Yedid Hoshen

Yedid Hoshen

Associate Professor, School of Computer Science and Engineering, the Hebrew University of Jerusalem
Email: yedid.hoshen_at_mail.huji.ac.il
About Me

I am an Associate Professor at the School of Computer Science and Engineering, the Hebrew University of Jerusalem. I am also a visiting faculty rsearcher at Google.

Before joining HUJI, I was a Research Scientist at Facebook AI Research in New York And Tel Aviv. My current research interests include anomaly detection, learning disentangled representations and generative models. I completed my PhD at the Hebrew University of Jerusalem, supervised by Prof. Shmuel Peleg. Previously I studied Physics at the University of Oxford, traded options (volatility arbitrage) for a prop desk at KBC-FP, designed mission control algorithms for the Israeli Space Program and worked at Google for two summers (Google [x] and Google Research NYC).
Publications
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
D. Winter, M. Cohen, S. Fruchter, Y. Pritch, A. Rav-Acha, Y. Hoshen
ECCV 2024
project page   
From Zero to Hero: Cold-Start Anomaly Detection
T. Reiss, Y. Hoshen
Findings of the ACL 2024
paper   |   code
Recovering the Pre-Fine-Tuning Weights of Generative Models
E. Horwitz, J. Kahana, Y. Hoshen
ICML 2024
project page   |   code
Disentanglement of single-cell data with biolord
Z. Piran, N. Cohen, Y. Hoshen, M Nitzan
Nature Biotechnology 2024
paper   |   code
Anomaly Detection and Biomarkers Localization in Retinal Images
L. Tiosano, R. Abutbul, R. Lender, Y. Shwartz, I. Chowers, Y. Hoshen, J. Levy
Journal of Clinical Medicine 2024
paper
Detecting anomalous proteins using deep representations
T. Michael-Pitschaze, N. Cohen, D. Ofer, Y. Hoshen, M. Linial
NAR Genomics and Bioinformatics 2024
paper   |   code
Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels
T.S. Fuchs, Y. Hoshen
ICASSP 2023
paper   |   code
Red PANDA: Disambiguating Anomaly Detection by Removing Nuisance Factors
N. Cohen, J Kahana, Y. Hoshen
ICLR 2023
paper   
Mean-shifted contrastive loss for anomaly detection
T. Reiss, Y. Hoshen
AAAI 2023
paper   |   code
You Say Factorization Machine, I Say Neural Network-It's All in the Activation
C. Almagor, Y. Hoshen
RecSys 2022
paper   |   code
Unsupervised Word Segmentation using K Nearest Neighbors
T.S. Fuchs Y. Hoshen, J. Keshet
Interspeech 2022
paper   |   code
A Contrastive Objective for Learning Disentangled Representations
J. Kahana Y. Hoshen
ECCV 2022
paper   |   code
Anomaly Detection Requires Better Representations
T. Reiss, N. Cohen, E. Horwitz, R. Abutbul Y. Hoshen
ECCV 2022 Workshops
paper   
The inductive bias of in-context learning: Rethinking pretraining example design
Y. Levine, N. Wies, D. Jannai, D. Navon Y. Hoshen A. Shashua
ICLR 2022 (Spotlight Presentation)
paper   
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
A. Gabbay, N. Cohen Y. Hoshen
NeurIPS 2021
project page   
Scaling-up Disentanglement for Image Translation
A. Gabbay Y. Hoshen
ICCV 2021
project page   
Deep Single Image Manipulation
Y. Vinker*, E. Horwitz*, N. Zabari Y. Hoshen
ICCV 2021 (Oral Presentation)
project page   |   code
Reconstruction-Based Membership Inference Attacks are Easier on Difficult Problems
A. Shafran, S. Peleg Y. Hoshen
ICCV 2021
paper   
PANDA -- Adapting Pretrained Features for Anomaly Detection
T. Reiss*, N. Cohen*, L. Bergman Y. Hoshen
CVPR 2021
paper   |   code
Crypto-Oriented Neural Architecture Design
A. Shafran, G. Segev, S. Peleg Y. Hoshen
ICASSP 2021
paper   
Classification-Based Anomaly Detection for General Data
L. Bergman Y. Hoshen
ICLR 2020
paper   |   code
Demystifying Inter-Class Disentanglement
A. Gabbay Y. Hoshen
ICLR 2020
paper   |   code
Neural Separation of Observed and Unobserved Distributions
T. Halperin, A. Ephrat Y. Hoshen
ICML 2019
paper   |   code
Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors
Y. Hoshen, K. Li and J. Malik
CVPR 2019
paper   |   code
Towards Unsupervised Single-Channel Blind Source Separation
using Adversarial Pair Unmix-and-Remix

Y. Hoshen
ICASSP 2019
paper   
Non-Adversarial Mapping with VAEs
Y. Hoshen
NIPS 2018
paper   
Non-Adversarial Unsupervised Word Translation
Y. Hoshen, L. Wolf
EMNLP 2018
arXiv   |   code
NAM: Non-Adversarial Unsupervised Domain Mapping
Y. Hoshen, L. Wolf
ECCV 2018
arXiv   |   ICLR extended abstract   |   code
Unsupervised Correlation Analysis
Y. Hoshen L. Wolf
CVPR 2018
arXiv
Identifying Analogies Across Domains
Y. Hoshen L. Wolf
ICLR 2018
paper   
VAIN: Attentional Multi-agent Predictive Modeling
Y. Hoshen
NIPS 2017
arXiv   
An Egocentric Look at Video Photographer Identity
Y. Hoshen S. Peleg
CVPR 2016
arXiv   |   popular press
Visual Learning of Arithmetic Operations
Y. Hoshen S. Peleg
AAAI 2016
arXiv   |   code
Live Video Synopsis for Multiple Cameras
Y. Hoshen S. Peleg
ICIP 2015
paper   
Speech Acoustic Modeling From Raw Multichannel Waveforms
Y. Hoshen R.J. Weiss K.W. Wilson
ICASSP 2015
paper   
The Information in Temporal Histograms
Y. Hoshen S. Peleg
WACV 2015
paper   
Wisdom of the Crowd in Egocentric Video Curation
Y. Hoshen G. Ben-Artzi S. Peleg
CVPR 2014 - Workshop
paper   
Efficient Representation of Distributions for Background Subtraction
Y. Hoshen Y. Poleg C. Arora S. Peleg
AVSS 2013
paper   
Resume
My resume can be found here.