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Ming Lin

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22 papers
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EAAI Journal 2025 Journal Article

Adaptive neural network tracking control for unknown high-order nonlinear systems: A constructive approximation set based approach

  • Yu-Fa Liu
  • Yong-Hua Liu
  • Jin-Wa Wu
  • Jie Tao
  • Ming Lin
  • Chun-Yi Su
  • Renquan Lu

This article addresses the problem of adaptive neural network (NN) tracking control for unknown high-order nonlinear systems, with a focus on accurately constructing NN approximation sets. To guarantee the local approximation capabilities of NNs, it is crucial that their input signals remain within corresponding compact sets. However, the unknown functions and powers in high-order nonlinear systems make it difficult to determine these sets accurately. To solve this, we introduce a novel adaptive NN tracking control strategy that integrates signal substitution technique, barrier functions (BFs), and NNs. Specifically, the signal substitution technique converts the original system states into state error variables, along with the desired reference signal and its time derivatives, which serve as part of the NN input. BFs are employed to constrain the state errors, while NNs approximate the transformed unknown system functions. This approach enables precise calculation of bounds for the NN weight estimators, ensuring that the NN approximation sets are constructed. Unlike existing methods, our approach not only proves the existence of NN approximation sets but also provides a constructive design strategy, significantly enhancing the approximation accuracy for unknown nonlinear functions. Simulation results demonstrate the effectiveness and advantages of the proposed method.

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NeurIPS Conference 2025 Conference Paper

DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization

  • Gang Li
  • Ming Lin
  • Tomer Galanti
  • Zhengzhong Tu
  • Tianbao Yang

The recent success and openness of DeepSeek-R1 have brought widespread attention to Group Relative Policy Optimization (GRPO) as a reinforcement learning method for large reasoning models (LRMs). In this work, we analyze the GRPO objective under a binary reward setting and reveal an inherent limitation of question-level difficulty bias arising from its group relative advantage function. We also identify a connection between GRPO and traditional discriminative methods in supervised learning. Motivated by these insights, we introduce a new Discriminative Constrained Optimization (DisCO) framework for reinforcing LRMs, grounded in the principle of discriminative learning: increasing the scores of positive answers while decreasing those of negative ones. The main differences between DisCO and GRPO and its recent variants are: (1) it replaces the group relative objective with a discriminative objective defined by a scoring function; (2) it abandons clipping-based surrogates in favor of non-clipping RL surrogate objectives used as scoring functions; (3) it employs a simple yet effective constrained optimization approach to enforce the KL divergence constraint. As a result, DisCO offers notable advantages over GRPO and its variants: (i) it completely eliminates difficulty bias by adopting discriminative objectives; (ii) it addresses the entropy instability in GRPO and its variants through the use of non-clipping scoring functions and a constrained optimization approach, yielding long and stable training dynamics; (iii) it allows the incorporation of advanced discriminative learning techniques to address data imbalance, where a significant number of questions have more negative than positive generated answers during training. Our experiments on enhancing the mathematical reasoning capabilities of SFT-finetuned models show that DisCO significantly outperforms GRPO and its improved variants such as DAPO, achieving average gains of 7\% over GRPO and 6\% over DAPO across six benchmark tasks for a 1. 5B model.

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ICML Conference 2025 Conference Paper

Ensemble Learned Bloom Filters: Two Oracles are Better than One

  • Ming Lin
  • Lin Chen 0002

Bloom filters (BF) are space-efficient probabilistic data structures for approximate membership testing. Boosted by the proliferation of machine learning, learned Bloom filters (LBF) were recently proposed by augmenting the canonical BFs with a learned oracle as a pre-filter, the size of which is crucial to the compactness of the overall system. In this paper, inspired by ensemble learning, we depart from the state-of-the-art single-oracle LBF structure by demonstrating that, by leveraging multiple learning oracles of smaller size and carefully optimizing the accompanied backup filters, we can significantly boost the performance of LBF under the same space budget. We then design and optimize ensemble learned Bloom filters for mutually independent and correlated learning oracles respectively. We also empirically demonstrate the performance improvement of our propositions under three practical data analysis tasks.

