2 citations · 3 across the 9 of their papers we have counts for
10 papers
Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation
Woo Chul Shin, Zhenyang Chen, Alfred Cueva +5
Visuomotor policies have advanced on manipulation tasks where the target object stays static during execution, but real deployments break this assumption: parts drift on conveyors…
EffGen: Enabling Small Language Models as Capable Autonomous Agents
Gaurav Srivastava, Aafiya Hussain, Chi Wang +2
Most existing language model agentic systems today are built and optimized for large language models (e.g., GPT, Claude, Gemini) via API calls; while powerful, this approach faces…
Report for NSF Workshop on AI for Electronic Design Automation
Deming Chen, Vijay Ganesh, Weikai Li +7
This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver alongside NeurIP…
FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
Fatih Ilhan, Selim Furkan Tekin, Tiansheng Huang +6
Fine-tuning pre-trained large language models (LLMs) has become a common practice for personalized natural language understanding (NLU) applications on downstream tasks and domain-…
LaCache: Ladder-Shaped KV Caching for Efficient Long-Context Modeling of Large Language Models
Dachuan Shi, Yonggan Fu, Xiangchi Yuan +8
Recent advancements in Large Language Models (LLMs) have spurred interest in numerous applications requiring robust long-range capabilities, essential for processing extensive inpu…
SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung +8
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined…
Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers
Haoran You, Connelly Barnes, Yuqian Zhou +10
Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) image generation quality but suffer from high latency and memory inefficiency, making them difficult to deploy o…
When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models
Haoran You, Yichao Fu, Zheng Wang +2
Autoregressive Large Language Models (LLMs) have achieved impressive performance in language tasks but face two significant bottlenecks: (1) quadratic complexity in the attention m…
Unveiling and Harnessing Hidden Attention Sinks: Enhancing Large Language Models without Training through Attention Calibration
Zhongzhi Yu, Zheng Wang, Yonggan Fu +3
Attention is a fundamental component behind the remarkable achievements of large language models (LLMs). However, our current understanding of the attention mechanism, especially r…
ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
Haoran You, Yipin Guo, Yichao Fu +6
Large language models (LLMs) have shown impressive performance on language tasks but face challenges when deployed on resource-constrained devices due to their extensive parameters…