Search Results for author: Chen Tang

Found 73 papers, 27 papers with code

DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation

no code implementations4 Jun 2025 Kun Zhao, Bohao Yang, Chen Tang, Siyuan Dai, Haoteng Tang, Chenghua Lin, Liang Zhan

To leverage their complementary strengths, we introduce SLIDE (Small and Large Integrated for Dialogue Evaluation), a method integrating SLMs and LLMs via adaptive weighting.

Dialogue Evaluation valid

UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines

no code implementations CVPR 2025 Chen Tang, Xinzhu Ma, Encheng Su, Xiufeng Song, Xiaohong Liu, Wei-Hong Li, Lei Bai, Wanli Ouyang, Xiangyu Yue

Traditional spatiotemporal models generally rely on task-specific architectures, which limit their generalizability and scalability across diverse tasks due to domain-specific design requirements.

Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification

no code implementations19 Mar 2025 Zhong Ji, Ci Liu, Jingren Liu, Chen Tang, Yanwei Pang, Xuelong Li

Central to this approach is the Optimal Transport Adapter (OTA), which employs a cross-modal attention mechanism to enrich textual representations and facilitate subsequent better information interaction.

Scene Classification

SurveyX: Academic Survey Automation via Large Language Models

1 code implementation20 Feb 2025 Xun Liang, Jiawei Yang, Yezhaohui Wang, Chen Tang, Zifan Zheng, Simin Niu, Shichao Song, Hanyu Wang, Bo Tang, Feiyu Xiong, Keming Mao, Zhiyu Li

Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation.

Survey

DERMARK: A Dynamic, Efficient and Robust Multi-bit Watermark for Large Language Models

no code implementations4 Feb 2025 Qihao Lin, Chen Tang, Lan Zhang, Junyang Zhang, Xiangyang Li

In this paper, we derive the watermark embedding distribution based on the logits of LLMs and propose a formal inequality to segment the text optimally for watermark embedding.

GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism

no code implementations14 Jan 2025 Chen Tang, Bo Lv, Zifan Zheng, Bohao Yang, Kun Zhao, Ning Liao, Xiaoxing Wang, Feiyu Xiong, Zhiyu Li, Nayu Liu, Jingchi Jiang

Additionally, this study explores a novel recurrent routing strategy that may inspire further advancements in enhancing the reasoning capabilities of language models.

Mixture-of-Experts

JAQ: Joint Efficient Architecture Design and Low-Bit Quantization with Hardware-Software Co-Exploration

no code implementations9 Jan 2025 Mingzi Wang, Yuan Meng, Chen Tang, Weixiang Zhang, Yijian Qin, Yang Yao, Yingxin Li, Tongtong Feng, Xin Wang, Xun Guan, Zhi Wang, Wenwu Zhu

The co-design of neural network architectures, quantization precisions, and hardware accelerators offers a promising approach to achieving an optimal balance between performance and efficiency, particularly for model deployment on resource-constrained edge devices.

Quantization

SpecFuse: Ensembling Large Language Models via Next-Segment Prediction

no code implementations10 Dec 2024 Bo Lv, Chen Tang, Yanan Zhang, Xin Liu, Yue Yu, Ping Luo

In this paper, we propose SpecFuse, a novel ensemble framework that outputs the fused result by iteratively producing the next segment through collaboration among LLMs.

Prediction

SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization

no code implementations8 Dec 2024 Shuzhao Xie, Jiahang Liu, Weixiang Zhang, Shijia Ge, Sicheng Pan, Chen Tang, Yunpeng Bai, Zhi Wang

Using the size estimator and MPQ, we develop a calibrated algorithm to identify optimal hyperparameters in just 10 minutes, achieving a 1. 69$\times$ efficiency increase with quality comparable to state-of-the-art methods.

3DGS Attribute +2

GAQAT: gradient-adaptive quantization-aware training for domain generalization

no code implementations7 Dec 2024 Jiacheng Jiang, Yuan Meng, Chen Tang, Han Yu, Qun Li, Zhi Wang, Wenwu Zhu

To address this limitation, we propose a novel Gradient-Adaptive Quantization-Aware Training (GAQAT) framework for DG.

Domain Generalization Quantization

MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation

no code implementations15 Sep 2024 Shuzhao Xie, Weixiang Zhang, Chen Tang, Yunpeng Bai, Rongwei Lu, Shijia Ge, Zhi Wang

More specifically, we first replace rotation quaternions with Euler angles; then, we apply region adaptive hierarchical transform to key attributes to reduce entropy.

