40 citations · 137 across the 37 of their papers we have counts for
41 papers
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…
TempoControl: Temporal Attention Guidance for Text-to-Video Models
Shira Schiber, Ofir Lindenbaum, Idan Schwartz
Recent advances in generative video models have enabled the creation of high-quality videos based on natural language prompts. However, these models frequently lack fine-grained te…
LORENZA: Enhancing Generalization in Low-Rank Gradient LLM Training via Efficient Zeroth-Order Adaptive SAM
Yehonathan Refael, Iftach Arbel, Ofir Lindenbaum +1
We study robust parameter-efficient fine-tuning (PEFT) techniques designed to improve accuracy and generalization while operating within strict computational and memory hardware co…
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…
Generalizable and Robust Spectral Method for Multi-view Representation Learning
Amitai Yacobi, Ofir Lindenbaum, Uri Shaham
Multi-view representation learning (MvRL) has garnered substantial attention in recent years, driven by the increasing demand for applications that can effectively process and anal…
TransformLLM: Adapting Large Language Models via LLM-Transformed Reading Comprehension Text
Iftach Arbel, Yehonathan Refael, Ofir Lindenbaum
Large Language Models (LLMs) have shown promise in highly-specialized domains, however challenges are still present in aspects of accuracy and costs. These limitations restrict the…
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…
Spectral Self-supervised Feature Selection
Daniel Segal, Ofir Lindenbaum, Ariel Jaffe
Choosing a meaningful subset of features from high-dimensional observations in unsupervised settings can greatly enhance the accuracy of downstream analysis, such as clustering or…
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…
Unveiling Multiple Descents in Unsupervised Autoencoders
Kobi Rahimi, Yehonathan Refael, Tom Tirer +1
The phenomenon of double descent has challenged the traditional bias-variance trade-off in supervised learning but remains unexplored in unsupervised learning, with some studies ar…
Knowledge Editing in Language Models via Adapted Direct Preference Optimization
Amit Rozner, Barak Battash, Lior Wolf +1
Large Language Models (LLMs) can become outdated over time as they may lack updated world knowledge, leading to factual knowledge errors and gaps. Knowledge Editing (KE) aims to ov…
SEL-CIE: Knowledge-Guided Self-Supervised Learning Framework for CIE-XYZ Reconstruction from Non-Linear sRGB Images
Shir Barzel, Moshe Salhov, Ofir Lindenbaum +1
Modern cameras typically offer two types of image states: a minimally processed linear raw RGB image representing the raw sensor data, and a highly-processed non-linear image state…
Obtaining Favorable Layouts for Multiple Object Generation
Barak Battash, Amit Rozner, Lior Wolf +1
Large-scale text-to-image models that can generate high-quality and diverse images based on textual prompts have shown remarkable success. These models aim ultimately to create com…
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…
Gappy local conformal auto-encoders for heterogeneous data fusion: in praise of rigidity
Erez Peterfreund, Iryna Burak, Ofir Lindenbaum +4
Fusing measurements from multiple, heterogeneous, partial sources, observing a common object or process, poses challenges due to the increasing availability of numbers and types of…
Efficient Verification-Based Face Identification
Amit Rozner, Barak Battash, Ofir Lindenbaum +1
We study the problem of performing face verification with an efficient neural model . The efficiency of stems from simplifying the face verification problem from an embeddin…
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models
Guy Zamberg, Moshe Salhov, Ofir Lindenbaum +1
Tables are an abundant form of data with use cases across all scientific fields. Real-world datasets often contain anomalous samples that can negatively affect downstream analysis.…
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…
Anomaly Detection with Variance Stabilized Density Estimation
Amit Rozner, Barak Battash, Henry Li +2
We propose a modified density estimation problem that is highly effective for detecting anomalies in tabular data. Our approach assumes that the density function is relatively stab…
Multi-modal Differentiable Unsupervised Feature Selection
Junchen Yang, Ofir Lindenbaum, Yuval Kluger +1
Multi-modal high throughput biological data presents a great scientific opportunity and a significant computational challenge. In multi-modal measurements, every sample is observed…
Revisiting the Noise Model of Stochastic Gradient Descent
Barak Battash, Ofir Lindenbaum
The stochastic gradient noise (SGN) is a significant factor in the success of stochastic gradient descent (SGD). Following the central limit theorem, SGN was initially modeled as G…
Domain-Generalizable Multiple-Domain Clustering
Amit Rozner, Barak Battash, Lior Wolf +1
This work generalizes the problem of unsupervised domain generalization to the case in which no labeled samples are available (completely unsupervised). We are given unlabeled samp…
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…
Imbalanced Classification via a Tabular Translation GAN
Jonathan Gradstein, Moshe Salhov, Yoav Tulpan +2
When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class…