Sheng Xu

EAAI Journal 2026 Journal Article

A novel variational feature decomposition framework for joint shadow detection and removal in complex visual scenes

• Chenxi Wang
• Yue Chi
• Sheng Xu

TMLR Journal 2025 Journal Article

A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges

• Guiliang Liu
• Sheng Xu
• Shicheng Liu
• Ashish Gaurav
• Sriram Ganapathi Subramanian
• Pascal Poupart

Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints that expert agents adhere to, based on their demonstration data. As an emerging research topic, ICRL has received considerable attention in recent years. This article presents a categorical survey of the latest advances in ICRL. It serves as a comprehensive reference for machine learning researchers and practitioners, as well as starters seeking to comprehend the definitions, advancements, and important challenges in ICRL. We begin by formally defining the problem and outlining the algorithmic framework that facilitates constraint inference across various scenarios. These include deterministic or stochastic environments, environments with limited demonstrations, and multiple agents. For each context, we illustrate the critical challenges and introduce a series of fundamental methods to tackle these issues. This survey encompasses discrete, virtual, and realistic environments for evaluating ICRL agents. We also delve into the most pertinent applications of ICRL, such as autonomous driving, robot control, and sports analytics. To stimulate continuing research, we conclude the survey with a discussion of key unresolved questions in ICRL that can effectively foster a bridge between theoretical understanding and practical industrial applications. The papers referenced in this survey can be found at https://github.com/Jasonxu1225/Awesome-Constraint-Inference-in-RL.

ICLR Conference 2025 Conference Paper

A Distributional Approach to Uncertainty-Aware Preference Alignment Using Offline Demonstrations

• Sheng Xu
• Bo Yue
• Hongyuan Zha
• Guiliang Liu

Designing reward functions in Reinforcement Learning (RL) often demands significant task-specific expertise. Offline Preference-based Reinforcement Learning (PbRL) provides an effective alternative to address the complexity of reward design by learning policies from offline datasets that contain human preferences between trajectory pairs. Existing offline PbRL studies typically model a reward function by maximizing its likelihood of generating the observed human preferences. However, due to the varying number of samples within the limited dataset, less frequently compared trajectories exhibit greater uncertainty, which potentially leads to unreliable behaviors during reward and policy updates. To solve this issue, in this work, we introduce Uncertainty-Aware PbRL (UA-PbRL) to learn a distributional reward model and a risk-sensitive policy from an offline preference dataset. Our approach employs a Maximum A Posteriori (MAP) objective to update trajectory rewards and incorporates an informative prior to account for the uncertainties. Building upon this reward update, we propose a generative reward model to capture the reward distribution, utilizing the offline distributional Bellman operator and the Conditional Value-at-Risk (CVaR) metric to train a risk-sensitive policy. Experimental results demonstrate that UA-PbRL effectively identifies and avoids states with high uncertainty, facilitating risk-averse behaviors across various tasks, including robot control and language model alignment. The code is available at https://github.com/Jasonxu1225/UA-PbRL.

NeurIPS Conference 2025 Conference Paper

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation

• Xiang Zhang
• Jiaqi Wei
• Zijie Qiu
• Sheng Xu
• Zhi Jin
• Zhiqiang Gao
• Nanqing Dong
• Siqi Sun

Autoregressive (AR) models, common in sequence generation, are limited in many biological tasks like de novo peptide sequencing and protein modeling by their unidirectional nature, failing to capture crucial global bidirectional token dependencies. Non-Autoregressive (NAR) models offer holistic, bidirectional representations but face challenges with generative coherence and scalability. To transcend this, we propose a hybrid framework enhancing AR generation by dynamically integrating rich contextual information from non-autoregressive mechanisms. Our approach couples a shared input encoder with two decoders: a non-autoregressive one learning latent bidirectional biological features, and an AR decoder synthesizing the biological sequence by leveraging these bidirectional features. A novel cross-decoder attention module enables the AR decoder to iteratively query and integrate these bidirectional features, enriching its predictions. This synergy is cultivated via a tailored training strategy with importance annealing for balanced objectives and cross-decoder gradient blocking for stable, focused learning. Evaluations on a demanding 9-species benchmark of de novo peptide sequencing task show our model substantially surpasses AR and NAR baselines. It uniquely harmonizes AR stability with NAR contextual awareness, delivering robust, superior performance on diverse downstream data. This research advances biological sequence modeling techniques and contributes a novel architectural paradigm for augmenting AR models with enhanced bidirectional understanding for complex sequence generation. Our code is available on GitHub: https: //github. com/BEAM-Labs/denovo

