1 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
Generative Actor Critic
Aoyang Qin, Deqian Kong, Wei Wang +3
Conventional Reinforcement Learning (RL) algorithms, typically focused on estimating or maximizing expected returns, face challenges when refining offline pretrained models with on…
FFT-Accelerated Auxiliary Variable MCMC for Fermionic Lattice Models: A Determinant-Free Approach with Complexity
Deqian Kong, Shi Feng, Jianwen Xie +1
We introduce a Markov Chain Monte Carlo (MCMC) algorithm that dramatically accelerates the simulation of quantum many-body systems, a grand challenge in computational science. Stat…
Latent Adaptive Planner for Dynamic Manipulation
Donghun Noh, Deqian Kong, Minglu Zhao +4
We present the Latent Adaptive Planner (LAP), a trajectory-level latent-variable policy for dynamic nonprehensile manipulation (e.g., box catching) that formulates planning as infe…
Latent Thought Models with Variational Bayes Inference-Time Computation
Deqian Kong, Minglu Zhao, Dehong Xu +8
We propose a novel class of language models, Latent Thought Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space. Th…