29 citations · 32 across the 14 of their papers we have counts for
14 papers
Quantifying and Optimizing Simplicity via Polynomial Representations
Tianren Zhang, Xiangxin Li, Minghao Xiao +2
Deep networks often exhibit a preference for "simple" solutions, and such a simplicity bias is widely believed to play a key role in generalization. Yet a broadly applicable, quant…
Semantic-Enriched Latent Visual Reasoning
Tianrun Xu, Yue Sun, Qixun Wang +8
Multimodal latent-space reasoning aims to replace explicit thinking with images by performing visual reasoning directly in a compact latent space. However, existing approaches larg…
Reconciling In-Context and In-Weight Learning via Dual Representation Space Encoding
Guanyu Chen, Ruichen Wang, Tianren Zhang +1
In-context learning (ICL) is a valuable capability exhibited by Transformers pretrained on diverse sequence tasks. However, previous studies have observed that ICL often conflicts…
DeFacto: Counterfactual Thinking with Images for Enforcing Evidence-Grounded and Faithful Reasoning
Tianrun Xu, Haoda Jing, Ye Li +6
Recent advances in multimodal language models (MLLMs) have made thinking with images a dominant paradigm for multimodal reasoning. However, existing methods still fail to ensure ev…
Exploring the Hidden Reasoning Process of Large Language Models by Misleading Them
Guanyu Chen, Peiyang Wang, Yizhou Jiang +5
Large language models (LLMs) have been able to perform various forms of reasoning tasks in a wide range of scenarios, but are they truly engaging in task abstraction and rule-based…
When Do Neural Networks Learn World Models?
Tianren Zhang, Guanyu Chen, Feng Chen
Humans develop world models that capture the underlying generation process of data. Whether neural networks can learn similar world models remains an open problem. In this work, we…
Feature contamination: Neural networks learn uncorrelated features and fail to generalize
Tianren Zhang, Chujie Zhao, Guanyu Chen +2
Learning representations that generalize under distribution shifts is critical for building robust machine learning models. However, despite significant efforts in recent years, al…
Preserving Silent Features for Domain Generalization
Chujie Zhao, Tianren Zhang, Feng Chen
Domain generalization (DG) aims to improve the generalization ability of the model trained on several known training domains over unseen test domains. Previous work has shown that…
Learning Invariable Semantical Representation from Language for Extensible Policy Generalization
Yihan Li, Jinsheng Ren, Tianrun Xu +3
Recently, incorporating natural language instructions into reinforcement learning (RL) to learn semantically meaningful representations and foster generalization has caught many co…
Adjacency constraint for efficient hierarchical reinforcement learning
Tianren Zhang, Shangqi Guo, Tian Tan +2
Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training in…
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
Chuan-Fei Zhang, Yan Liu, Tian-Hao Zhang +3
Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and a substantial part of NAR transformer models is to use the casu…
A method of supervised learning from conflicting data with hidden contexts
Tianren Zhang, Yizhou Jiang, Feng Chen
Conventional supervised learning assumes a stable input-output relationship. However, this assumption fails in open-ended training settings where the input-output relationship depe…
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model
Chongkai Gao, Haichuan Gao, Shangqi Guo +2
Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided a…
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning
Tianren Zhang, Shangqi Guo, Tian Tan +2
Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training in…