Daphna Weinshall | DeepAI

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Featured Co-authors

Omri Abend
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∙ 08/27/2023

Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario

Semi-Supervised Learning (SSL) leverages both labeled and unlabeled data...

0 Noam Fluss, et al. ∙

∙ 08/27/2023

Pruning the Unlabeled Data to Improve Semi-Supervised Learning

In the domain of semi-supervised learning (SSL), the conventional approa...

0 Guy Hacohen, et al. ∙

∙ 06/06/2023

How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget

In Active Learning (AL), a learner actively chooses which unlabeled exam...

0 Guy Hacohen, et al. ∙

∙ 10/02/2022

The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels

Deep neural networks have incredible capacity and expressibility, and ca...

0 Daniel Shwartz, et al. ∙

∙ 05/23/2022

Active Learning Through a Covering Lens

Deep active learning aims to reduce the annotation cost for deep neural ...

0 Ofer Yehuda, et al. ∙

∙ 02/06/2022

Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets

Investigating active learning, we focus on the relation between the numb...

0 Guy Hacohen, et al. ∙

∙ 09/13/2021

The Grammar-Learning Trajectories of Neural Language Models

The learning trajectories of linguistic phenomena provide insight into t...

1 Leshem Choshen, et al. ∙

∙ 05/12/2021

Convergence Analysis of Over-parameterized Deep Linear Networks, and the Principal Components Bias

Convolutional Neural networks of different architectures seem to learn t...

0 Guy Hacohen, et al. ∙

∙ 02/09/2021

More Is More – Narrowing the Generalization Gap by Adding Classification Heads

Overfit is a fundamental problem in machine learning in general, and in ...

0 Roee Cates, et al. ∙

∙ 12/01/2020

Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering

Recently, Semi-Supervised Learning (SSL) has shown much promise in lever...

0 Boaz Lerner, et al. ∙

∙ 11/25/2020

Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample

The generation of synthetic images is currently being dominated by Gener...

51 Itamar Winter, et al. ∙

∙ 03/31/2020

Learning from Small Data Through Sampling an Implicit Conditional Generative Latent Optimization Model

We revisit the long-standing problem of learning from small sample. In r...

0 Idan Azuri, et al. ∙

∙ 12/05/2019

Multi-Modal Deep Clustering: Unsupervised Partitioning of Images

The clustering of unlabeled raw images is a daunting task, which has rec...

0 Guy Shiran, et al. ∙

∙ 05/26/2019

All Neural Networks are Created Equal

One of the unresolved questions in the context of deep learning is the t...

0 Guy Hacohen, et al. ∙

∙ 04/07/2019

On The Power of Curriculum Learning in Training Deep Networks

Training neural networks is traditionally done by providing a sequence o...

0 Guy Hacohen, et al. ∙

∙ 01/30/2019

Blurred Images Lead to Bad Local Minima

Blurred Images Lead to Bad Local Minima...

0 Gal Katzhendler, et al. ∙

∙ 12/09/2018

Theory of Curriculum Learning, with Convex Loss Functions

Curriculum Learning - the idea of teaching by gradually exposing the lea...

0 Daphna Weinshall, et al. ∙

∙ 08/30/2018

Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images

Generative Adversarial Networks (GANs) have been shown to produce realis...

0 Matan Ben-Yosef, et al. ∙

∙ 02/11/2018

Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks

Our first contribution in this paper is a theoretical investigation of c...

0 Daphna Weinshall, et al. ∙

∙ 09/28/2017

Distance-based Confidence Score for Neural Network Classifiers

The reliable measurement of confidence in classifiers' predictions is ve...

0 Amit Mandelbaum, et al. ∙

∙ 04/20/2017

Every Untrue Label is Untrue in its Own Way: Controlling Error Type with the Log Bilinear Loss

Deep learning has become the method of choice in many application domain...

0 Yehezkel S. Resheff, et al. ∙

∙ 04/21/2016

Novelty Detection in MultiClass Scenarios with Incomplete Set of Class Labels

We address the problem of novelty detection in multiclass scenarios wher...

0 Nomi Vinokurov, et al. ∙

∙ 11/17/2015

Optimized Linear Imputation

Often in real-world datasets, especially in high dimensional data, some ...

0 Yehezkel S. Resheff, et al. ∙

∙ 01/27/2015

A Cheap System for Vehicle Speed Detection

The reliable detection of speed of moving vehicles is considered key to ...

0 Chaim Ginzburg, et al. ∙

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