ICML Conference 2025 Conference Paper

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing

• Xiang Zhang 0011
• Jiaqi Wei
• Zijie Qiu
• Sheng Xu
• Nanqing Dong
• Zhiqiang Gao
• Siqi Sun

Peptide sequencing—the process of identifying amino acid sequences from mass spectrometry data—is a fundamental task in proteomics. Non-Autoregressive Transformers (NATs) have proven highly effective for this task, outperforming traditional methods. Unlike autoregressive models, which generate tokens sequentially, NATs predict all positions simultaneously, leveraging bidirectional context through unmasked self-attention. However, existing NAT approaches often rely on Connectionist Temporal Classification (CTC) loss, which presents significant optimization challenges due to CTC’s complexity and increases the risk of training failures. To address these issues, we propose an improved non-autoregressive peptide sequencing model that incorporates a structured protein sequence curriculum learning strategy. This approach adjusts protein’s learning difficulty based on the model’s estimated protein generational capabilities through a sampling process, progressively learning peptide generation from simple to complex sequences. Additionally, we introduce a self-refining inference-time module that iteratively enhances predictions using learned NAT token embeddings, improving sequence accuracy at a fine-grained level. Our curriculum learning strategy reduces NAT training failures frequency by more than 90% based on sampled training over various data distributions. Evaluations on nine benchmark species demonstrate that our approach outperforms all previous methods across multiple metrics and species. Model and source code are available at https: //github. com/BEAM-Labs/denovo.

NeurIPS Conference 2025 Conference Paper

OmniTalker: One-shot Real-time Text-Driven Talking Audio-Video Generation With Multimodal Style Mimicking

• Zhongjian Wang
• Peng Zhang
• Jinwei Qi
• Wang Yuan
• Sheng Xu
• Bang Zhang

Although significant progress has been made in audio-driven talking head generation, text-driven methods remain underexplored. In this work, we present OmniTalker, a unified framework that jointly generates synchronized talking audio-video content from input text while emulating the target identity's speaking and facial movement styles, including speech characteristics, head motion, and facial dynamics. Our framework adopts a dual-branch diffusion transformer (DiT) architecture, with one branch dedicated to audio generation and the other to video synthesis. At the shallow layers, cross-modal fusion modules are introduced to integrate information between the two modalities. In deeper layers, each modality is processed independently, with the generated audio decoded by a vocoder and the video rendered using a GAN-based high-quality visual renderer. Leveraging DiT’s in-context learning capability through a masked-infilling strategy, our model can simultaneously capture both audio and visual styles without requiring explicit style extraction modules. Thanks to the efficiency of the DiT backbone and the optimized visual renderer, OmniTalker achieves real-time inference at 25 FPS. To the best of our knowledge, OmniTalker is the first one-shot framework capable of jointly modeling speech and facial styles in real time. Extensive experiments demonstrate its superiority over existing methods in terms of generation quality, particularly in preserving style consistency and ensuring precise audio-video synchronization, all while maintaining efficient inference.

