no code implementations • 15 Jan 2024 • Shuze Liu, Shuhang Chen, Shangtong Zhang
Stochastic approximation is a class of algorithms that update a vector iteratively, incrementally, and stochastically, including, e. g., stochastic gradient descent and temporal difference learning.
1 code implementation • ACM Multimedia 2023 • Xianliang Huang, Jiajie Gou, Shuhang Chen, Zhizhou Zhong, Jihong Guan, Shuigeng Zhou
To validate the effectiveness and robustness of IDDR-NGP, we provide a wide range of distractors with corresponding annotated labels added to both realistic and synthetic scenes.
1 code implementation • 2 Oct 2023 • Sai Vemprala, Shuhang Chen, Abhinav Shukla, Dinesh Narayanan, Ashish Kapoor
In addition, the modular design enables various deep ML components and existing foundation models to be easily usable in a wider variety of robot-centric problems.
1 code implementation • ICCV 2023 • Yao Wei, Yanchao Sun, Ruijie Zheng, Sai Vemprala, Rogerio Bonatti, Shuhang Chen, Ratnesh Madaan, Zhongjie Ba, Ashish Kapoor, Shuang Ma
We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning.
no code implementations • 22 Sep 2022 • Rogerio Bonatti, Sai Vemprala, Shuang Ma, Felipe Frujeri, Shuhang Chen, Ashish Kapoor
Robotics has long been a field riddled with complex systems architectures whose modules and connections, whether traditional or learning-based, require significant human expertise and prior knowledge.
no code implementations • 27 Oct 2021 • Vivek Borkar, Shuhang Chen, Adithya Devraj, Ioannis Kontoyiannis, Sean Meyn
In addition to standard Lipschitz assumptions and conditions on the vanishing step-size sequence, it is assumed that the associated \textit{mean flow} $ \tfrac{d}{dt} \vartheta_t = \bar{f}(\vartheta_t)$, is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar{f}(\theta)=\text{ E}[f(\theta,\Phi)]$ with $\Phi$ having the stationary distribution of the chain.
no code implementations • 27 Jul 2021 • Shuhang Chen, Xiang Zhang, Xiang Shen, Yifan Huang, Yiwen Wang
In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework.
no code implementations • 30 Sep 2020 • Shuhang Chen, Adithya Devraj, Andrey Bernstein, Sean Meyn
(ii) With gain $a_t = g/(1+t)$ the results are not as sharp: the rate of convergence $1/t$ holds only if $I + g A^*$ is Hurwitz.
no code implementations • 7 Feb 2020 • Shuhang Chen, Adithya M. Devraj, Ana Bušić, Sean Meyn
This is motivation for the focus on mean square error bounds for parameter estimates.
no code implementations • NeurIPS 2020 • Shuhang Chen, Adithya M. Devraj, Fan Lu, Ana Bušić, Sean P. Meyn
Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.
no code implementations • 25 Apr 2019 • Shuhang Chen, Adithya M. Devraj, Ana Bušić, Sean P. Meyn
The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$.