Attribute Novel View Synthesis +1

Expansive Supervision for Neural Radiance Field

no code implementations12 Sep 2024 Weixiang Zhang, Shuzhao Xie, Shijia Ge, Wei Yao, Chen Tang, Zhi Wang

Neural Radiance Field (NeRF) has achieved remarkable success in creating immersive media representations through its exceptional reconstruction capabilities.

NeRF

RTF-Q: Efficient Unsupervised Domain Adaptation with Retraining-free Quantization

no code implementations11 Aug 2024 Nanyang Du, Chen Tang, Yuxiao Jiang, Yuan Meng, Zhi Wang

To address these limitations, we propose efficient unsupervised domain adaptation with ReTraining-Free Quantization (RTF-Q).

Quantization Unsupervised Domain Adaptation

Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

no code implementations7 Aug 2024 Chen Tang, Ben Abbatematteo, Jiaheng Hu, Rohan Chandra, Roberto Martín-Martín, Peter Stone

Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications, suggesting its potential for enabling the development of sophisticated robotic behaviors.

Deep Reinforcement Learning Reinforcement Learning (RL) +1

PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference

1 code implementation6 Jul 2024 Ye Li, Chen Tang, Yuan Meng, Jiajun Fan, Zenghao Chai, Xinzhu Ma, Zhi Wang, Wenwu Zhu

We introduce PRANCE, a Vision Transformer compression framework that jointly optimizes the activated channels and reduces tokens, based on the characteristics of inputs.

Combinatorial Optimization Decision Making

HGNET: A Hierarchical Feature Guided Network for Occupancy Flow Field Prediction

no code implementations1 Jul 2024 Zhan Chen, Chen Tang, Lu Xiong

Additionally, to enhance the temporal consistency and causal relationships of the predictions, we propose a Time Series Memory framework to learn the conditional distribution models of the prediction outputs at future time steps from multivariate time series.

Autonomous Driving multimodal interaction +3

BioMNER: A Dataset for Biomedical Method Entity Recognition

no code implementations28 Jun 2024 Chen Tang, Bohao Yang, Kun Zhao, Bo Lv, Chenghao Xiao, Frank Guerin, Chenghua Lin

Named entity recognition (NER) stands as a fundamental and pivotal task within the realm of Natural Language Processing.

Information Retrieval named-entity-recognition +2

Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers

1 code implementation CVPR 2025 Lei Chen, Yuan Meng, Chen Tang, Xinzhu Ma, Jingyan Jiang, Xin Wang, Zhi Wang, Wenwu Zhu

Recent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality and scalability of image and video generation.

Image Generation Model Compression +2

Crafting Customisable Characters with LLMs: Introducing SimsChat, a Persona-Driven Role-Playing Agent Framework

1 code implementation25 Jun 2024 Bohao Yang, Dong Liu, Chenghao Xiao, Kun Zhao, Chen Tang, Chao Li, Lin Yuan, Guang Yang, Lanxiao Huang, Chenghua Lin

Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior replication.

MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention

no code implementations24 Jun 2024 Yuxin Chen, Chen Tang, Chenran Li, Ran Tian, Wei Zhan, Peter Stone, Masayoshi Tomizuka

Instead of inferring the complete human behavior characteristics, MEReQ infers a residual reward function that captures the discrepancy between the human expert's and the prior policy's underlying reward functions.

Imitation Learning Q-Learning

Evaluating the Generalization Ability of Quantized LLMs: Benchmark, Analysis, and Toolbox

1 code implementation15 Jun 2024 Yijun Liu, Yuan Meng, Fang Wu, Shenhao Peng, Hang Yao, Chaoyu Guan, Chen Tang, Xinzhu Ma, Zhi Wang, Wenwu Zhu

Based on this benchmark, we conduct extensive experiments with two well-known LLMs (English and Chinese) and four quantization algorithms to investigate this topic in-depth, yielding several counter-intuitive and valuable findings, e. g., models quantized using a calibration set with the same distribution as the test data are not necessarily optimal.

