Daphna Weinshall

Daphna Weinshall

Hebrew University of Jerusalem

H-index: 52

Asia-Israel

About Daphna Weinshall

Daphna Weinshall, With an exceptional h-index of 52 and a recent h-index of 25 (since 2020), a distinguished researcher at Hebrew University of Jerusalem, specializes in the field of computer vision, machine learning, visual perception.

His recent articles reflect a diverse array of research interests and contributions to the field:

Semi-supervised learning in the few-shot zero-shot scenario

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

Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs

United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit

Pruning the unlabeled data to improve semi-supervised learning

Principal components bias in over-parameterized linear models, and its manifestation in deep neural networks

The grammar-learning trajectories of neural language models

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

Daphna Weinshall Information

University

Position

Professor of Computer Science

Citations(all)

10288

Citations(since 2020)

2405

Cited By

8688

hIndex(all)

52

hIndex(since 2020)

25

i10Index(all)

114

i10Index(since 2020)

48

Email

University Profile Page

Hebrew University of Jerusalem

Google Scholar

View Google Scholar Profile

Daphna Weinshall Skills & Research Interests

computer vision

machine learning

visual perception

Top articles of Daphna Weinshall

Title

Journal

Author(s)

Publication Date

Semi-supervised learning in the few-shot zero-shot scenario

arXiv preprint arXiv:2308.14119

Noam Fluss

Guy Hacohen

Daphna Weinshall

2023/8/27

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

Guy Hacohen

Daphna Weinshall

2023/6

Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs

arXiv preprint arXiv:2310.11094

Uri Stern

Daphna Weinshall

2023/10/17

United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit

Uri Stern

Daniel Shwartz

Daphna Weinshall

2023/10/17

Pruning the unlabeled data to improve semi-supervised learning

arXiv preprint arXiv:2308.14058

Guy Hacohen

Daphna Weinshall

2023/8/27

Principal components bias in over-parameterized linear models, and its manifestation in deep neural networks

Journal of Machine Learning Research

Guy Hacohen

Daphna Weinshall

2022

The grammar-learning trajectories of neural language models

arXiv preprint arXiv:2109.06096

Leshem Choshen

Guy Hacohen

Daphna Weinshall

Omri Abend

2021/9/13

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

arXiv preprint arXiv:2210.00583

Daniel Shwartz

Uri Stern

Daphna Weinshall

2022/10/2

Active learning on a budget: Opposite strategies suit high and low budgets

Guy Hacohen

Avihu Dekel

Daphna Weinshall

2022/7

Active Learning Through a Covering Lens

Advances in Neural Information Processing Systems (NeurIPS)

Ofer Yehuda

Avihu Dekel

Guy Hacohen

Daphna Weinshall

2022

Multi-modal deep clustering: Unsupervised partitioning of images

Guy Shiran

Daphna Weinshall

2021/1/10

Learning from small data through sampling an implicit conditional generative latent optimization model

IEEE 2020 25th International Conference on Pattern Recognition (ICPR).

Idan Azuri

Daphna Weinshall

2020/3/31

Theory of curriculum learning, with convex loss functions

Journal of Machine Learning Research

Daphna Weinshall

Dan Amir

2020

Boosting the performance of semi-supervised learning with unsupervised clustering

arXiv preprint arXiv:2012.00504

Boaz Lerner

Guy Shiran

Daphna Weinshall

2020/12/1

Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets

Guy Hacohen

Leshem Chosen

Daphna Weinshall

2020/7

See List of Professors in Daphna Weinshall University(Hebrew University of Jerusalem)