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AAAI Conference 2025 Conference Paper

MeRino: Entropy-Driven Design for Generative Language Models on IoT Devices

  • Youpeng Zhao
  • Ming Lin
  • Huadong Tang
  • Qiang Wu
  • Jun Wang

Generative Large Language Models (LLMs) stand as a revolutionary advancement in the modern era of artificial intelligence (AI). However, scaling down LLMs for resource-constrained hardware, such as Internet-of-Things (IoT) devices requires non-trivial efforts and domain knowledge. In this paper, we propose a novel information-entropy framework for designing mobile-friendly generative language models. The whole design procedure involves solving a mathematical programming (MP) problem, which can be done on the CPU within minutes, making it nearly zero-cost. We evaluate our designed models, termed MeRino, across fourteen NLP downstream tasks, showing their competitive performance against the state-of-the-art autoregressive transformer models under the mobile setting. Notably, MeRino achieves similar or better performance on both language modeling and zero-shot learning tasks, compared to the 350M parameter OPT while being 4.9x faster on NVIDIA Jetson Nano with 5.5x reduction in model size.

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AAAI Conference 2024 Conference Paper

Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge

  • Xuan Shen
  • Peiyan Dong
  • Lei Lu
  • Zhenglun Kong
  • Zhengang Li
  • Ming Lin
  • Chao Wu
  • Yanzhi Wang

Large Language Models (LLMs) stand out for their impressive performance in intricate language modeling tasks. However, their demanding computational and memory needs pose obstacles for broad use on edge devices. Quantization is then introduced to boost LLMs' on-device efficiency. Recent works show that 8-bit or lower weight quantization is feasible with minimal impact on end-to-end task performance, while the activation is still not quantized. On the other hand, mainstream commodity edge devices still struggle to execute these sub-8-bit quantized networks effectively. In this paper, we propose Agile-Quant, an Activation-Guided quantization framework for faster Inference of popular Large Language Models (LLMs) on the Edge. Considering the hardware profiling and activation analysis, we first introduce a basic activation quantization strategy to balance the trade-off of task performance and real inference speed. Then we leverage the activation-aware token pruning technique to reduce the outliers and the adverse impact on attentivity. Ultimately, we utilize the SIMD-based 4-bit multiplier and our efficient TRIP matrix multiplication to implement the accelerator for LLMs on the edge. We apply our framework on different scales of LLMs including LLaMA, OPT, and BLOOM with 4-bit or 8-bit for the activation and 4-bit for the weight quantization. Experiments show that Agile-Quant achieves simultaneous quantization of model weights and activations while maintaining task performance comparable to existing weight-only quantization methods. Moreover, in the 8- and 4-bit scenario, Agile-Quant achieves an on-device speedup of up to 2.55x compared to its FP16 counterparts across multiple edge devices, marking a pioneering advancement in this domain.

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AAAI Conference 2024 Conference Paper

ICAR: Image-Based Complementary Auto Reasoning

  • Xijun Wang
  • Anqi Liang
  • Junbang Liang
  • Ming Lin
  • Yu Lou
  • Shan Yang

Scene-aware Complementary Item Retrieval (CIR) is a challenging task which requires to generate a set of compatible items across domains. Due to the subjectivity, it is difficult to set up a rigorous standard for both data collection and learning objectives. To address this challenging task, we propose a visual compatibility concept, composed of similarity (resembling in color, geometry, texture, and etc.) and complementarity (different items like table vs chair completing a group). Based on this notion, we propose a compatibility learning framework, a category-aware Flexible Bidirectional Transformer (FBT), for visual ``scene-based set compatibility reasoning'' with the cross-domain visual similarity input and auto-regressive complementary item generation. We introduce a ``Flexible Bidirectional Transformer (FBT),'' consisting of an encoder with flexible masking, a category prediction arm, and an auto-regressive visual embedding prediction arm. And the inputs for FBT are cross-domain visual similarity invariant embeddings, making this framework quite generalizable. Furthermore, our proposed FBT model learns the inter-object compatibility from a large set of scene images in a self-supervised way. Compared with the SOTA methods, this approach achieves up to 5.3% and 9.6% in FITB score and 22.3% and 31.8% SFID improvement on fashion and furniture, respectively.

