) layers. To jointly perform weight sharing and pruning, we propose to use a regularization term to align model weights and the desired structure during the multimodal pre-training step. The structure vectors of sharing and pruning are generated by using a hypernetwork, which can capture complex interactions between pruning and sharing across layers and modalities. We train the hypernetwork and model weights iteratively so that the learned structure evolves along with model weights. After minimizing the proposed objective in the pre-training step, we perform weight sharing and pruning and fine-tune the compressed model on downstream tasks. Finally, we perform experiments on vision and language tasks, including Referring Expression Comprehension (REC), Visual Question Answering (VQA), and Object Detection using the state-of-the-art grounding based models: MDETR and GLIP. Our experiments show that we can compress these models by $35-40\%$ by sharing and pruning MSA and FFN weights without almost any loss in accuracy.

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ICLR Conference 2022 Conference Paper

DictFormer: Tiny Transformer with Shared Dictionary

  • Qian Lou
  • Ting Hua
  • Yen-Chang Hsu
  • Yilin Shen
  • Hongxia Jin

We introduce DictFormer with the efficient shared dictionary to provide a compact, fast, and accurate transformer model. DictFormer significantly reduces the redundancy in the transformer's parameters by replacing the prior transformer's parameters with a compact, shared dictionary, few unshared coefficients, and indices. Also, DictFormer enables faster computations since expensive weights multiplications are converted into cheap shared look-ups on dictionary and few linear projections. Training dictionary and coefficients are not trivial since indices used for looking up dictionary are not differentiable. We adopt a sparse-constraint training with $l_1\,\,norm$ relaxation to learn coefficients and indices in DictFormer. DictFormer is flexible to support different model sizes by dynamically changing dictionary size. Compared to existing lightweight Transformers, DictFormer consistently reduces model size over Transformer on multiple tasks, e.g., machine translation, abstractive summarization, and language modeling. Extensive experiments show that DictFormer reduces $3.6\times$ to $8.9\times$ model size with similar accuracy over multiple tasks, compared to Transformer.

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AAAI Conference 2022 Conference Paper

Improving Zero-Shot Phrase Grounding via Reasoning on External Knowledge and Spatial Relations

  • Zhan Shi
  • Yilin Shen
  • Hongxia Jin
  • Xiaodan Zhu

Phrase grounding is a multi-modal problem that localizes a particular noun phrase in an image referred to by a text query. In the challenging zero-shot phrase grounding setting, the existing state-of-the-art grounding models have limited capacity in handling the unseen phrases. Humans, however, can ground novel types of objects in images with little effort, significantly benefiting from reasoning with commonsense. In this paper, we design a novel phrase grounding architecture that builds multi-modal knowledge graphs using external knowledge and then performs graph reasoning and spatial relation reasoning to localize the referred nouns phrases. We perform extensive experiments on different zero-shot grounding splits sub-sampled from the Flickr30K Entity and Visual Genome dataset, demonstrating that the proposed framework is orthogonal to backbone image encoders and outperforms the baselines by 2∼3% in accuracy, resulting in a significant improvement under the standard evaluation metrics.

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AAAI Conference 2022 Conference Paper

ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs

  • Manas Gaur
  • Kalpa Gunaratna
  • Vijay Srinivasan
  • Hongxia Jin

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users’ needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the enduser. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generativeadversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. The qualitative human evaluation confirms ISEEQ-generated ISQs are comparable in quality to human-generated questions and outperform the best comparable baseline.

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ICLR Conference 2022 Conference Paper

Language model compression with weighted low-rank factorization

  • Yen-Chang Hsu
  • Ting Hua
  • Sungen Chang
  • Qian Lou
  • Yilin Shen
  • Hongxia Jin

Factorizing a large matrix into small matrices is a popular strategy for model compression. Singular value decomposition (SVD) plays a vital role in this compression strategy, approximating a learned matrix with fewer parameters. However, SVD minimizes the squared error toward reconstructing the original matrix without gauging the importance of the parameters, potentially giving a larger reconstruction error for those who affect the task accuracy more. In other words, the optimization objective of SVD is not aligned with the trained model's task accuracy. We analyze this previously unexplored problem, make observations, and address it by introducing Fisher information to weigh the importance of parameters affecting the model prediction. This idea leads to our method: Fisher-Weighted SVD (FWSVD). Although the factorized matrices from our approach do not result in smaller reconstruction errors, we find that our resulting task accuracy is much closer to the original model's performance. We perform analysis with the transformer-based language models, showing our weighted SVD largely alleviates the mismatched optimization objectives and can maintain model performance with a higher compression rate. Our method can directly compress a task-specific model while achieving better performance than other compact model strategies requiring expensive model pre-training. Moreover, the evaluation of compressing an already compact model shows our method can further reduce 9% to 30% parameters with an insignificant impact on task accuracy.

