1 citations · 1 across the 7 of their papers we have counts for
8 papers
Acceleration of Diffusion Language Model through Discrete Average Generator
Yidong Ouyang, Zhengyan Wan, Themis Haris +8
Discrete diffusion models and flow matching have emerged as powerful frameworks for generative modeling over discrete state spaces, yet efficient few-step generation remains a fund…
Elastic Threshold Attention: Learned Contextual Sparsity for Long-Context Decoding
Themistoklis Haris, Henry Li, Maryam Karimzadehgan
Massive KV caches can cause severe memory-bandwidth bottlenecks during long-context decoding. Sparse attention methods mitigate this problem, but often drop necessary context, lead…
A Unified Model and Document Representation for On-Device Retrieval-Augmented Generation
Julian Killingback, Ofer Meshi, Henry Li +2
Traditional Retrieval-Augmented Generation (RAG) approaches generally assume that retrieval and generation occur on powerful servers removed from the end user. While this reduces l…
Injecting Measurement Information Yields a Fast and Noise-Robust Diffusion-Based Inverse Problem Solver
Jonathan Patsenker, Henry Li, Myeongseob Ko +2
Diffusion models have been firmly established as principled zero-shot solvers for linear and nonlinear inverse problems, owing to their powerful image prior and iterative sampling…
Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
Henry Li, Ronen Basri, Yuval Kluger
Cascaded models are multi-scale generative models with a marked capacity for producing perceptually impressive samples at high resolutions. In this work, we show that they can also…
Dual Diffusion for Unified Image Generation and Understanding
Zijie Li, Henry Li, Yichun Shi +4
Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-langua…
Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
Myeongseob Ko, Henry Li, Zhun Wang +6
Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful o…
Exponential weight averaging as damped harmonic motion
Jonathan Patsenker, Henry Li, Yuval Kluger
The exponential moving average (EMA) is a commonly used statistic for providing stable estimates of stochastic quantities in deep learning optimization. Recently, EMA has seen cons…