Quantization

STAR: Skeleton-aware Text-based 4D Avatar Generation with In-Network Motion Retargeting

1 code implementation7 Jun 2024 Zenghao Chai, Chen Tang, Yongkang Wong, Mohan Kankanhalli

The creation of 4D avatars (i. e., animated 3D avatars) from text description typically uses text-to-image (T2I) diffusion models to synthesize 3D avatars in the canonical space and subsequently applies animation with target motions.

motion retargeting

Causal prompting model-based offline reinforcement learning

no code implementations3 Jun 2024 Xuehui Yu, Yi Guan, Rujia Shen, Xin Li, Chen Tang, Jingchi Jiang

To tackle these issues, we introduce the Causal Prompting Reinforcement Learning (CPRL) framework, designed for highly suboptimal and resource-constrained online scenarios.

model Offline RL +3

SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation

1 code implementation24 May 2024 Kun Zhao, Bohao Yang, Chen Tang, Chenghua Lin, Liang Zhan

Our approach introduces several techniques: (1) Contrastive learning to differentiate between robust and non-robust response embeddings; (2) A novel metric for semantic sensitivity that combines embedding cosine distances with similarity learned through neural networks, and (3) a strategy for incorporating the evaluation results from both the SLM and LLMs.

Contrastive Learning Dialogue Evaluation

Investigating the Impact of Quantization on Adversarial Robustness

no code implementations8 Apr 2024 Qun Li, Yuan Meng, Chen Tang, Jiacheng Jiang, Zhi Wang

Quantization is a promising technique for reducing the bit-width of deep models to improve their runtime performance and storage efficiency, and thus becomes a fundamental step for deployment.

Adversarial Robustness Quantization

Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation

1 code implementation1 Apr 2024 Bohao Yang, Kun Zhao, Chen Tang, Dong Liu, Liang Zhan, Chenghua Lin

Trainable evaluation metrics, typically trained with true positive and randomly selected negative responses, tend to assign higher scores to responses that share greater content similarity with a given context.

Abstract Meaning Representation Dialogue Evaluation +2

Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases

no code implementations20 Mar 2024 Tyler Loakman, Chen Tang, Chenghua Lin

Previous work in phonologically and phonetically grounded language generation has mainly focused on domains such as puns and poetry.

Language Modeling Language Modelling +1

BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

no code implementations22 Feb 2024 Catherine Weaver, Chen Tang, Ce Hao, Kenta Kawamoto, Masayoshi Tomizuka, Wei Zhan

Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL.

Autonomous Racing Imitation Learning +1

Retraining-free Model Quantization via One-Shot Weight-Coupling Learning

1 code implementation CVPR 2024 Chen Tang, Yuan Meng, Jiacheng Jiang, Shuzhao Xie, Rongwei Lu, Xinzhu Ma, Zhi Wang, Wenwu Zhu

Conversely, mixed-precision quantization (MPQ) is advocated to compress the model effectively by allocating heterogeneous bit-width for layers.

Model Compression Quantization

A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation

1 code implementation19 Nov 2023 Chen Tang, Tyler Loakman, Chenghua Lin

These results underscore the effectiveness of our model in leveraging context and event features to improve the quality of generated narratives.

Story Generation

Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing

no code implementations13 Nov 2023 Rongwei Lu, Yutong Jiang, Yinan Mao, Chen Tang, Bin Chen, Laizhong Cui, Zhi Wang

Recognizing the computational limitations of mobile devices, we propose the DAGC-A, which is computationally less demanding and enhances the robustness of compression in non-IID scenarios.

Federated Learning

Enhancing Biomedical Lay Summarisation with External Knowledge Graphs

1 code implementation24 Oct 2023 Tomas Goldsack, Zhihao Zhang, Chen Tang, Carolina Scarton, Chenghua Lin

Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e. g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience.

Decoder Knowledge Graphs

Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers

1 code implementation24 Oct 2023 Chen Tang, Shun Wang, Tomas Goldsack, Chenghua Lin

Abstracts derived from biomedical literature possess distinct domain-specific characteristics, including specialised writing styles and biomedical terminologies, which necessitate a deep understanding of the related literature.

Quantifying Agent Interaction in Multi-agent Reinforcement Learning for Cost-efficient Generalization

no code implementations11 Oct 2023 Yuxin Chen, Chen Tang, Ran Tian, Chenran Li, Jinning Li, Masayoshi Tomizuka, Wei Zhan

We observe that, generally, a more diverse set of co-play agents during training enhances the generalization performance of the ego agent; however, this improvement varies across distinct scenarios and environments.

Multi-agent Reinforcement Learning

Effective Distillation of Table-based Reasoning Ability from LLMs

1 code implementation22 Sep 2023 Bohao Yang, Chen Tang, Kun Zhao, Chenghao Xiao, Chenghua Lin

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks.

Table-to-Text Generation

Improving Medical Dialogue Generation with Abstract Meaning Representations

1 code implementation19 Sep 2023 Bohao Yang, Chen Tang, Chenghua Lin

In this paper, We propose a novel framework that models dialogues between patients and healthcare professionals using AMR graphs, where the neural networks incorporate textual and graphical knowledge with a dual attention mechanism.

