Daphna Weinshall
Computer Vision and Pattern Recognition · 1998–2019
A researcher in the Science corpus: 9 papers; trusts 402 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 56 citations into it; #11,692 of 21,575 by trust here, #13,269 by citations
- Artificial Intelligence — 52 citations into it; #11,421 of 35,345 by trust here, #14,718 by citations
- Representation & Self-Supervised Learning — 27 citations into it
- Generative Models — 25 citations into it
- Optimization & Training — 21 citations into it; #943 of 2,601 by trust here, #1,986 by citations
- Natural Language Processing — 13 citations into it; #4,310 of 5,688 by trust here, #5,593 by citations
- Evaluation & Benchmarks — 8 citations into it
- Model Architectures — 8 citations into it
Whom Daphna Weinshall trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yann LeCun
- Alex Graves
- Ilya Sutskever
- Kaiming He
- Karen Simonyan
- Christian Szegedy
Who trusts Daphna Weinshall overall
- Yu Zhang
- Lei Han
- Yajing Kong
- Yu Zhang
- Guy Hacohen
- Xin Wang
- Yu Zhang
- Petru Soviany
- Eduardo Mosqueira-Rey
- Konstantina Lazaridou
Papers from Daphna Weinshall's perspective
Ordered by how strongly the researchers Daphna Weinshall trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Class Representation and Image Retrieval with Non-Metric Distances (1998)
- Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images (2018) — 59 citations
- Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks (2018) — 295 citations
- On The Power of Curriculum Learning in Training Deep Networks (2019) — 578 citations
- Coming to Your Senses: on Controls and Evaluation Sets in Polysemy Research (2018) — 15 citations
- Hierarchical Regularization Cascade for Joint Learning (2013) — 50 citations
- Learning a Mahalanobis Metric from Equivalence Constraints (2005) — 589 citations
- Learning Distance Functions using Equivalence Relations (2003) — 510 citations
- Computing Gaussian Mixture Models with EM Using Equivalence Constraints (2003) — 326 citations