13 citations · 21 across the 13 of their papers we have counts for
19 papers
Test-Time Training as Residual Memory for Robot Policies
Haoxuan Wang, Gengyu Zhang, Ramana Rao Kompella +2
Memory is essential for long-horizon robotic manipulation, where successful actions may depend on past events that are no longer recoverable from the current observation. As episod…
Fisher-Guided Submodular Data Selection for Continual Pre-Training of Large Language Models
Zhenghao Zhao, Gaowen Liu, Zhiling Lan +1
Data selection is already a central bottleneck in large-language-model training, where web-scale corpora are noisy and token budgets are finite. In continual pre-training (CPT), it…
Looking Back to Move Forward: Temporal Verification for Generative Robot Policies
Haoxuan Wang, Wayne Wu, Yan Yan +1
Generative policies have emerged as a promising paradigm for robot learning, combining expressive generative action modeling with scalable imitation learning from large demonstrati…
Real-Time Robot Execution with Masked Action Chunking
Haoxuan Wang, Gengyu Zhang, Yan Yan +3
Real-time execution is essential for cyber-physical systems such as robots. These systems operate in dynamic real-world environments where even small delays can undermine responsiv…
Distill Video Datasets into Images
Zhenghao Zhao, Haoxuan Wang, Kai Wang +3
Dataset distillation aims to synthesize compact yet informative datasets that allow models trained on them to achieve performance comparable to training on the full dataset. While…
Efficient Multimodal Dataset Distillation via Generative Models
Zhenghao Zhao, Haoxuan Wang, Junyi Wu +3
Dataset distillation aims to synthesize a small dataset from a large dataset, enabling the model trained on it to perform well on the original dataset. With the blooming of large l…
CaO: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation
Haoxuan Wang, Zhenghao Zhao, Junyi Wu +3
The recent introduction of diffusion models in dataset distillation has shown promising potential in creating compact surrogate datasets for large, high-resolution target datasets,…
DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture
Qianlong Xiang, Miao Zhang, Yuzhang Shang +3
Diffusion models (DMs) have demonstrated exceptional generative capabilities across various domains, including image, video, and so on. A key factor contributing to their effective…
Distilling Long-tailed Datasets
Zhenghao Zhao, Haoxuan Wang, Yuzhang Shang +2
Dataset distillation aims to synthesize a small, information-rich dataset from a large one for efficient model training. However, existing dataset distillation methods struggle wit…
Dataset Quantization with Active Learning based Adaptive Sampling
Zhenghao Zhao, Yuzhang Shang, Junyi Wu +1
Deep learning has made remarkable progress recently, largely due to the availability of large, well-labeled datasets. However, the training on such datasets elevates costs and comp…
Efficient Multitask Dense Predictor via Binarization
Yuzhang Shang, Dan Xu, Gaowen Liu +2
Multi-task learning for dense prediction has emerged as a pivotal area in computer vision, enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks.…
PTQ4DiT: Post-training Quantization for Diffusion Transformers
Junyi Wu, Haoxuan Wang, Yuzhang Shang +2
The recent introduction of Diffusion Transformers (DiTs) has demonstrated exceptional capabilities in image generation by using a different backbone architecture, departing from tr…
Online Multi-spectral Neuron Tracing
Bin Duan, Yuzhang Shang, Dawen Cai +1
In this paper, we propose an online multi-spectral neuron tracing method with uniquely designed modules, where no offline training are required. Our method is trained online to upd…
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
Yuzhang Shang, Mu Cai, Bingxin Xu +2
Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model. LMMs typically take in a fixed and la…
FBPT: A Fully Binary Point Transformer
Zhixing Hou, Yuzhang Shang, Yan Yan
This paper presents a novel Fully Binary Point Cloud Transformer (FBPT) model which has the potential to be widely applied and expanded in the fields of robotics and mobile devices…
LLM Inference Unveiled: Survey and Roofline Model Insights
Zhihang Yuan, Yuzhang Shang, Yang Zhou +11
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is v…
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Haoxuan Wang, Yuzhang Shang, Zhihang Yuan +3
The practical deployment of diffusion models is still hindered by the high memory and computational overhead. Although quantization paves a way for model compression and accelerati…
MIM4DD: Mutual Information Maximization for Dataset Distillation
Yuzhang Shang, Zhihang Yuan, Yan Yan
Dataset distillation (DD) aims to synthesize a small dataset whose test performance is comparable to a full dataset using the same model. State-of-the-art (SoTA) methods optimize s…
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models
Zhihang Yuan, Yuzhang Shang, Yue Song +4
In this paper, we introduce a new post-training compression paradigm for Large Language Models (LLMs) to facilitate their wider adoption. We delve into LLM weight low-rank decompos…