Dialogue Generation

Pre-training on Synthetic Driving Data for Trajectory Prediction

1 code implementation18 Sep 2023 Yiheng Li, Seth Z. Zhao, Chenfeng Xu, Chen Tang, Chenran Li, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan

Accumulating substantial volumes of real-world driving data proves pivotal in the realm of trajectory forecasting for autonomous driving.

Autonomous Driving Prediction +1

Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration

no code implementations18 Sep 2023 Jinning Li, Xinyi Liu, Banghua Zhu, Jiantao Jiao, Masayoshi Tomizuka, Chen Tang, Wei Zhan

GOLD distills an offline DT policy into a lightweight policy network through guided online safe RL training, which outperforms both the offline DT policy and online safe RL algorithms.

Autonomous Driving Decision Making +4

Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation

1 code implementation28 Jun 2023 Chen Tang, Hongbo Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge.

Dialogue Generation Graph Attention +3

Residual Q-Learning: Offline and Online Policy Customization without Value

no code implementations NeurIPS 2023 Chenran Li, Chen Tang, Haruki Nishimura, Jean Mercat, Masayoshi Tomizuka, Wei Zhan

Specifically, we formulate the customization problem as a Markov Decision Process (MDP) with a reward function that combines 1) the inherent reward of the demonstration; and 2) the add-on reward specified by the downstream task.

Imitation Learning Q-Learning

Skill-Critic: Refining Learned Skills for Hierarchical Reinforcement Learning

no code implementations14 Jun 2023 Ce Hao, Catherine Weaver, Chen Tang, Kenta Kawamoto, Masayoshi Tomizuka, Wei Zhan

Our Skill-Critic algorithm optimizes both the low-level and high-level policies; these policies are initialized and regularized by the latent space learned from offline demonstrations to guide the parallel policy optimization.

Autonomous Racing Decision Making +4

TwistList: Resources and Baselines for Tongue Twister Generation

1 code implementation6 Jun 2023 Tyler Loakman, Chen Tang, Chenghua Lin

Previous work in phonetically-grounded language generation has mainly focused on domains such as lyrics and poetry.

Text Generation

Knowledge Soft Integration for Multimodal Recommendation

no code implementations12 May 2023 Kai Ouyang, Chen Tang, Wenhao Zheng, Xiangjin Xie, Xuanji Xiao, Jian Dong, Hai-Tao Zheng, Zhi Wang

To address this issue, we propose using knowledge soft integration to balance the utilization of multimodal features and the curse of knowledge problem it brings about.

Graph Neural Network Multimodal Recommendation +1

Eye tracking guided deep multiple instance learning with dual cross-attention for fundus disease detection

no code implementations25 Apr 2023 Hongyang Jiang, Jingqi Huang, Chen Tang, Xiaoqing Zhang, Mengdi Gao, Jiang Liu

Concretely, the HITL CAD system was implemented on the multiple instance learning (MIL), where eye-tracking gaze maps were beneficial to cherry-pick diagnosis-related instances.

Multiple Instance Learning

Click-aware Structure Transfer with Sample Weight Assignment for Post-Click Conversion Rate Estimation

no code implementations3 Apr 2023 Kai Ouyang, Wenhao Zheng, Chen Tang, Xuanji Xiao, Hai-Tao Zheng

To tackle this issue, we argue that a trade-off should be achieved between the introduction of large amounts of auxiliary information and the protection of valuable information related to CVR.

Multi-Task Learning

Editing Driver Character: Socially-Controllable Behavior Generation for Interactive Traffic Simulation

no code implementations24 Mar 2023 Wei-Jer Chang, Chen Tang, Chenran Li, Yeping Hu, Masayoshi Tomizuka, Wei Zhan

To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment.

Autonomous Driving

ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices

1 code implementation ICCV 2023 Chen Tang, Li Lyna Zhang, Huiqiang Jiang, Jiahang Xu, Ting Cao, Quanlu Zhang, Yuqing Yang, Zhi Wang, Mao Yang

However, prior supernet training methods that rely on uniform sampling suffer from the gradient conflict issue: the sampled subnets can have vastly different model sizes (e. g., 50M vs. 2G FLOPs), leading to different optimization directions and inferior performance.

Neural Architecture Search

SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization

no code implementations14 Feb 2023 Chen Tang, Kai Ouyang, Zenghao Chai, Yunpeng Bai, Yuan Meng, Zhi Wang, Wenwu Zhu

This general and dataset-independent property makes us search for the MPQ policy over a rather small-scale proxy dataset and then the policy can be directly used to quantize the model trained on a large-scale dataset.

