Rémi Monasson

Molecular Biology · 1995–2020

A researcher in the Science corpus: 9 papers; trusts 38 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 122 citations into it; #7,669 of 17,773 by trust here, #8,381 by citations
• Computational Biology & Bioinformatics — 76 citations into it
• Genetics — 32 citations into it
• Probabilistic & Bayesian Methods — 21 citations into it
• Artificial Intelligence — 20 citations into it
• Materials Chemistry — 18 citations into it
• Statistics and Probability — 15 citations into it
• Statistical and Nonlinear Physics — 12 citations into it; #2,832 of 10,127 by trust here, #3,100 by citations

Whom Rémi Monasson trusts overall

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

1. Geoffrey E. Hinton
2. Magnus Ekeberg
3. Kevin P. Murphy
4. Martin Weigt
5. David John Cameron MacKay
6. B. Reva
7. Ruslan Salakhutdinov
8. Simona Cocco
9. Vinod Nair
10. Individualized Treat

Who trusts Rémi Monasson overall

1. Stefano Spigler
2. Madhu S. Advani
3. Alia Abbara
4. Benjamin Aubin
5. Mario Geiger
6. H. C. Nguyen
7. Magnus Ekeberg
8. Manfred Opper
9. Subhaneil Lahiri
10. Riccardo Zecchina

Papers from Rémi Monasson's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. GENERATIVE ADVERSARIAL NETS
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Rectified Linear Units Improve Restricted Boltzmann Machines
9. Long Short-Term Memory
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning protein constitutive motifs from sequence data (2018) — 115 citations
• An evolution-based model for designing chorismate mutase enzymes (2020) — 297 citations
• Inverse statistical physics of protein sequences: a key issues review (2017) — 242 citations
• Large pseudocounts and L2-norm penalties are necessary for the mean-field inference of Ising and Potts models. (2014) — 23 citations
• From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction (2012) — 109 citations
• High-dimensional inference with the generalized Hopfield model: principal component analysis and corrections. (2011) — 33 citations
• Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods (2009) — 187 citations
• Small-correlation expansions for the inverse Ising problem (2008) — 47 citations
• Weight space structure and internal representations: A direct approach to learning and generalization in multilayer neural networks. (1995)