2 citations · 4 across the 15 of their papers we have counts for
6 papers · 1 filter
VisionMX: Unlocking Microscaling Post-Training Quantization for Vision Models
Elad Dror Cohen, Ofir Gordon, Lior Dikstein +2
Microscaling (MX) formats are emerging as a hardware-supported approach to efficient training and inference. They combine low-precision elements with shared block scales, but their…
PG-SELD: Physics-Guided Sound Event Localization and Detection
Elad Cohen, Elad Dror Cohen, Arnon Netzer +1
Sound event localization and detection (SELD) aims to jointly recognize sound events and estimate their directions of arrival from multichannel audio. Although recent deep learning…
Adaptive Latent Capacity for World Models
Idan Achituve, Lior Dikstein, Idit Diamant +2
We introduce Adaptive LeWorldModel (ALeWM), a world model based on a joint-embedding predictive architecture (JEPA) that learns to concentrate predictive information in compact pre…
Low-Latency State Space Voice Activity Detection with Robust Onset Time Evaluation
Elad Cohen, Arnon Netzer, Hai Victor Habi
Voice Activity Detection (VAD) systems are commonly evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), but these metrics do not acc…
Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Badr Moufad, Albina Ilina, Hai Victor Habi +4
Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate ground-level reconstruction challeng…
LATMiX: Learnable Affine Transformations for Microscaling Quantization of LLMs
Ofir Gordon, Lior Dikstein, Arnon Netzer +2
Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent studies have shown that applying invert…