Carlo Baldassi

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 10 papers; trusts 52 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #10,604 of 35,345 by trust here, #4,137 by citations
• Optimization & Training — 63 citations into it; #2,034 of 2,601 by trust here, #560 by citations
• Molecular Biology — 60 citations into it; #11,524 of 17,773 by trust here, #10,347 by citations
• Computer Vision and Pattern Recognition — 56 citations into it; #11,251 of 21,575 by trust here, #4,030 by citations
• Computational Biology & Bioinformatics — 33 citations into it; #572 of 660 by trust here, #558 by citations
• Model Architectures — 21 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it
• Scaling & Learning Theory — 18 citations into it; #390 of 428 by trust here, #316 by citations

Whom Carlo Baldassi trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Alessandro Ingrosso
6. David John Cameron MacKay
7. M. M'ezard
8. L. Eon Bottou
9. Max Welling
10. Carlo Lucibello

Who trusts Carlo Baldassi overall

1. Pratik Chaudhari
2. Anne-Florence Bitbol
3. Gintare Karolina Dziugaite
4. Riccardo Zecchina
5. Fabrizio Pittorino
6. Stefano Soatto
7. Robin Pearce
8. Charles H. Martin
9. Levent Sagun
10. Jianyu Wang

Papers from Carlo Baldassi's perspective

Ordered by how strongly the researchers Carlo Baldassi 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Entropy-SGD: biasing gradient descent into wide valleys (2016) — 882 citations
• Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses. (2015) — 152 citations
• Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes (2016) — 183 citations
• Learning may need only a few bits of synaptic precision. (2016) — 29 citations
• Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners (2014) — 137 citations
• Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis (2016) — 117 citations
• Local entropy as a measure for sampling solutions in constraint satisfaction problems (2015) — 71 citations
• Entropic gradient descent algorithms and wide flat minima (2020) — 40 citations
• Shaping the learning landscape in neural networks around wide flat minima (2019) — 102 citations
• Parle: parallelizing stochastic gradient descent (2017) — 23 citations