14 citations · 16 across the 11 of their papers we have counts for
11 papers
Train Less, Infer Faster: Efficient Model Finetuning and Compression via Structured Sparsity
Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum
Fully finetuning foundation language models (LMs) with billions of parameters is often impractical due to high computational costs, memory requirements, and the risk of overfitting…
Provable Speech Attributes Conversion via Latent Independence
Jonathan Svirsky, Ofir Lindenbaum, Uri Shaham
Conditional generation and disentangled representation learning are central to controlled generation across audio, vision, and multimodal domains. However, despite strong empirical…
FineGates: LLMs Finetuning with Compression using Stochastic Gates
Jonathan Svirsky, Yehonathan Refael, Ofir Lindenbaum
Large Language Models (LLMs), with billions of parameters, present significant challenges for full finetuning due to the high computational demands, memory requirements, and imprac…
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
Yehonathan Refael, Jonathan Svirsky, Boris Shustin +2
Training and fine-tuning large language models (LLMs) come with challenges related to memory and computational requirements due to the increasing size of the model weights and the…
Sparse Binarization for Fast Keyword Spotting
Jonathan Svirsky, Uri Shaham, Ofir Lindenbaum
With the increasing prevalence of voice-activated devices and applications, keyword spotting (KWS) models enable users to interact with technology hands-free, enhancing convenience…
Self Supervised Correlation-based Permutations for Multi-View Clustering
Ran Eisenberg, Jonathan Svirsky, Ofir Lindenbaum
Combining data from different sources can improve data analysis tasks such as clustering. However, most of the current multi-view clustering methods are limited to specific domains…
Interpretable Deep Clustering for Tabular Data
Jonathan Svirsky, Ofir Lindenbaum
Clustering is a fundamental learning task widely used as a first step in data analysis. For example, biologists use cluster assignments to analyze genome sequences, medical records…
SG-VAD: Stochastic Gates Based Speech Activity Detection
Jonathan Svirsky, Ofir Lindenbaum
We propose a novel voice activity detection (VAD) model in a low-resource environment. Our key idea is to model VAD as a denoising task, and construct a network that is designed to…
Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features
Uri Shaham, Ofir Lindenbaum, Jonathan Svirsky +1
Modern datasets often contain large subsets of correlated features and nuisance features, which are not or loosely related to the main underlying structures of the data. Nuisance f…
Deep Ordinal Regression using Optimal Transport Loss and Unimodal Output Probabilities
Uri Shaham, Igal Zaidman, Jonathan Svirsky
It is often desired that ordinal regression models yield unimodal predictions. However, in many recent works this characteristic is either absent, or implemented using soft targets…
Differentiable Unsupervised Feature Selection based on a Gated Laplacian
Ofir Lindenbaum, Uri Shaham, Jonathan Svirsky +2
Scientific observations may consist of a large number of variables (features). Identifying a subset of meaningful features is often ignored in unsupervised learning, despite its po…