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AAAI Conference 2022 Conference Paper

Text-Based Interactive Recommendation via Offline Reinforcement Learning

  • Ruiyi Zhang
  • Tong Yu
  • Yilin Shen
  • Hongxia Jin

Interactive recommendation with natural-language feedback can provide richer user feedback and has demonstrated advantages over traditional recommender systems. However, the classical online paradigm involves iteratively collecting experience via interaction with users, which is expensive and risky. We consider an offline interactive recommendation to exploit arbitrary experience collected by multiple unknown policies. A direct application of policy learning with such fixed experience suffers from the distribution shift. To tackle this issue, we develop a behavior-agnostic off-policy correction framework to make offline interactive recommendation possible. Specifically, we leverage the conservative Q-function to perform off-policy evaluation, which enables learning effective policies from fixed datasets without further interactions. Empirical results on the simulator derived from real-world datasets demonstrate the effectiveness of our proposed offline training framework.

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IJCAI Conference 2021 Conference Paper

Automatic Mixed-Precision Quantization Search of BERT

  • Changsheng Zhao
  • Ting Hua
  • Yilin Shen
  • Qian Lou
  • Hongxia Jin

Pre-trained language models such as BERT have shown remarkable effectiveness in various natural language processing tasks. However, these models usually contain millions of parameters, which prevent them from the practical deployment on resource-constrained devices. Knowledge distillation, Weight pruning, and Quantization are known to be the main directions in model compression. However, compact models obtained through knowledge distillation may suffer from significant accuracy drop even for a relatively small compression ratio. On the other hand, there are only a few attempts based on quantization designed for natural language processing tasks, and they usually require manual setting on hyper-parameters. In this paper, we proposed an automatic mixed-precision quantization framework designed for BERT that can conduct quantization and pruning simultaneously. Specifically, our proposed method leverages Differentiable Neural Architecture Search to assign scale and precision for parameters in each sub-group automatically, and at the same pruning out redundant groups of parameters. Extensive evaluations on BERT downstream tasks reveal that our proposed method beats baselines by providing the same performance with much smaller model size. We also show the possibility of obtaining the extremely light-weight model by combining our solution with orthogonal methods such as DistilBERT.

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ICLR Conference 2021 Conference Paper

Negative Data Augmentation

  • Abhishek Sinha
  • Kumar Ayush
  • Jiaming Song
  • Burak Uzkent
  • Hongxia Jin
  • Stefano Ermon

Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA) that intentionally create out-of-distribution samples. We show that such negative out-of-distribution samples provide information on the support of the data distribution, and can be leveraged for generative modeling and representation learning. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. We prove that under suitable conditions, optimizing the resulting objective still recovers the true data distribution but can directly bias the generator towards avoiding samples that lack the desired structure. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities. Further, we incorporate the same negative data augmentation strategy in a contrastive learning framework for self-supervised representation learning on images and videos, achieving improved performance on downstream image classification, object detection, and action recognition tasks. These results suggest that prior knowledge on what does not constitute valid data is an effective form of weak supervision across a range of unsupervised learning tasks.

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ICLR Conference 2021 Conference Paper

SAFENet: A Secure, Accurate and Fast Neural Network Inference

  • Qian Lou
  • Yilin Shen
  • Hongxia Jin
  • Lei Jiang 0001

The advances in neural networks have driven many companies to provide prediction services to users in a wide range of applications. However, current prediction systems raise privacy concerns regarding the user's private data. A cryptographic neural network inference service is an efficient way to allow two parties to execute neural network inference without revealing either party’s data or model. Nevertheless, existing cryptographic neural network inference services suffer from huge running latency; in particular, the latency of communication-expensive cryptographic activation function is 3 orders of magnitude higher than plaintext-domain activation function. And activations are the necessary components of the modern neural networks. Therefore, slow cryptographic activation has become the primary obstacle of efficient cryptographic inference. In this paper, we propose a new technique, called SAFENet, to enable a Secure, Accurate and Fast nEural Network inference service. To speedup secure inference and guarantee inference accuracy, SAFENet includes channel-wise activation approximation with multiple-degree options. This is implemented by keeping the most useful activation channels and replacing the remaining, less useful, channels with various-degree polynomials. SAFENet also supports mixed-precision activation approximation by automatically assigning different replacement ratios to various layer; further increasing the approximation ratio and reducing inference latency. Our experimental results show SAFENet obtains the state-of-the-art inference latency and performance, reducing latency by $38\% \sim 61\%$ or improving accuracy by $1.8\% \sim 4\%$ over prior techniques on various encrypted datasets.