ICLR Conference 2025 Conference Paper

Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers

• Runyi Zhao
• Sheng Xu
• Bo Yue
• Guiliang Liu

An important prerequisite for safe control is aligning the policy with the underlying constraints in the environment. In many real-world applications, due to the difficulty of manually specifying these constraints, existing works have proposed recovering constraints from expert demonstrations by solving the Inverse Constraint Learning (ICL) problem. However, ICL is inherently ill-posed, as multiple constraints can equivalently explain the experts' preferences, making the optimal solutions not uniquely identifiable. In this work, instead of focusing solely on a single constraint, we propose the novel approach of Exploratory ICL (ExICL). The goal of ExICL is to recover a diverse set of feasible constraints, thereby providing practitioners the flexibility to select the most appropriate constraint based on the practical needs of deployment. To achieve this goal, we design a generative diffusion verifier that guides the trajectory generation process using the probabilistic representation of an optimal constrained policy. By comparing these decisions with those made by expert agents, we can efficiently verify a candidate constraint. Driven by the verification feedback, ExICL implements an exploratory constraint update mechanism that strategically facilitates diversity within the collection of feasible constraints. Our empirical results demonstrate that ExICL can seamlessly and reliably generalize across different tasks and environments. The code is available at https://github.com/ZhaoRunyi/ExICL.

NeurIPS Conference 2025 Conference Paper

Uncertainty-aware Preference Alignment for Diffusion Policies

• Runqing Miao
• Sheng Xu
• Runyi Zhao
• Wai Kin (Victor) Chan
• Guiliang Liu

Recent advancements in diffusion policies have demonstrated promising performance in decision-making tasks. To align these policies with human preferences, a common approach is incorporating Preference-based Reinforcement Learning (PbRL) into policy tuning. However, since preference data is practically collected from populations with different backgrounds, a key challenge lies in handling the inherent uncertainties in people's preferences during policy updates. To address this challenge, we propose the Diff-UAPA algorithm, designed for uncertainty-aware preference alignment in diffusion policies. Specifically, Diff-UAPA introduces a novel iterative preference alignment framework in which the diffusion policy adapts incrementally to preferences from different user groups. To accommodate this online learning paradigm, Diff-UAPA employs a maximum posterior objective, which aligns the diffusion policy with regret-based preferences under the guidance of an informative Beta prior. This approach enables direct optimization of the diffusion policy without specifying any reward functions, while effectively mitigating the influence of inconsistent preferences across different user groups. We conduct extensive experiments across both simulated and real-world robotics tasks, and diverse human preference configurations, demonstrating the robustness and reliability of Diff-UAPA in achieving effective preference alignment. The project page is available at https: //github. com/mr20010112/Diff_UAPA.

ICML Conference 2025 Conference Paper

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing

• Zijie Qiu
• Jiaqi Wei
• Xiang Zhang 0011
• Sheng Xu
• Kai Zou
• Zhi Jin
• Zhiqiang Gao
• Nanqing Dong

De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrometry data and the heterogeneous distribution of noise signals, leading to data-specific biases. We present RankNovo, the first deep reranking framework that enhances de novo peptide sequencing by leveraging the complementary strengths of multiple sequencing models. RankNovo employs a list-wise reranking approach, modeling candidate peptides as multiple sequence alignments and utilizing axial attention to extract informative features across candidates. Additionally, we introduce two new metrics, PMD ( P eptide M ass D eviation) and RMD ( R esidual M ass D eviation), which offer delicate supervision by quantifying mass differences between peptides at both the sequence and residue levels. Extensive experiments demonstrate that RankNovo not only surpasses its base models used to generate training candidates for reranking pre-training, but also sets a new state-of-the-art benchmark. Moreover, RankNovo exhibits strong zero-shot generalization to unseen models—those whose generations were not exposed during training, highlighting its robustness and potential as a universal reranking framework for peptide sequencing. Our work presents a novel reranking strategy that fundamentally challenges existing single-model paradigms and advances the frontier of accurate de novo sequencing. Our source code is provided on GitHub.

NeurIPS Conference 2024 Conference Paper

BEACON: Benchmark for Comprehensive RNA Tasks and Language Models

• Yuchen Ren
• Zhiyuan Chen
• Lifeng Qiao
• Hongtai Jing
• Yuchen Cai
• Sheng Xu
• Peng Ye
• Xinzhu Ma

RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms. Despite the emergence of numerous deep learning approaches for RNA, particularly universal RNA language models, there remains a significant lack of standardized benchmarks to assess the effectiveness of these methods. In this study, we introduce the first comprehensive RNA benchmark BEACON BE nchm A rk for CO mprehensive R N A Task and Language Models). First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications, enabling a comprehensive assessment of the performance of methods on various RNA understanding tasks. Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models. Third, we investigate the vital RNA language model components from the tokenizer and positional encoding aspects. Notably, our findings emphasize the superiority of single nucleotide tokenization and the effectiveness of Attention with Linear Biases (ALiBi) over traditional positional encoding methods. Based on these insights, a simple yet strong baseline called BEACON-B is proposed, which can achieve outstanding performance with limited data and computational resources. The datasets and source code of our benchmark are available at https: //github. com/terry-r123/RNABenchmark.