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NeurIPS Conference 2024 Conference Paper

Search for Efficient Large Language Models

  • Xuan Shen
  • Pu Zhao
  • Yifan Gong
  • Zhenglun Kong
  • Zheng Zhan
  • Yushu Wu
  • Ming Lin
  • Chao Wu

Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Numerous efficient techniques, including weight pruning, quantization, and distillation, have been embraced to compress LLMs, targeting memory reduction and inference acceleration, which underscore the redundancy in LLMs. However, most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures. Besides, traditional architecture search methods, limited by the elevated complexity with extensive parameters, struggle to demonstrate their effectiveness on LLMs. In this paper, we propose a training-free architecture search framework to identify optimal subnets that preserve the fundamental strengths of the original LLMs while achieving inference acceleration. Furthermore, after generating subnets that inherit specific weights from the original LLMs, we introduce a reformation algorithm that utilizes the omitted weights to rectify the inherited weights with a small amount of calibration data. Compared with SOTA training-free structured pruning works that can generate smaller networks, our method demonstrates superior performance across standard benchmarks. Furthermore, our generated subnets can directly reduce the usage of GPU memory and achieve inference acceleration.

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NeurIPS Conference 2023 Conference Paper

Gradient Informed Proximal Policy Optimization

  • Sanghyun Son
  • Laura Zheng
  • Ryan Sullivan
  • Yi-Ling Qiao
  • Ming Lin

We introduce a novel policy learning method that integrates analytical gradients from differentiable environments with the Proximal Policy Optimization (PPO) algorithm. To incorporate analytical gradients into the PPO framework, we introduce the concept of an α-policy that stands as a locally superior policy. By adaptively modifying the α value, we can effectively manage the influence of analytical policy gradients during learning. To this end, we suggest metrics for assessing the variance and bias of analytical gradients, reducing dependence on these gradients when high variance or bias is detected. Our proposed approach outperforms baseline algorithms in various scenarios, such as function optimization, physics simulations, and traffic control environments. Our code can be found online: https: //github. com/SonSang/gippo.

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NeurIPS Conference 2022 Conference Paper

Differentiable Analog Quantum Computing for Optimization and Control

  • Jiaqi Leng
  • Yuxiang Peng
  • Yi-Ling Qiao
  • Ming Lin
  • Xiaodi Wu

We formulate the first differentiable analog quantum computing framework with specific parameterization design at the analog signal (pulse) level to better exploit near-term quantum devices via variational methods. We further propose a scalable approach to estimate the gradients of quantum dynamics using a forward pass with Monte Carlo sampling, which leads to a quantum stochastic gradient descent algorithm for scalable gradient-based training in our framework. Applying our framework to quantum optimization and control, we observe a significant advantage of differentiable analog quantum computing against SOTAs based on parameterized digital quantum circuits by {\em orders of magnitude}.

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NeurIPS Conference 2022 Conference Paper

Entropy-Driven Mixed-Precision Quantization for Deep Network Design

  • Zhenhong Sun
  • Ce Ge
  • Junyan Wang
  • Ming Lin
  • Hesen Chen
  • Hao Li
  • Xiuyu Sun

Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage. Previous works re-design a small network for IoT devices, and then compress the network size by mixed-precision quantization. This two-stage procedure cannot optimize the architecture and the corresponding quantization jointly, leading to sub-optimal tiny deep models. In this work, we propose a one-stage solution that optimizes both jointly and automatically. The key idea of our approach is to cast the joint architecture design and quantization as an Entropy Maximization process. Particularly, our algorithm automatically designs a tiny deep model such that: 1) Its representation capacity measured by entropy is maximized under the given computational budget; 2) Each layer is assigned with a proper quantization precision; 3) The overall design loop can be done on CPU, and no GPU is required. More impressively, our method can directly search high-expressiveness architecture for IoT devices within less than half a CPU hour. Extensive experiments on three widely adopted benchmarks, ImageNet, VWW and WIDER FACE, demonstrate that our method can achieve the state-of-the-art performance in the tiny deep model regime. Code and pre-trained models are available at https: //github. com/alibaba/lightweight-neural-architecture-search.

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NeurIPS Conference 2022 Conference Paper

NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos

  • Yi-Ling Qiao
  • Alexander Gao
  • Ming Lin

We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input. To decouple the learning of underlying scene geometry from dynamic motion, we represent the scene as a time-invariant signed distance function (SDF) which serves as a reference frame, along with a time-conditioned deformation field. We further bridge this neural geometry representation with a differentiable physics simulator by designing a two-way conversion between the neural field and its corresponding hexahedral mesh, enabling us to estimate physics parameters from the source video by minimizing a cycle consistency loss. Our method also allows a user to interactively edit 3D objects from the source video by modifying the recovered hexahedral mesh, and propagating the operation back to the neural field representation. Experiments show that our method achieves superior mesh and video reconstruction of dynamic scenes compared to competing Neural Field approaches, and we provide extensive examples which demonstrate its ability to extract useful 3D representations from videos captured with consumer-grade cameras.

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NeurIPS Conference 2022 Conference Paper

Robust Graph Structure Learning via Multiple Statistical Tests

  • Yaohua Wang
  • Fangyi Zhang
  • Ming Lin
  • Senzhang Wang
  • Xiuyu Sun
  • Rong Jin

Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is available for images for most cases. A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges. It is well known that pairwise similarities between images are sensitive to the noise in feature representations, leading to unreliable graph structures. We address this problem from the viewpoint of statistical tests. By viewing the feature vector of each node as an independent sample, the decision of whether creating an edge between two nodes based on their similarity in feature representation can be thought as a ${\it single}$ statistical test. To improve the robustness in the decision of creating an edge, multiple samples are drawn and integrated by ${\it multiple}$ statistical tests to generate a more reliable similarity measure, consequentially more reliable graph structure. The corresponding elegant matrix form named $\mathcal{B}$$\textbf{-Attention}$ is designed for efficiency. The effectiveness of multiple tests for graph structure learning is verified both theoretically and empirically on multiple clustering and ReID benchmark datasets. Source codes are available at https: //github. com/Thomas-wyh/B-Attention.

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NeurIPS Conference 2021 Conference Paper

Differentiable Simulation of Soft Multi-body Systems

  • Yiling Qiao
  • Junbang Liang
  • Vladlen Koltun
  • Ming Lin

We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective Dynamics and derive a generalized dry friction model for soft continuum using a new matrix splitting strategy. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes. The experiments demonstrate that our designs make soft body simulation more stable and realistic compared to other frameworks. Our method accelerates the solution of system identification problems by more than an order of magnitude, and enables efficient gradient-based learning of motion control with soft robots.

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NeurIPS Conference 2021 Conference Paper

Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering

  • Yu Shen
  • Laura Zheng
  • Manli Shu
  • Weizi Li
  • Tom Goldstein
  • Ming Lin

We introduce a simple yet effective framework for improving the robustness of learning algorithms against image corruptions for autonomous driving. These corruptions can occur due to both internal (e. g. , sensor noises and hardware abnormalities) and external factors (e. g. , lighting, weather, visibility, and other environmental effects). Using sensitivity analysis with FID-based parameterization, we propose a novel algorithm exploiting basis perturbations to improve the overall performance of autonomous steering and other image processing tasks, such as classification and detection, for self-driving cars. Our model not only improves the performance on the original dataset, but also achieves significant performance improvement on datasets with multiple and unseen perturbations, up to 87% and 77%, respectively. A comparison between our approach and other SOTA techniques confirms the effectiveness of our technique in improving the robustness of neural network training for learning-based steering and other image processing tasks.

