23 citations · 38 across the 10 of their papers we have counts for
11 papers
Nested Sparsity for Scalable Pretraining and Continual Learning
Tianzhe Chu, Druv Pai, Yi Ma
Mixture-of-experts (MoE) transformers, the standard backbone for modern large-scale deep learning, employ conditional computation: only a sparse subset of model weights is used in…
DexHoldem: An Agentic Robotics Benchmark for Dexterous Manipulation in Texas Hold'em
Feng Chen, Tianzhe Chu, Li Sun +6
Evaluating embodied systems with real dexterous hardware requires more than isolated motor-skill tests: an agent must perceive a changing scene (e.g. a tabletop), choose a context-…
Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces Selection
Dacheng Qi, Chenyu Wang, Jingwei Xu +6
Constructing computer-aided design (CAD) models is labor-intensive but essential for engineering and manufacturing. Recent advances in Large Language Models (LLMs) have inspired th…
DeepTutor: Towards Agentic Personalized Tutoring
Bingxi Zhao, Jiahao Zhang, Xubin Ren +4
Education is one of the most promising real-world applications for Large Language Models (LLMs). However, current LLMs rely on static pre-training knowledge and lack adaptation to…
GenDexHand: Generative Simulation for Dexterous Hands
Feng Chen, Zhuxiu Xu, Tianzhe Chu +7
Data scarcity remains a fundamental bottleneck for embodied intelligence. Existing approaches use large language models (LLMs) to automate gripper-based simulation generation, but…
Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs
Chun-Hsiao Yeh, Chenyu Wang, Shengbang Tong +7
Multi-view understanding, the ability to reconcile visual information across diverse viewpoints for effective navigation, manipulation, and 3D scene comprehension, is a fundamental…
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Tianzhe Chu, Yuexiang Zhai, Jihan Yang +6
Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models. However, their roles in enhancing model generalization…
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?
Yaodong Yu, Sam Buchanan, Druv Pai +7
In this paper, we contend that a natural objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a low-dimension…
Emergence of Segmentation with Minimalistic White-Box Transformers
Yaodong Yu, Tianzhe Chu, Shengbang Tong +4
Transformer-like models for vision tasks have recently proven effective for a wide range of downstream applications such as segmentation and detection. Previous works have shown th…
Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Models
Tianzhe Chu, Shengbang Tong, Tianjiao Ding +4
The advent of large pre-trained models has brought about a paradigm shift in both visual representation learning and natural language processing. However, clustering unlabeled imag…
White-Box Transformers via Sparse Rate Reduction
Yaodong Yu, Sam Buchanan, Druv Pai +5
In this paper, we contend that the objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a mixture of low-dime…