Ofir Lindenbaum | DeepAI

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∙ 07/23/2023

TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models

Tables are an abundant form of data with use cases across all scientific...

0 Guy Zamberg, et al. ∙

∙ 06/07/2023

Interpretable Deep Clustering

Clustering is a fundamental learning task widely used as a first step in...

0 Jonathan Svirsky, et al. ∙

∙ 06/01/2023

Anomaly Detection with Variance Stabilized Density Estimation

Density estimation based anomaly detection schemes typically model anoma...

0 Amit Rozner, et al. ∙

∙ 06/01/2023

Neuronal Cell Type Classification using Deep Learning

The brain is likely the most complex organ, given the variety of functio...

0 Ofek Ophir, et al. ∙

∙ 03/16/2023

Multi-modal Differentiable Unsupervised Feature Selection

Multi-modal high throughput biological data presents a great scientific ...

0 Junchen Yang, et al. ∙

∙ 03/05/2023

Revisiting the Noise Model of Stochastic Gradient Descent

The stochastic gradient noise (SGN) is a significant factor in the succe...

0 Barak Battash, et al. ∙

∙ 01/31/2023

Domain-Generalizable Multiple-Domain Clustering

Accurately clustering high-dimensional measurements is vital for adequat...

0 Amit Rozner, et al. ∙

∙ 01/01/2023

Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction

Classical methods for acoustic scene mapping require the estimation of t...

0 Idan Cohen, et al. ∙

∙ 10/28/2022

SG-VAD: Stochastic Gates Based Speech Activity Detection

We propose a novel voice activity detection (VAD) model in a low-resourc...

0 Jonathan Svirsky, et al. ∙

∙ 04/19/2022

Imbalanced Classification via a Tabular Translation GAN

When presented with a binary classification problem where the data exhib...

0 Jonathan Gradstein, et al. ∙

∙ 10/29/2021

Support Recovery with Stochastic Gates: Theory and Application for Linear Models

We analyze the problem of simultaneous support recovery and estimation o...

0 Soham Jana, et al. ∙

∙ 10/11/2021

Deep Unsupervised Feature Selection by Discarding Nuisance and Correlated Features

Modern datasets often contain large subsets of correlated features and n...

0 Uri Shaham, et al. ∙

∙ 10/01/2021

Probabilistic Robust Autoencoders for Anomaly Detection

Empirical observations often consist of anomalies (or outliers) that con...

0 Yariv Aizenbud, et al. ∙

∙ 06/11/2021

Locally Sparse Networks for Interpretable Predictions

Despite the enormous success of neural networks, they are still hard to ...

0 Junchen Yang, et al. ∙

∙ 03/19/2021

Refined Least Squares for Support Recovery

We study the problem of exact support recovery based on noisy observatio...

0 Ofir Lindenbaum, et al. ∙

∙ 10/12/2020

Deep Gated Canonical Correlation Analysis

Canonical Correlation Analysis (CCA) models can extract informative corr...

5 Ofir Lindenbaum, et al. ∙

∙ 09/09/2020

Kernel-based parameter estimation of dynamical systems with unknown observation functions

A low-dimensional dynamical system is observed in an experiment as a hig...

7 Ofir Lindenbaum, et al. ∙

∙ 07/09/2020

Let the Data Choose its Features: Differentiable Unsupervised Feature Selection

Scientific observations often consist of a large number of variables (fe...

7 Ofir Lindenbaum, et al. ∙

∙ 04/15/2020

LOCA: LOcal Conformal Autoencoder for standardized data coordinates

We propose a deep-learning based method for obtaining standardized data ...

10 Erez Peterfreund, et al. ∙

∙ 03/16/2020

Randomly Aggregated Least Squares for Support Recovery

We study the problem of exact support recovery: given an (unknown) vecto...

0 Ofir Lindenbaum, et al. ∙

∙ 02/27/2020

The Spectral Underpinning of word2vec

word2vec due to Mikolov et al. (2013) is a word embedding method that is...

0 Ariel Jaffe, et al. ∙

∙ 10/09/2018

Deep supervised feature selection using Stochastic Gates

In this study, we propose a novel non-parametric embedded feature select...

0 Yutaro Yamada, et al. ∙

∙ 02/14/2018

Geometry-Based Data Generation

Many generative models attempt to replicate the density of their input d...

0 Ofir Lindenbaum, et al. ∙

∙ 06/28/2016

Multi-View Kernel Consensus For Data Analysis and Signal Processing

The input data features set for many data driven tasks is high-dimension...

0 Moshe Salhov, et al. ∙

∙ 08/23/2015

MultiView Diffusion Maps

In this study we consider learning a reduced dimensionality representati...

0 Ofir Lindenbaum, et al. ∙

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