AAAI Conference 2024 Conference Paper

Bi-ViT: Pushing the Limit of Vision Transformer Quantization

• Yanjing Li
• Sheng Xu
• Mingbao Lin
• Xianbin Cao
• Chuanjian Liu
• Xiao Sun
• Baochang Zhang

Vision transformers (ViTs) quantization offers a promising prospect to facilitate deploying large pre-trained networks on resource-limited devices. Fully-binarized ViTs (Bi-ViT) that pushes the quantization of ViTs to its limit remain largely unexplored and a very challenging task yet, due to their unacceptable performance. Through extensive empirical analyses, we identify the severe drop in ViT binarization is caused by attention distortion in self-attention, which technically stems from the gradient vanishing and ranking disorder. To address these issues, we first introduce a learnable scaling factor to reactivate the vanished gradients and illustrate its effectiveness through theoretical and experimental analyses. We then propose a ranking-aware distillation method to rectify the disordered ranking in a teacher-student framework. Bi-ViT achieves significant improvements over popular DeiT and Swin backbones in terms of Top-1 accuracy and FLOPs. For example, with DeiT-Tiny and Swin-Tiny, our method significantly outperforms baselines by 22.1% and 21.4% respectively, while 61.5x and 56.1x theoretical acceleration in terms of FLOPs compared with real-valued counterparts on ImageNet. Our codes and models are attached on https://github.com/YanjingLi0202/Bi-ViT/.

AAAI Conference 2024 Conference Paper

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing

• Zhi Jin
• Sheng Xu
• Xiang Zhang
• Tianze Ling
• Nanqing Dong
• Wanli Ouyang
• Zhiqiang Gao
• Cheng Chang

De novo peptide sequencing from mass spectrometry (MS) data is a critical task in proteomics research. Traditional de novo algorithms have encountered a bottleneck in accuracy due to the inherent complexity of proteomics data. While deep learning-based methods have shown progress, they reduce the problem to a translation task, potentially overlooking critical nuances between spectra and peptides. In our research, we present ContraNovo, a pioneering algorithm that leverages contrastive learning to extract the relationship between spectra and peptides and incorporates the mass information into peptide decoding, aiming to address these intricacies more efficiently. Through rigorous evaluations on two benchmark datasets, ContraNovo consistently outshines contemporary state-of-the-art solutions, underscoring its promising potential in enhancing de novo peptide sequencing.

AAAI Conference 2024 Conference Paper

CrossBind: Collaborative Cross-Modal Identification of Protein Nucleic-Acid-Binding Residues

• Linglin Jing
• Sheng Xu
• Yifan Wang
• Yuzhe Zhou
• Tao Shen
• Zhigang Ji
• Hui Fang
• Zhen Li

Accurate identification of protein nucleic acid binding residues poses a significant challenge with important implications for various biological processes and drug design. Many typical computational methods for protein analysis rely on a single model that could ignore either the semantic context of the protein or the global 3D geometric information. Consequently, these approaches may result in incomplete or inaccurate protein analysis. To address the above issue, in this paper, we present CrossBind, a novel collaborative cross modal approach for identifying binding residues by exploiting both protein geometric structure and its sequence prior knowledge extracted from a large scale protein language model. Specifically, our multi modal approach leverages a contrastive learning technique and atom wise attention to capture the positional relationships between atoms and residues, thereby incorporating fine grained local geometric knowledge, for better binding residue prediction. Extensive experimental results demonstrate that our approach outperforms the next best state of the art methods, GraphSite and GraphBind, on DNA and RNA datasets by 10.8/17.3% in terms of the harmonic mean of precision and recall (F1 Score) and 11.9/24.8% in Matthews correlation coefficient (MCC), respectively. We release the code at https://github.com/BEAM-Labs/CrossBind.

