Search Results for author: Xuejie Liu

Found 5 papers, 0 papers with code

OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents

no code implementations27 Jun 2024 ZiHao Wang, Shaofei Cai, Zhancun Mu, Haowei Lin, Ceyao Zhang, Xuejie Liu, Qing Li, Anji Liu, Xiaojian Ma, Yitao Liang

First, we introduce a self-supervised approach to learn a behavior encoder that produces discretized tokens for behavior trajectories $\tau = \{o_0, a_0, \dots\}$ and an imitation learning policy decoder conditioned on these tokens.

Decoder Imitation Learning +2

A Tractable Inference Perspective of Offline RL

no code implementations31 Oct 2023 Xuejie Liu, Anji Liu, Guy Van Den Broeck, Yitao Liang

A popular paradigm for offline Reinforcement Learning (RL) tasks is to first fit the offline trajectories to a sequence model, and then prompt the model for actions that lead to high expected return.

Offline RL Reinforcement Learning (RL)

Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits

no code implementations16 Feb 2023 Xuejie Liu, Anji Liu, Guy Van Den Broeck, Yitao Liang

In this paper, we theoretically and empirically discover that the performance of a PC can exceed that of its teacher model.

Supervised learning of sparse context reconstruction coefficients for data representation and classification

no code implementations18 Aug 2015 Xuejie Liu, Jingbin Wang, Ming Yin, Benjamin Edwards, Peijuan Xu

Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification.

Classification General Classification

Sparse Coding with Earth Mover's Distance for Multi-Instance Histogram Representation

no code implementations9 Feb 2015 Mohua Zhang, Jianhua Peng, Xuejie Liu, Jim Jing-Yan Wang

It attempts to represent the feature vector of a data sample by reconstructing it as the sparse linear combination of some basic elements, and a $L_2$ norm distance function is usually used as the loss function for the reconstruction error.

Retrieval

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