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IROS Conference 2020 Conference Paper

Explainable and Efficient Sequential Correlation Network for 3D Single Person Concurrent Activity Detection

  • Yi Wei 0006
  • Wenbo Li 0001
  • Ming-Ching Chang
  • Hongxia Jin
  • Siwei Lyu

We present the sequential correlation network (SCN) to improve concurrent activity detection. SCN combines a recurrent neural network and a correlation model hierarchically to model the complex correlations and temporal dynamics of concurrent activities. SCN has several advantages that enable effective learning even from a small dataset for real-world deployment. Unlike the majority of approaches assuming that each subject performs one activity at a time, SCN is end-to- end trainable, i. e. , it can automatically learn the inclusive or exclusive relations of concurrent activities. SCN is lightweight in design using only a small set of learnable parameters to model the spatio-temporal correlations of activities. This also enhances the explainability of the learned parameters. Furthermore, the learning of SCN can benefit from the initialization using semantically meaningful priors. We evaluate the proposed method against the state-of-the-art method on two benchmark datasets with human skeletal data, SCN achieves comparable performance to the SOTA but with much faster inference speed and less memory usage.

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AAAI Conference 2020 Conference Paper

Towards Hands-Free Visual Dialog Interactive Recommendation

  • Tong Yu
  • Yilin Shen
  • Hongxia Jin

With the recent advances of multimodal interactive recommendations, the users are able to express their preference by natural language feedback to the item images, to find the desired items. However, the existing systems either retrieve only one item or require the user to specify (e. g. , by click or touch) the commented items from a list of recommendations in each user interaction. As a result, the users are not hands-free and the recommendations may be impractical. We propose a hands-free visual dialog recommender system to interactively recommend a list of items. At each time, the system shows a list of items with visual appearance. The user can comment on the list in natural language, to describe the desired features they further want. With these multimodal data, the system chooses another list of items to recommend. To understand the user preference from these multimodal data, we develop neural network models which identify the described items among the list and further predict the desired attributes. To achieve efficient interactive recommendations, we leverage the inferred user preference and further develop a novel bandit algorithm. Specifically, to avoid the system exploring more than needed, the desired attributes are utilized to reduce the exploration space. More importantly, to achieve sample efficient learning in this hands-free setting, we derive additional samples from the user’s relative preference expressed in natural language and design a pairwise logistic loss in bandit learning. Our bandit model is jointly updated by the pairwise logistic loss on the additional samples derived from natural language feedback and the traditional logistic loss. The empirical results show that the probability of finding the desired items by our system is about 3 times as high as that by the traditional interactive recommenders, after a few user interactions.

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AAAI Conference 2019 Conference Paper

A Deep Reinforcement Learning Based Multi-Step Coarse to Fine Question Answering (MSCQA) System

  • Yu Wang
  • Hongxia Jin

In this paper, we present a multi-step coarse to fine question answering (MSCQA) system which can efficiently processes documents with different lengths by choosing appropriate actions. The system is designed using an actor-critic based deep reinforcement learning model to achieve multistep question answering. Compared to previous QA models targeting on datasets mainly containing either short or long documents, our multi-step coarse to fine model takes the merits from multiple system modules, which can handle both short and long documents. The system hence obtains a much better accuracy and faster trainings speed compared to the current state-of-the-art models. We test our model on four QA datasets, WIKEREADING, WIKIREADING LONG, CNN and SQuAD, and demonstrate 1. 3%-1. 7% accuracy improvements with 1. 5x-3. 4x training speed-ups in comparison to the baselines using state-of-the-art models.

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AAMAS Conference 2019 Conference Paper

A New Concept of Convex based Multiple Neural Networks Structure

  • Yu Wang
  • Yue Deng
  • Yilin Shen
  • Hongxia Jin

In this paper, a new concept of convex based multiple neural networks structure is proposed. This new approach uses the collective information from multiple neural networks to train the model. From both theoretical and experimental analysis, it is going to demonstrate that the new approach gives a faster training speed of convergence with a similar or even better test accuracy, compared to a conventional neural network structure. Two experiments are conducted to demonstrate the performance of our new structure: the first one is a semantic frame parsing task for spoken language understanding (SLU) on ATIS dataset, and the other is a hand written digits recognition task on MNIST dataset. We test this new structure using both recurrent neural network and convolutional neural networks through these two tasks. The results of both experiments demonstrate a 4x-8x faster training speed with better or similar performance by using this new concept.

