3 citations · 4 across the 9 of their papers we have counts for
10 papers
More Value per Key: Asymmetric Sparse Attention for Faster LLM Decoding
Noam Elata, Itay Lamprecht, Mikey Shechter +3
Autoregressive generation in Large Language Models (LLMs) is constrained by the memory and computational demands of attention mechanisms. Sparse attention methods mitigate this cos…
Accelerating Text-to-Video Generation with Calibrated Sparse Attention
Shai Yehezkel, Shahar Yadin, Noam Elata +2
Recent diffusion models enable high-quality video generation, but suffer from slow runtimes. The large transformer-based backbones used in these models are bottlenecked by spatiote…
Block Sparse Flash Attention
Daniel Ohayon, Itay Lamprecht, Itay Hubara +3
Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity creates a severe computational bottlene…
InvFusion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems
Noam Elata, Hyungjin Chung, Jong Chul Ye +2
Diffusion Models have demonstrated remarkable capabilities in handling inverse problems, offering high-quality posterior-sampling-based solutions. Despite significant advances, a f…
Novel View Synthesis with Pixel-Space Diffusion Models
Noam Elata, Bahjat Kawar, Yaron Ostrovsky-Berman +2
Synthesizing a novel view from a single input image is a challenging task. Traditionally, this task was approached by estimating scene depth, warping, and inpainting, with machine…
Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling
Noam Elata, Tomer Michaeli, Michael Elad
Compressed Sensing (CS) facilitates rapid image acquisition by selecting a small subset of measurements sufficient for high-fidelity reconstruction. Adaptive CS seeks to further en…
PSC: Posterior Sampling-Based Compression
Noam Elata, Tomer Michaeli, Michael Elad
Diffusion models have transformed the landscape of image generation and now show remarkable potential for image compression. Most of the recent diffusion-based compression methods…
Classification Diffusion Models: Revitalizing Density Ratio Estimation
Shahar Yadin, Noam Elata, Tomer Michaeli
A prominent family of methods for learning data distributions relies on density ratio estimation (DRE), where a model is trained to between data samples and sam…
Nested Diffusion Processes for Anytime Image Generation
Noam Elata, Bahjat Kawar, Tomer Michaeli +1
Diffusion models are the current state-of-the-art in image generation, synthesizing high-quality images by breaking down the generation process into many fine-grained denoising ste…
GSURE-Based Diffusion Model Training with Corrupted Data
Bahjat Kawar, Noam Elata, Tomer Michaeli +1
Diffusion models have demonstrated impressive results in both data generation and downstream tasks such as inverse problems, text-based editing, classification, and more. However,…