ICLR Conference 2024 Conference Paper

Enhancing Human-AI Collaboration Through Logic-Guided Reasoning

• Chengzhi Cao
• Yinghao Fu
• Sheng Xu
• Ruimao Zhang
• Shuang Li 0002

We present a systematic framework designed to enhance human-robot perception and collaboration through the integration of logical rules and Theory of Mind (ToM). Logical rules provide interpretable predictions and generalize well across diverse tasks, making them valuable for learning and decision-making. Leveraging the ToM for understanding others' mental states, our approach facilitates effective collaboration. In this paper, we employ logic rules derived from observational data to infer human goals and guide human-like agents. These rules are treated as latent variables, and a rule encoder is trained alongside a multi-agent system in the robot's mind. We assess the posterior distribution of latent rules using learned embeddings, representing entities and relations. Confidence scores for each rule indicate their consistency with observed data. Then, we employ a hierarchical reinforcement learning model with ToM to plan robot actions for assisting humans. Extensive experiments validate each component of our framework, and results on multiple benchmarks demonstrate that our model outperforms the majority of existing approaches.

EAAI Journal 2024 Journal Article

Exploratory parallel hybrid sampling framework for imbalanced data classification

• Ming Zheng
• Zhuo Zhao
• Fei Wang
• Xiaowen Hu
• Sheng Xu
• Wanggen Li
• Tong Li

ICML Conference 2024 Conference Paper

Robust Inverse Constrained Reinforcement Learning under Model Misspecification

• Sheng Xu
• Guiliang Liu

To solve safety-critical decision-making problems, Inverse Constrained Reinforcement Learning (ICRL) infers constraints from expert demonstrations and seeks to imitate expert preference by utilizing these constraints. While prior ICRL research commonly overlooks the discrepancy between the training and deploying environments, we demonstrate that such a discrepancy can significantly compromise the reliability of the inferred constraints and thus induce unsafe movements. Motivated by this finding, we propose the Robust Constraint Inference (RCI) problem and an Adaptively Robust ICRL (AR-ICRL) algorithm to solve RCI efficiently. Specifically, we model the impact of misspecified dynamics with an opponent policy and learn a robust policy to facilitate safe control in a Markov Game. Subsequently, we adjust our constraint model to align the learned policies to expert demonstrations, accommodating both soft and hard optimality in our behavioral models. Empirical results demonstrate the significance of robust constraints and the effectiveness of the proposed AR-ICRL algorithm under continuous and discrete domains. The code is available at https: //github. com/Jasonxu1225/AR-ICRL.

ICLR Conference 2024 Conference Paper

Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning

• Sheng Xu
• Guiliang Liu

Aiming for safe control, Inverse Constrained Reinforcement Learning (ICRL) considers inferring the constraints respected by expert agents from their demonstrations and learning imitation policies that adhere to these constraints. While previous ICRL works often neglected underlying uncertainties during training, we contend that modeling these uncertainties is crucial for facilitating robust constraint inference. This insight leads to the development of an Uncertainty-aware Inverse Constrained Reinforcement Learning (UAICRL) algorithm. Specifically, 1) aleatoric uncertainty arises from the inherent stochasticity of environment dynamics, leading to constraint-violating behaviors in imitation policies. To address this, UAICRL constructs risk-sensitive constraints by incorporating distributional Bellman updates into the cumulative costs model. 2) Epistemic uncertainty, resulting from the model's limited knowledge of Out-of-Distribution (OoD) samples, affects the accuracy of step-wise cost predictions. To tackle this issue, UAICRL develops an information-theoretic quantification of the epistemic uncertainty and mitigates its impact through flow-based generative data augmentation. Empirical results demonstrate that UAICRL consistently outperforms other baselines in continuous and discrete environments with stochastic dynamics. The code is available at https://github.com/Jasonxu1225/UAICRL.

