327 citations · 726 across the 29 of their papers we have counts for
13 papers · 1 filter
Anomaly Detection Requires Better Representations
Tal Reiss, Niv Cohen, Eliahu Horwitz +2
Anomaly detection seeks to identify unusual phenomena, a central task in science and industry. The task is inherently unsupervised as anomalies are unexpected and unknown during tr…
Time Series Anomaly Detection by Cumulative Radon Features
Yedid Hoshen
Detecting anomalous time series is key for scientific, medical and industrial tasks, but is challenging due to its inherent unsupervised nature. In recent years, progress has been…
Membership Inference Attacks are Easier on Difficult Problems
Avital Shafran, Shmuel Peleg, Yedid Hoshen
Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e.g. to detect copyright abuses. We show that models with higher dimensi…
Classification-Based Anomaly Detection for General Data
Liron Bergman, Yedid Hoshen
Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-b…
Deep Nearest Neighbor Anomaly Detection
Liron Bergman, Niv Cohen, Yedid Hoshen
Nearest neighbors is a successful and long-standing technique for anomaly detection. Significant progress has been recently achieved by self-supervised deep methods (e.g. RotNet).…
Crypto-Oriented Neural Architecture Design
Avital Shafran, Gil Segev, Shmuel Peleg +1
As neural networks revolutionize many applications, significant privacy conflicts between model users and providers emerge. The cryptography community developed a variety of techni…
Demystifying Inter-Class Disentanglement
Aviv Gabbay, Yedid Hoshen
Learning to disentangle the hidden factors of variations within a set of observations is a key task for artificial intelligence. We present a unified formulation for class and cont…
Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors
Yedid Hoshen, Jitendra Malik
Unconditional image generation has recently been dominated by generative adversarial networks (GANs). GAN methods train a generator which regresses images from random noise vectors…
Neural separation of observed and unobserved distributions
Tavi Halperin, Ariel Ephrat, Yedid Hoshen
Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source…
NAM: Non-Adversarial Unsupervised Domain Mapping
Yedid Hoshen, Lior Wolf
Several methods were recently proposed for the task of translating images between domains without prior knowledge in the form of correspondences. The existing methods apply adversa…
Non-Adversarial Unsupervised Word Translation
Yedid Hoshen, Lior Wolf
Unsupervised word translation from non-parallel inter-lingual corpora has attracted much research interest. Very recently, neural network methods trained with adversarial loss func…
VAIN: Attentional Multi-agent Predictive Modeling
Yedid Hoshen
Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of mo…
Visual Learning of Arithmetic Operations
Yedid Hoshen, Shmuel Peleg
A simple Neural Network model is presented for end-to-end visual learning of arithmetic operations from pictures of numbers. The input consists of two pictures, each showing a 7-di…