Rocco A. Servedio

Artificial Intelligence · 2001–2013

A researcher in the Science corpus: 13 papers; trusts 52 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #2,939 of 35,345 by trust here, #5,533 by citations
• Optimization & Training — 23 citations into it
• Statistical Learning Theory — 21 citations into it; #499 of 1,038 by trust here, #519 by citations
• Computational Theory and Mathematics — 15 citations into it; #1,682 of 12,182 by trust here, #1,851 by citations
• Algorithms & Complexity — 7 citations into it
• Scaling & Learning Theory — 7 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Statistics and Probability — 6 citations into it

Whom Rocco A. Servedio trusts overall

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

1. Yoav Freund
2. Leslie G. Valiant
3. Robert E. Schapire
4. Thomas G. Dietterich
5. Nick Littlestone
6. Avrim Blum
7. Michael Kearns
8. Jeffrey C. Jackson
9. Adam R. Klivans
10. Yoshua Bengio

Who trusts Rocco A. Servedio overall

1. Philip M. Long
2. Adam R. Klivans
3. Vitaly Feldman
4. R. Meir
5. Yunlong Gao
6. Ilias Diakonikolas
7. Tong Wang
8. Gunnar Rätsch
9. Feng Gao
10. Cynthia Rudin

Papers from Rocco A. Servedio's perspective

Ordered by how strongly the researchers Rocco A. Servedio trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Halfspaces with Malicious Noise (2009) — 145 citations
• Random classification noise defeats all convex potential boosters (2008) — 295 citations
• Agnostically learning halfspaces (2005) — 366 citations
• Boosting and Hard-Core Set Construction (2003) — 59 citations
• New degree bounds for polynomial threshold functions (2003) — 83 citations
• Learning DNF in time 2Õ(n1/3) (2004) — 176 citations
• Consistency versus Realizable H-Consistency for Multiclass Classification (2013) — 58 citations
• Hardness results for agnostically learning low-degree polynomial threshold functions (2010) — 26 citations
• Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate (2010) — 87 citations
• Toward Attribute Efficient Learning of Decision Lists and Parities (2006) — 83 citations
• Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions (2006) — 17 citations
• Learning DNF in time 2 Õ(n 1/3 ) . (2001) — 170 citations
• Smooth Boosting and Learning with Malicious Noise (2001) — 213 citations