Liva Ralaivola

Statistical Learning Theory · 2001–2012

A researcher in the Science corpus: 10 papers; trusts 57 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 41 citations into it
• Artificial Intelligence — 40 citations into it; #8,898 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #6,899 of 21,575 by trust here, #13,815 by citations
• Molecular Biology — 16 citations into it; #9,611 of 17,773 by trust here, #6,817 by citations
• Computational Theory and Mathematics — 15 citations into it; #7,283 of 12,182 by trust here, #1,500 by citations
• Optimization & Training — 11 citations into it; #1,292 of 2,601 by trust here, #2,052 by citations
• Probabilistic & Bayesian Methods — 10 citations into it
• Statistics and Probability — 9 citations into it

Whom Liva Ralaivola trusts overall

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

1. Christopher K. I. Williams
2. Leo Breiman
3. L. Eon Bottou
4. Vladimir Naumovich Vapnik
5. David A. McAllester
6. Pierre Baldi
7. Bernhard Schölkopf
8. David E. Heckerman
9. Yann LeCun
10. Peter L. Bartlett

Who trusts Liva Ralaivola overall

1. Anastasia Pentina
2. Bryan Lim
3. Kaspar Riesen
4. Nils M. Kriege
5. Georg Hinselmann
6. Céline Brouard
7. Yudong Cao
8. Federico Errica
9. Yevgeny Seldin
10. Benoît Frénay

Papers from Liva Ralaivola's perspective

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

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

Their papers

• Learning SVMs from Sloppily Labeled Data (2009) — 73 citations
• Confusion Matrix Stability Bounds for Multiclass Classification (2012) — 31 citations
• Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity (2005) — 206 citations
• Graph kernels for chemical informatics (2005) — 513 citations
• One- to Four-Dimensional Kernels for Virtual Screening and the Prediction of Physical, Chemical, and Biological Properties (2007) — 63 citations
• Gene networks inference using dynamic Bayesian networks (2003) — 501 citations
• Chromatic PAC-Bayes Bounds for Non-IID Data (2009) — 76 citations
• Learning Kernel Perceptrons on Noisy Data Using Random Projections (2007) — 14 citations
• Time series filtering, smoothing and learning using the kernel Kalman filter (2005) — 63 citations
• Incremental Support Vector Machine Learning: A Local Approach (2001) — 128 citations