334 citations · 883 across the 97 of their papers we have counts for
4 papers · 1 filter
Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF
Eric Zhu, Abhinav Shrivastava, Soumik Mukhopadhyay
Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences. However, applying RLHF to diffusion mode…
TeCoNeRV: Leveraging Temporal Coherence for Compressible Neural Representations for Videos
Namitha Padmanabhan, Matthew Gwilliam, Abhinav Shrivastava
Implicit Neural Representations (INRs) have recently demonstrated impressive performance for video compression. However, since a separate INR must be overfit for each video, scalin…
Towards Understanding Best Practices for Quantization of Vision-Language Models
Gautom Das, Vincent La, Ethan Lau +2
Large language models (LLMs) deliver impressive results for a variety of tasks, but state-of-the-art systems require fast GPUs with large amounts of memory. To reduce both the memo…
UPLiFT: Efficient Pixel-Dense Feature Upsampling with Local Attenders
Matthew Walmer, Saksham Suri, Anirud Aggarwal +1
The space of task-agnostic feature upsampling has emerged as a promising area of research to efficiently create denser features from pre-trained visual backbones. These methods act…