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IJCAI Conference 2019 Conference Paper

Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction

  • Yue Deng
  • Yilin Shen
  • Hongxia Jin

We introduce an adversarial-critic-and-assistant (ACA) learning framework to improve the performance of existing supervised learning with multiple outputs. The core contribution of our ACA is the innovation of two novel modules, i. e. an `adversarial critic' and a `collaborative assistant', that are jointly designed to provide augmenting information for facilitating general learning tasks. Our approach is not intended to be regarded as an emerging competitor for tons of well-established algorithms in the field. In fact, most existing approaches, while implemented with different learning objectives, can all be adopted as building blocks seamlessly integrated in the ACA framework to accomplish various real-world tasks. We show the performance and generalization ability of ACA on diverse learning tasks including multi-label classification, attributes prediction and sequence-to-sequence generation.

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NeurIPS Conference 2019 Conference Paper

Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning

  • Ruiyi Zhang
  • Tong Yu
  • Yilin Shen
  • Hongxia Jin
  • Changyou Chen

Text-based interactive recommendation provides richer user preferences and has demonstrated advantages over traditional interactive recommender systems. However, recommendations can easily violate preferences of users from their past natural-language feedback, since the recommender needs to explore new items for further improvement. To alleviate this issue, we propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference, which is incorporated into the standard RL objective of maximizing expected cumulative future rewards. Our proposed framework is general and is further extended to the task of constrained text generation. Empirical results show that the proposed method yields consistent improvement relative to standard RL methods.

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IJCAI Conference 2018 Conference Paper

Adversarial Active Learning for Sequences Labeling and Generation

  • Yue Deng
  • KaWai Chen
  • Yilin Shen
  • Hongxia Jin

We introduce an active learning framework for general sequence learning tasks including sequence labeling and generation. Most existing active learning algorithms mainly rely on an uncertainty measure derived from the probabilistic classifier for query sample selection. However, such approaches suffer from two shortcomings in the context of sequence learning including 1) cold start problem and 2) label sampling dilemma. To overcome these shortcomings, we propose a deep-learning-based active learning framework to directly identify query samples from the perspective of adversarial learning. Our approach intends to offer labeling priorities for sequences whose information content are least covered by existing labeled data. We verify our sequence-based active learning approach on two tasks including sequence labeling and sequence generation.

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IJCAI Conference 2018 Conference Paper

Learning Out-of-Vocabulary Words in Intelligent Personal Agents

  • Avik Ray
  • Yilin Shen
  • Hongxia Jin

Semantic parsers play a vital role in intelligent agents to convert natural language instructions to an actionable logical form representation. However, after deployment, these parsers suffer from poor accuracy on encountering out-of-vocabulary (OOV) words, or significant accuracy drop on previously supported instructions after retraining. Achieving both goals simultaneously is non-trivial. In this paper, we propose novel neural networks based parsers to learn OOV words; one incorporating a new hybrid paraphrase generation model, and an enhanced sequence-to-sequence model. Extensive experiments on both benchmark and custom datasets show our new parsers achieve significant accuracy gain on OOV words and phrases, and in the meanwhile learn OOV words while maintaining accuracy on previously supported instructions.

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IJCAI Conference 2018 Conference Paper

Network Approximation using Tensor Sketching

  • Shiva Prasad Kasiviswanathan
  • Nina Narodytska
  • Hongxia Jin

Deep neural networks are powerful learning models that achieve state-of-the-art performance on many computer vision, speech, and language processing tasks. In this paper, we study a fundamental question that arises when designing deep network architectures: Given a target network architecture can we design a `smaller' network architecture that 'approximates' the operation of the target network? The question is, in part, motivated by the challenge of parameter reduction (compression) in modern deep neural networks, as the ever increasing storage and memory requirements of these networks pose a problem in resource constrained environments. In this work, we focus on deep convolutional neural network architectures, and propose a novel randomized tensor sketching technique that we utilize to develop a unified framework for approximating the operation of both the convolutional and fully connected layers. By applying the sketching technique along different tensor dimensions, we design changes to the convolutional and fully connected layers that substantially reduce the number of effective parameters in a network. We show that the resulting smaller network can be trained directly, and has a classification accuracy that is comparable to the original network.