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NeurIPS Conference 2019 Conference Paper

Differentiable Cloth Simulation for Inverse Problems

  • Junbang Liang
  • Ming Lin
  • Vladlen Koltun

We propose a differentiable cloth simulator that can be embedded as a layer in deep neural networks. This approach provides an effective, robust framework for modeling cloth dynamics, self-collisions, and contacts. Due to the high dimensionality of the dynamical system in modeling cloth, traditional gradient computation for collision response can become impractical. To address this problem, we propose to compute the gradient directly using QR decomposition of a much smaller matrix. Experimental results indicate that our method can speed up backpropagation by two orders of magnitude. We demonstrate the presented approach on a number of inverse problems, including parameter estimation and motion control for cloth.

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AAAI Conference 2019 Conference Paper

Which Factorization Machine Modeling Is Better: A Theoretical Answer with Optimal Guarantee

  • Ming Lin
  • Shuang Qiu
  • Jieping Ye
  • Xiaomin Song
  • Qi Qian
  • Liang Sun
  • Shenghuo Zhu
  • Rong Jin

Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is not yet developed. For a rank k generalized FM of d dimensional input, the previous best known sampling complexity is O[k3 d · polylog(kd)] under Gaussian distribution. This bound is sub-optimal comparing to the information theoretical lower bound O(kd). In this work, we aim to tighten this bound towards optimal and generalize the analysis to sub-gaussian distribution. We prove that when the input data satisfies the so-called τ-Moment Invertible Property, the sampling complexity of generalized FM can be improved to O[k2 d · polylog(kd)/τ2 ]. When the second order self-interaction terms are excluded in the generalized FM, the bound can be improved to the optimal O[kd · polylog(kd)] up to the logarithmic factors. Our analysis also suggests that the positive semi-definite constraint in the conventional FM is redundant as it does not improve the sampling complexity while making the model difficult to optimize. We evaluate our improved FM model in real-time high precision GPS signal calibration task to validate its superiority.

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AAAI Conference 2018 Conference Paper

Margin Based PU Learning

  • Tieliang Gong
  • Guangtao Wang
  • Jieping Ye
  • Zongben Xu
  • Ming Lin

The PU learning problem concerns about learning from positive and unlabeled data. A popular heuristic is to iteratively enlarge training set based on some marginbased criterion. However, little theoretical analysis has been conducted to support the success of these heuristic methods. In this work, we show that not all marginbased heuristic rules are able to improve the learned classifiers iteratively. We find that a so-called large positive margin oracle is necessary to guarantee the success of PU learning. Under this oracle, a provable positivemargin based PU learning algorithm is proposed for linear regression and classification under the truncated Gaussian distributions. The proposed algorithm is able to reduce the recovering error geometrically proportional to the positive margin. Extensive experiments on real-world datasets verify our theory and the state-ofthe-art performance of the proposed PU learning algorithm.

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NeurIPS Conference 2016 Conference Paper

A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing

  • Ming Lin
  • Jieping Ye

We develop an efficient alternating framework for learning a generalized version of Factorization Machine (gFM) on steaming data with provable guarantees. When the instances are sampled from $d$ dimensional random Gaussian vectors and the target second order coefficient matrix in gFM is of rank $k$, our algorithm converges linearly, achieves $O(\epsilon)$ recovery error after retrieving $O(k^{3}d\log(1/\epsilon))$ training instances, consumes $O(kd)$ memory in one-pass of dataset and only requires matrix-vector product operations in each iteration. The key ingredient of our framework is a construction of an estimation sequence endowed with a so-called Conditionally Independent RIP condition (CI-RIP). As special cases of gFM, our framework can be applied to symmetric or asymmetric rank-one matrix sensing problems, such as inductive matrix completion and phase retrieval.