NeurIPS Conference 2023 Conference Paper

Q-DM: An Efficient Low-bit Quantized Diffusion Model

• Yanjing Li
• Sheng Xu
• Xianbin Cao
• Xiao Sun
• Baochang Zhang

Denoising diffusion generative models are capable of generating high-quality data, but suffers from the computation-costly generation process, due to a iterative noise estimation using full-precision networks. As an intuitive solution, quantization can significantly reduce the computational and memory consumption by low-bit parameters and operations. However, low-bit noise estimation networks in diffusion models (DMs) remain unexplored yet and perform much worse than the full-precision counterparts as observed in our experimental studies. In this paper, we first identify that the bottlenecks of low-bit quantized DMs come from a large distribution oscillation on activations and accumulated quantization error caused by the multi-step denoising process. To address these issues, we first develop a Timestep-aware Quantization (TaQ) method and a Noise-estimating Mimicking (NeM) scheme for low-bit quantized DMs (Q-DM) to effectively eliminate such oscillation and accumulated error respectively, leading to well-performed low-bit DMs. In this way, we propose an efficient Q-DM to calculate low-bit DMs by considering both training and inference process in the same framework. We evaluate our methods on popular DDPM and DDIM models. Extensive experimental results show that our method achieves a much better performance than the prior arts. For example, the 4-bit Q-DM theoretically accelerates the 1000-step DDPM by 7. 8x and achieves a FID score of 5. 17, on the unconditional CIFAR-10 dataset.

AAAI Conference 2023 Conference Paper

Resilient Binary Neural Network

• Sheng Xu
• Yanjing Li
• Teli Ma
• Mingbao Lin
• Hao Dong
• Baochang Zhang
• Peng Gao
• Jinhu Lu

Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of reducing storage burden as well as quickening inference time. However, there is a severe performance drop compared with {real-valued} networks, due to its intrinsic frequent weight oscillation during training. In this paper, we introduce a Resilient Binary Neural Network (ReBNN) to mitigate the frequent oscillation for better BNNs' training. We identify that the weight oscillation mainly stems from the non-parametric scaling factor. To address this issue, we propose to parameterize the scaling factor and introduce a weighted reconstruction loss to build an adaptive training objective. For the first time, we show that the weight oscillation is controlled by the balanced parameter attached to the reconstruction loss, which provides a theoretical foundation to parameterize it in back propagation. Based on this, we learn our ReBNN by calculating the balanced parameter based on its maximum magnitude, which can effectively mitigate the weight oscillation with a resilient training process. Extensive experiments are conducted upon various network models, such as ResNet and Faster-RCNN for computer vision, as well as BERT for natural language processing. The results demonstrate the overwhelming performance of our ReBNN over prior arts. For example, our ReBNN achieves 66.9% Top-1 accuracy with ResNet-18 backbone on the ImageNet dataset, surpassing existing state-of-the-arts by a significant margin. Our code is open-sourced at https://github.com/SteveTsui/ReBNN.

AAAI Conference 2022 Short Paper

Automatic Slides Generation for Scholarly Papers: A Fine-Grained Dataset and Baselines (Student Abstract)

• Sheng Xu
• Xiaojun Wan

Slides are broadly used to present the research works and there are several studies on the problem of automatic slides generation. However, the lack of dataset hinders further research. In this paper, we construct a benchmark dataset for the problem of slides generation from scholarly papers. The dataset is fine-grained and consists of aligned pairs of single slide and specific region of a paper. Then we deploy several baseline models and conduct preliminary experiments. The results show that this task is challenging and awaits more exploration. The dataset and code will be released.

AAAI Conference 2022 System Paper

PosterBot: A System for Generating Posters of Scientific Papers with Neural Models

• Sheng Xu
• Xiaojun Wan

Posters are broadly used to present the important points of academic papers and can be seen as a special form of document summarization. However, the problem of automatic poster generation is under-investigated. In this paper, we present PosterBot, an automatic poster generation system for academic papers. Given a scholarly paper, PosterBot takes three steps to generate the poster. It first selects the most important sections, and then generates corresponding panels from them. Finally, all panels are integrated to get the complete poster. The demonstration shows the efficacy of our proposed system.