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IJCAI Conference 2017 Conference Paper

Disguise Adversarial Networks for Click-through Rate Prediction

  • Yue Deng
  • Yilin Shen
  • Hongxia Jin

We introduced an adversarial learning framework for improving CTR prediction in Ads recommendation. Our approach was motivated by observing the extremely low click-through rate and imbalanced label distribution in the historical Ads impressions. We hence proposed a Disguise-Adversarial-Networks (DAN) to improve the accuracy of supervised learning with limited positive-class information. In the context of CTR prediction, the rationality behind DAN could be intuitively understood as ``non-clicked Ads makeup''. DAN disguises the disliked Ads impressions (non-clicks) to be interesting ones and encourages a discriminator to classify these disguised Ads as positive recommendations. In an adversarial aspect, the discriminator should be sober-minded which is optimized to allocate these disguised Ads to their inherent classes according to an unsupervised information theoretic assignment strategy. We applied DAN to two Ads datasets including both mobile and display Ads for CTR prediction. The results showed that our DAN approach significantly outperformed other supervised learning and generative adversarial networks (GAN) in CTR prediction.

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ICML Conference 2016 Conference Paper

Efficient Private Empirical Risk Minimization for High-dimensional Learning

  • Shiva Prasad Kasiviswanathan
  • Hongxia Jin

Dimensionality reduction is a popular approach for dealing with high dimensional data that leads to substantial computational savings. Random projections are a simple and effective method for universal dimensionality reduction with rigorous theoretical guarantees. In this paper, we theoretically study the problem of differentially private empirical risk minimization in the projected subspace (compressed domain). We ask: is it possible to design differentially private algorithms with small excess risk given access to only projected data? In this paper, we answer this question in affirmative, by showing that for the class of generalized linear functions, given only the projected data and the projection matrix, we can obtain excess risk bounds of $O(w(Theta)^2/3/n^1/3) under eps-differential privacy, and O((w(Theta)/n)^1/2)$ under (eps, delta)-differential privacy, where n is the sample size and w(Theta) is the Gaussian width of the parameter space that we optimize over. A simple consequence of these results is that, for a large class of ERM problems, in the traditional setting (i. e. , with access to the original data), under eps-differential privacy, we improve the worst-case risk bounds of Bassily et al. (FOCS 2014).

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AAAI Conference 2016 Conference Paper

Privacy-CNH: A Framework to Detect Photo Privacy with Convolutional Neural Network using Hierarchical Features

  • Lam Tran
  • Deguang Kong
  • Hongxia Jin
  • Ji Liu

Photo privacy is a very important problem in the digital age where photos are commonly shared on social networking sites and mobile devices. The main challenge in photo privacy detection is how to generate discriminant features to accurately detect privacy at risk photos. Existing photo privacy detection works, which rely on low-level vision features, are non-informative to the users regarding what privacy information is leaked from their photos. In this paper, we propose a new framework called Privacy- CNH that utilizes hierarchical features which include both object and convolutional features in a deep learning model to detect privacy at risk photos. The generation of object features enables our model to better inform the users about the reason why a photo has privacy risk. The combination of convolutional and object features provide a richer model to understand photo privacy from different aspects, thus improving photo privacy detection accuracy. Experimental results demonstrate that the proposed model outperforms the state-of-the-art work and the standard convolutional neural network (CNN) with convolutional features on photo privacy detection tasks.

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AAAI Conference 2012 Conference Paper

Content Recommendation for Attention Management in Unified Social Messaging

  • Hongxia Jin

With the growing popularity of social networks and collaboration systems, people are increasingly working with or socially connected with each other. Unified messaging system provides a single interface for users to receive and process information from multiple sources. It is highly desirable to design attention management solution that can help users easily navigate and process dozens of unread messages from a unified message system. Moreover, with the proliferation of mobile devices people are now selectively consuming the most important messages on the go between different activities in their daily life. The information overload problem is especially acute for mobile users with small screen to display. In this paper, we present PAM, an intelligent end-to-end Personalized Attention Management solution that employs analytical techniques that can learn user interests and organize and prioritize incoming messages based on user interests. For a list of unread messages, PAM generates a concise attention report that allows users to quickly scan the important new messages from his important social connections as well as messages about his most important tasks that the user is involved with. Our solution can also be applied in other applications such as news filtering and alerts on mobile devices. Our evaluation results demonstrate the effectiveness of PAM.

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