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AAAI Conference 2016 Conference Paper

Concepts Not Alone: Exploring Pairwise Relationships for Zero-Shot Video Activity Recognition

  • Chuang Gan
  • Ming Lin
  • Yi Yang
  • Gerard Melo
  • Alexander G. Hauptmann

Vast quantities of videos are now being captured at astonishing rates, but the majority of these are not labelled. To cope with such data, we consider the task of content-based activity recognition in videos without any manually labelled examples, also known as zero-shot video recognition. To achieve this, videos are represented in terms of detected visual concepts, which are then scored as relevant or irrelevant according to their similarity with a given textual query. In this paper, we propose a more robust approach for scoring concepts in order to alleviate many of the brittleness and low precision problems of previous work. Not only do we jointly consider semantic relatedness, visual reliability, and discriminative power. To handle noise and non-linearities in the ranking scores of the selected concepts, we propose a novel pairwise order matrix approach for score aggregation. Extensive experiments on the large-scale TRECVID Multimedia Event Detection data show the superiority of our approach.

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IJCAI Conference 2015 Conference Paper

Density Corrected Sparse Recovery when R. I. P. Condition Is Broken

  • Ming Lin
  • Zhengzhong Lan
  • Alexander G. Hauptmann

The Restricted Isometric Property (R. I. P.) is a very important condition for recovering sparse vectors from high dimensional space. Traditional methods often rely on R. I. P or its relaxed variants. However, in real applications, features are often correlated to each other, which makes these assumptions too strong to be useful. In this paper, we study the sparse recovery problem in which the feature matrix is strictly non-R. I. P. . We prove that when features exhibit cluster structures, which often happens in real applications, we are able to recover the sparse vector consistently. The consistency comes from our proposed density correction algorithm, which removes the variance of estimated cluster centers using cluster density. The proposed algorithm converges geometrically, achieves nearly optimal recovery bound O(s2 log(d)) where s is the sparsity and d is the nominal dimension.

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AAAI Conference 2015 Conference Paper

Exploring Semantic Inter-Class Relationships (SIR) for Zero-Shot Action Recognition

  • Chuang Gan
  • Ming Lin
  • Yi Yang
  • Yueting Zhuang
  • Alexander G.Hauptmann

Automatically recognizing a large number of action categories from videos is of significant importance for video understanding. Most existing works focused on the design of more discriminative feature representation, and have achieved promising results when the positive samples are enough. However, very limited efforts were spent on recognizing a novel action without any positive exemplars, which is often the case in the real settings due to the large amount of action classes and the users’ queries dramatic variations. To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning. Different from other zero-shot learning approaches, which exploit attributes as the intermediate layer for the knowledge transfer, our main contribution is SIR, which directly leverages the semantic inter-class relationships between the known and unknown actions followed by label transfer learning. The inter-class semantic relationships are automatically measured by continuous word vectors, which learned by the skip-gram model using the large-scale text corpus. Extensive experiments on the UCF101 dataset validate the superiority of our method over fully-supervised approaches using few positive exemplars.

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AAAI Conference 2011 Conference Paper

Self-Aware Traffic Route Planning

  • David Wilkie
  • Jur van den Berg
  • Ming Lin
  • Dinesh Manocha

One of the most ubiquitous AI applications is vehicle route planning. While state-of-the-art systems take into account current traffic conditions or historic traffic data, current planning approaches ignore the impact of their own plans on the future traffic conditions. We present a novel algorithm for self-aware route planning that uses the routes it plans for current vehicle traffic to more accurately predict future traffic conditions for subsequent cars. Our planner uses a roadmap with stochastic, timevarying traffic densities that are defined by a combination of historical data and the densities predicted by the planned routes for the cars ahead of the current traf- fic. We have applied our algorithm to large-scale traf- fic route planning, and demonstrated that our self-aware route planner can more accurately predict future traf- fic conditions, which results in a reduction of the travel time for those vehicles that use our algorithm.

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