Quantization

Terminology-aware Medical Dialogue Generation

1 code implementation27 Oct 2022 Chen Tang, Hongbo Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology.

Dialogue Generation

EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention

1 code implementation22 Oct 2022 Chen Tang, Chenghua Lin, Henglin Huang, Frank Guerin, Zhihao Zhang

One of the key challenges of automatic story generation is how to generate a long narrative that can maintain fluency, relevance, and coherence.

Story Generation

NGEP: A Graph-based Event Planning Framework for Story Generation

1 code implementation19 Oct 2022 Chen Tang, Zhihao Zhang, Tyler Loakman, Chenghua Lin, Frank Guerin

To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i. e. storylines) to guide story generation.

Hallucination Story Generation

Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics

1 code implementation19 Oct 2022 Henglin Huang, Chen Tang, Tyler Loakman, Frank Guerin, Chenghua Lin

In spite of the success of prior works with the application of pre-trained models, current neural models for Chinese stories still struggle to generate high-quality long text narratives.

Denoising Representation Learning +1

PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map

1 code implementation21 Apr 2022 Chenfeng Xu, Tian Li, Chen Tang, Lingfeng Sun, Kurt Keutzer, Masayoshi Tomizuka, Alireza Fathi, Wei Zhan

It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e. g., 34K samples in the nuScenes dataset).

Contrastive Learning Representation Learning +1

Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach

no code implementations21 Apr 2022 Chen Tang, Haoyu Zhai, Kai Ouyang, Zhi Wang, Yifei Zhu, Wenwu Zhu

We propose to feed different data samples with varying quantization schemes to achieve a data-dependent dynamic inference, at a fine-grained layer level.

Quantization

Interventional Behavior Prediction: Avoiding Overly Confident Anticipation in Interactive Prediction

no code implementations19 Apr 2022 Chen Tang, Wei Zhan, Masayoshi Tomizuka

Moreover, to properly evaluate an IBP model with offline datasets, we propose a Shapley-value-based metric to verify if the prediction model satisfies the inherent temporal independence of an interventional distribution.

Prediction

Mixed-Precision Neural Network Quantization via Learned Layer-wise Importance

1 code implementation16 Mar 2022 Chen Tang, Kai Ouyang, Zhi Wang, Yifei Zhu, YaoWei Wang, Wen Ji, Wenwu Zhu

For example, MPQ search on ResNet18 with our indicators takes only 0. 06 s, which improves time efficiency exponentially compared to iterative search methods.

Quantization

Recent Advances in Neural Text Generation: A Task-Agnostic Survey

no code implementations6 Mar 2022 Chen Tang, Frank Guerin, Chenghua Lin

In recent years, considerable research has been dedicated to the application of neural models in the field of natural language generation (NLG).

Survey Text Generation

Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling

no code implementations NeurIPS 2021 Chen Tang, Wei Zhan, Masayoshi Tomizuka

In this work, we argue that one of the typical formulations of VAEs in multi-agent modeling suffers from an issue we refer to as social posterior collapse, i. e., the model is prone to ignoring historical social context when predicting the future trajectory of an agent.

Graph Attention Trajectory Forecasting

Dealing with the Unknown: Pessimistic Offline Reinforcement Learning

no code implementations9 Nov 2021 Jinning Li, Chen Tang, Masayoshi Tomizuka, Wei Zhan

Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment.

reinforcement-learning Reinforcement Learning +1

Grounded Relational Inference: Domain Knowledge Driven Explainable Autonomous Driving

no code implementations23 Feb 2021 Chen Tang, Nishan Srishankar, Sujitha Martin, Masayoshi Tomizuka

Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation.

Autonomous Driving

Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs

no code implementations21 Oct 2020 Chen Tang, Wenyu Sun, Zhuqing Yuan, Yongpan Liu

To accelerate deep CNN models, this paper proposes a novel spatially adaptive framework that can dynamically generate pixel-wise sparsity according to the input image.

General Classification image-classification +5

ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations

2 code implementations26 Oct 2019 Daniel Seita, David Chan, Roshan Rao, Chen Tang, Mandi Zhao, John Canny

Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning.

Atari Games Deep Reinforcement Learning +3

Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control

no code implementations7 Dec 2018 Zhuo Xu, Chen Tang, Masayoshi Tomizuka

Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain.

Autonomous Driving Deep Reinforcement Learning

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