Dan Feldman
Artificial Intelligence · 2009–2020
A researcher in the Science corpus: 12 papers; trusts 429 researchers and is trusted by 51 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 159 citations into it; #6,909 of 35,345 by trust here, #8,007 by citations
- Computer Vision and Pattern Recognition — 131 citations into it; #4,699 of 21,575 by trust here, #8,831 by citations
- Efficiency & Compression — 79 citations into it; #515 of 1,264 by trust here, #899 by citations
- Signal Processing — 43 citations into it
- Computational Theory and Mathematics — 32 citations into it; #667 of 12,182 by trust here, #1,027 by citations
- Computational Mechanics — 26 citations into it; #765 of 10,001 by trust here, #1,671 by citations
- Scaling & Learning Theory — 22 citations into it
- Computer Graphics and Computer-Aided Design — 20 citations into it
Whom Dan Feldman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sariel Har-Peled
- Michael Langberg
- Alex Krizhevsky
- Kenneth L. Clarkson
- Pankaj Kumar Agarwal
- Michael W. Mahoney
- S. Muthu Muthukrishnan
- Geoffrey E. Hinton
- Yann LeCun
- Cynthia Dwork
Who trusts Dan Feldman overall
- Mario Lucic
- David P. Woodruff
- Trevor Campbell
- Olivier Bachem
- Andreas Krause
- Artem Barger
- Matthew Faulkner
- Ben Mussay
- Tamara Broderick
- Yingyu Liang
Papers from Dan Feldman's perspective
Ordered by how strongly the researchers Dan Feldman 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
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- Data-Independent Neural Pruning via Coresets (2019) — 67 citations
- Training Gaussian Mixture Models at Scale via Coresets (2017) — 114 citations
- Private coresets (2009) — 129 citations
- Turning big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering (2013) — 591 citations
- A unified framework for approximating and clustering data (2011) — 505 citations
- k-Means for Streaming and Distributed Big Sparse Data (2015) — 32 citations
- Data-Independent Structured Pruning of Neural Networks via Coresets (2020) — 28 citations
- Core‐sets: An updated survey (2019) — 135 citations
- Provable Filter Pruning for Efficient Neural Networks (2019) — 167 citations
- Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds (2018) — 86 citations
- Training Mixture Models at Scale via Coresets (2017) — 30 citations
- Scalable Training of Mixture Models via Coresets (2011) — 170 citations