NeurIPS Conference 2022 Conference Paper

Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer

• Yanjing Li
• Sheng Xu
• Baochang Zhang
• Xianbin Cao
• Peng Gao
• Guodong Guo

The large pre-trained vision transformers (ViTs) have demonstrated remarkable performance on various visual tasks, but suffer from expensive computational and memory cost problems when deployed on resource-constrained devices. Among the powerful compression approaches, quantization extremely reduces the computation and memory consumption by low-bit parameters and bit-wise operations. However, low-bit ViTs remain largely unexplored and usually suffer from a significant performance drop compared with the real-valued counterparts. In this work, through extensive empirical analysis, we first identify the bottleneck for severe performance drop comes from the information distortion of the low-bit quantized self-attention map. We then develop an information rectification module (IRM) and a distribution guided distillation (DGD) scheme for fully quantized vision transformers (Q-ViT) to effectively eliminate such distortion, leading to a fully quantized ViTs. We evaluate our methods on popular DeiT and Swin backbones. Extensive experimental results show that our method achieves a much better performance than the prior arts. For example, our Q-ViT can theoretically accelerates the ViT-S by 6. 14x and achieves about 80. 9% Top-1 accuracy, even surpassing the full-precision counterpart by 1. 0% on ImageNet dataset. Our codes and models are attached on https: //github. com/YanjingLi0202/Q-ViT

ICLR Conference 2021 Conference Paper

Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS

• Lin Chen
• Sheng Xu

We prove that the reproducing kernel Hilbert spaces (RKHS) of a deep neural tangent kernel and the Laplace kernel include the same set of functions, when both kernels are restricted to the sphere $\mathbb{S}^{d-1}$. Additionally, we prove that the exponential power kernel with a smaller power (making the kernel less smooth) leads to a larger RKHS, when it is restricted to the sphere $\mathbb{S}^{d-1}$ and when it is defined on the entire $\mathbb{R}^d$.

ICRA Conference 2021 Conference Paper

RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images

• Minghao Gou
• Haoshu Fang
• Zhanda Zhu
• Sheng Xu
• Chenxi Wang 0003
• Cewu Lu

General object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most of the success grasps, or only take the unstable depth image or point cloud as input which may lead to poor results in some cases. In this paper, we propose RGBD-Grasp, a pipeline that solves this problem by decoupling 7-DoF grasp detection into two sub-tasks where RGB and depth information are processed separately. In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image. Consequently, a Fast Analytic Searching(FAS) module calculates the opening width and the distance of the gripper to the grasp point. By decoupling the grasp detection problem and introducing the stable RGB modality, our pipeline alleviates the requirement for the high-quality depth image and is robust to depth sensor noise. We achieve state-of-the-art results on GraspNet-1Billion dataset compared with several baselines. Real robot experiments on a UR5 robot with an Intel Realsense camera and a Robotiq two-finger gripper show high success rates for both single object scenes and cluttered scenes. Our code and trained model are available at graspnet.net.

ICML Conference 2015 Conference Paper

Robust Estimation of Transition Matrices in High Dimensional Heavy-tailed Vector Autoregressive Processes

• Huitong Qiu
• Sheng Xu
• Fang Han
• Han Liu 0001
• Brian Caffo

Gaussian vector autoregressive (VAR) processes have been extensively studied in the literature. However, Gaussian assumptions are stringent for heavy-tailed time series that frequently arises in finance and economics. In this paper, we develop a unified framework for modeling and estimating heavy-tailed VAR processes. In particular, we generalize the Gaussian VAR model by an elliptical VAR model that naturally accommodates heavy-tailed time series. Under this model, we develop a quantile-based robust estimator for the transition matrix of the VAR process. We show that the proposed estimator achieves parametric rates of convergence in high dimensions. This is the first work in analyzing heavy-tailed high dimensional VAR processes. As an application of the proposed framework, we investigate Granger causality in the elliptical VAR process, and show that the robust transition matrix estimator induces sign-consistent estimators of Granger causality. The empirical performance of the proposed methodology is demonstrated by both synthetic and real data. We show that the proposed estimator is robust to heavy tails, and exhibit superior performance in stock price prediction.