Search Results for author: Jiayu Chen

Found 27 papers, 12 papers with code

Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries

no code implementations15 Mar 2024 Swetha Ganesh, Jiayu Chen, Gugan Thoppe, Vaneet Aggarwal

Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories.

Decision Making Policy Gradient Methods

Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformer

1 code implementation12 Mar 2024 Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, Vaneet Aggarwal

Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications.

Decision Making

Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions

no code implementations21 Feb 2024 Jiayu Chen, Bhargav Ganguly, Yang Xu, Yongsheng Mei, Tian Lan, Vaneet Aggarwal

This work offers a hands-on reference for the research progress in deep generative models for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms.

Imitation Learning Offline RL

Active Generation Network of Human Skeleton for Action Recognition

no code implementations30 Jan 2024 Long Liu, Xin Wang, Fangming Li, Jiayu Chen

To solve those problems, We propose a novel active generative network (AGN), which can adaptively learn various action categories by motion style transfer to generate new actions when the data for a particular action is only a single sample or few samples.

Action Generation Action Recognition +4

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

1 code implementation25 Jan 2024 Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kunchang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, Lei Zhang

We introduce Grounded SAM, which uses Grounding DINO as an open-set object detector to combine with the segment anything model (SAM).

Segmentation

TaskFlex Solver for Multi-Agent Pursuit via Automatic Curriculum Learning

no code implementations19 Dec 2023 Jiayu Chen, Guosheng Li, Chao Yu, Xinyi Yang, Botian Xu, Huazhong Yang, Yu Wang

In this work, we combine RL and curriculum learning to introduce a flexible solver for multiagent pursuit problems, named TaskFlex Solver (TFS), which is capable of solving multi-agent pursuit problems with diverse and dynamically changing task conditions in both 2-dimensional and 3-dimensional scenarios.

Reinforcement Learning (RL)

MASP: Scalable GNN-based Planning for Multi-Agent Navigation

no code implementations5 Dec 2023 Xinyi Yang, Xinting Yang, Chao Yu, Jiayu Chen, Huazhong Yang, Yu Wang

Besides, to enhance generalization capabilities in scenarios with unseen team sizes, we divide agents into multiple groups, each with a previously trained number of agents.

Reinforcement Learning (RL) Zero-shot Generalization

Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data

1 code implementation6 Nov 2023 Bishal Thapaliya, Esra Akbas, Jiayu Chen, Raam Sapkota, Bhaskar Ray, Pranav Suresh, Vince Calhoun, Jingyu Liu

Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli.

Active Neural Topological Mapping for Multi-Agent Exploration

no code implementations1 Nov 2023 Xinyi Yang, Yuxiang Yang, Chao Yu, Jiayu Chen, Jingchen Yu, Haibing Ren, Huazhong Yang, Yu Wang

In this paper, we propose Multi-Agent Neural Topological Mapping (MANTM) to improve exploration efficiency and generalization for multi-agent exploration tasks.

Hierarchical Side-Tuning for Vision Transformers

no code implementations9 Oct 2023 Weifeng Lin, Ziheng Wu, Jiayu Chen, Wentao Yang, Mingxin Huang, Jun Huang, Lianwen Jin

Fine-tuning pre-trained Vision Transformers (ViT) has consistently demonstrated promising performance in the realm of visual recognition.

Instance Segmentation object-detection +4

DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion

no code implementations21 Sep 2023 Zhenzhen Chu, Jiayu Chen, Cen Chen, Chengyu Wang, Ziheng Wu, Jun Huang, Weining Qian

Position-aware global tokens also contain the position information of the image, which makes our model better for vision tasks.

Image Classification object-detection +3

Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs

no code implementations21 Jul 2023 Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal

Our key idea is to approximate the joint state space as a Kronecker graph, based on which we can directly estimate its Fiedler vector using the Laplacian spectrum of individual agents' transition graphs.

Representation Learning

Scale-Aware Modulation Meet Transformer

1 code implementation ICCV 2023 Weifeng Lin, Ziheng Wu, Jiayu Chen, Jun Huang, Lianwen Jin

Specifically, SMT with 11. 5M / 2. 4GFLOPs and 32M / 7. 7GFLOPs can achieve 82. 2% and 84. 3% top-1 accuracy on ImageNet-1K, respectively.

object-detection Object Detection +1

Hierarchical Deep Counterfactual Regret Minimization

1 code implementation27 May 2023 Jiayu Chen, Tian Lan, Vaneet Aggarwal

Imperfect Information Games (IIGs) offer robust models for scenarios where decision-makers face uncertainty or lack complete information.

counterfactual Decision Making

Multi-task Hierarchical Adversarial Inverse Reinforcement Learning

1 code implementation22 May 2023 Jiayu Chen, Dipesh Tamboli, Tian Lan, Vaneet Aggarwal

Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots.

Imitation Learning Multi-Task Learning +1

Detection Transformer with Stable Matching

1 code implementation ICCV 2023 Shilong Liu, Tianhe Ren, Jiayu Chen, Zhaoyang Zeng, Hao Zhang, Feng Li, Hongyang Li, Jun Huang, Hang Su, Jun Zhu, Lei Zhang

We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR.

Position

Multi-agent Deep Covering Skill Discovery

no code implementations7 Oct 2022 Jiayu Chen, Marina Haliem, Tian Lan, Vaneet Aggarwal

In this case, we propose Multi-agent Deep Covering Option Discovery, which constructs the multi-agent options through minimizing the expected cover time of the multiple agents' joint state space.

Multi-agent Reinforcement Learning reinforcement-learning +1

Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control

1 code implementation5 Oct 2022 Jiayu Chen, Tian Lan, Vaneet Aggarwal

In this work, we develop a novel HIL algorithm based on Adversarial Inverse Reinforcement Learning and adapt it with the Expectation-Maximization algorithm in order to directly recover a hierarchical policy from the unannotated demonstrations.

Imitation Learning Multi-Task Learning +2

Prediction of Gender from Longitudinal MRI data via Deep Learning on Adolescent Data Reveals Unique Patterns Associated with Brain Structure and Change over a Two-year Period

no code implementations15 Sep 2022 Yuda Bi, Anees Abrol, Zening Fu, Jiayu Chen, Jingyu Liu, Vince Calhoun

Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks.

Gender Prediction

Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs

no code implementations20 Jan 2022 Jiayu Chen, Jingdi Chen, Tian Lan, Vaneet Aggarwal

Covering skill (a. k. a., option) discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph.

reinforcement-learning Reinforcement Learning (RL)

Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems

1 code implementation NeurIPS 2021 Jiayu Chen, Yuanxin Zhang, Yuanfan Xu, Huimin Ma, Huazhong Yang, Jiaming Song, Yu Wang, Yi Wu

We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.

Multi-agent Reinforcement Learning

Scalable Polyhedral Verification of Recurrent Neural Networks

1 code implementation27 May 2020 Wonryong Ryou, Jiayu Chen, Mislav Balunovic, Gagandeep Singh, Andrei Dan, Martin Vechev

We present a scalable and precise verifier for recurrent neural networks, called Prover based on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and nonlinear recurrent update functions by combining sampling, optimization, and Fermat's theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron.

Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

no code implementations19 Nov 2019 Pin Wang, Dapeng Liu, Jiayu Chen, Hanhan Li, Ching-Yao Chan

Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.

Autonomous Driving Imitation Learning +2

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