2 citations · 4 across the 15 of their papers we have counts for
19 papers
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…
TabGRU: An Enhanced Design for Urban Rainfall Intensity Estimation Using Commercial Microwave Links
Xingwang Li, Mengyun Chen, Jiamou Liu +8
In the face of accelerating global urbanization and the increasing frequency of extreme weather events, highresolution urban rainfall monitoring is crucial for building resilient s…
MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression
Ofir Gordon, Ariel Lapid, Elad Cohen +3
Deploying transformer-based neural networks on resource-constrained edge devices presents a significant challenge. This challenge is often addressed through various techniques, suc…
Learned Bayesian Cramér-Rao Bound for Unknown Measurement Models Using Score Neural Networks
Hai Victor Habi, Hagit Messer, Yoram Bresler
The Bayesian Cramér-Rao bound (BCRB) is a crucial tool in signal processing for assessing the fundamental limitations of any estimation problem as well as benchmarking within a Bay…
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo
Idan Achituve, Hai Victor Habi, Amir Rosenfeld +3
In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known) degradation operator. Commonly…
Efficient Image Restoration via Latent Consistency Flow Matching
Elad Cohen, Idan Achituve, Idit Diamant +2
Recent advances in generative image restoration (IR) have demonstrated impressive results. However, these methods are hindered by their substantial size and computational demands,…
Data Generation for Hardware-Friendly Post-Training Quantization
Lior Dikstein, Ariel Lapid, Arnon Netzer +1
Zero-shot quantization (ZSQ) using synthetic data is a key approach for post-training quantization (PTQ) under privacy and security constraints. However, existing data generation m…
EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization
Ofir Gordon, Elad Cohen, Hai Victor Habi +1
Quantization is a key method for deploying deep neural networks on edge devices with limited memory and computation resources. Recent improvements in Post-Training Quantization (PT…
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation
Idit Diamant, Roy H. Jennings, Oranit Dror +2
Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation.…
Learning to Bound: A Generative Cramér-Rao Bound
Hai Victor Habi, Hagit Messer, Yoram Bresler
The Cramér-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems. However, to obtain t…
HPTQ: Hardware-Friendly Post Training Quantization
Hai Victor Habi, Reuven Peretz, Elad Cohen +5
Neural network quantization enables the deployment of models on edge devices. An essential requirement for their hardware efficiency is that the quantizers are hardware-friendly: u…
Multi-View Image-to-Image Translation Supervised by 3D Pose
Idit Diamant, Oranit Dror, Hai Victor Habi +1
We address the task of multi-view image-to-image translation for person image generation. The goal is to synthesize photo-realistic multi-view images with pose-consistency across a…
HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
Hai Victor Habi, Roy H. Jennings, Arnon Netzer
Recent work in network quantization produced state-of-the-art results using mixed precision quantization. An imperative requirement for many efficient edge device hardware implemen…
Genetic Network Architecture Search
Hai Victor Habi, Gil Rafalovich
We propose a method for learning the neural network architecture that based on Genetic Algorithm (GA). Our approach uses a genetic algorithm integrated with standard Stochastic Gra…