Richard Nock

Artificial Intelligence · 2004–2020

A researcher in the Science corpus: 18 papers; trusts 484 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 554 citations into it; #5,519 of 35,345 by trust here, #1,954 by citations
Privacy & Security — 219 citations into it; #601 of 709 by trust here, #342 by citations
Computer Vision and Pattern Recognition — 146 citations into it; #5,519 of 21,575 by trust here, #5,565 by citations
Optimization & Training — 136 citations into it
Statistics and Probability — 59 citations into it; #3,854 of 12,932 by trust here, #3,801 by citations
Representation & Self-Supervised Learning — 58 citations into it; #2,677 of 3,145 by trust here, #2,404 by citations
Computational Theory and Mathematics — 52 citations into it
Management Science and Operations Research — 43 citations into it

Whom Richard Nock trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Richard Nock overall

Papers from Richard Nock's perspective

Ordered by how strongly the researchers Richard Nock 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)
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach (2016) — 1,704 citations
Advances and Open Problems in Federated Learning (2019) — 9,019 citations
Statistical region merging (2004) — 902 citations
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption (2017) — 622 citations
A closed-form expression for the Sharma–Mittal entropy of exponential families (2011) — 107 citations
Fast Learning from Distributed Datasets without Entity Matching (2016) — 12 citations
On the chi square and higher-order chi distances for approximating f-divergences (2013) — 136 citations
Loss factorization, weakly supervised learning and label noise robustness (2016) — 120 citations
Entity Resolution and Federated Learning get a Federated Resolution (2018) — 95 citations
K-variates++: More Pluses in the K-means++ (2016) — 24 citations
Lipschitz Networks and Distributional Robustness (2018) — 12 citations
On Modulating the Gradient for Meta-learning (2020) — 57 citations
Adaptive Subspaces for Few-Shot Learning (2020) — 425 citations
On Regularizing Rademacher Observation Losses (2016) — 3 citations
Rademacher Observations, Private Data, and Boosting (2015) — 13 citations
Learning Games and Rademacher Observations Losses (2015)
(Almost) No Label No Cry (2014) — 122 citations
Bregman Divergences and Surrogates for Learning (2009) — 72 citations