| Entropy-sgd: Biasing gradient descent into wide valleys P Chaudhari, A Choromanska, S Soatto, Y LeCun, C Baldassi, C Borgs, ... Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124018, 2019 | 710 | 2019 |
| Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes C Baldassi, C Borgs, JT Chayes, A Ingrosso, C Lucibello, L Saglietti, ... Proceedings of the National Academy of Sciences 113 (48), E7655-E7662, 2016 | 179 | 2016 |
| Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners C Baldassi, M Zamparo, C Feinauer, A Procaccini, R Zecchina, M Weigt, ... PloS one 9 (3), e92721, 2014 | 168 | 2014 |
| Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses C Baldassi, A Ingrosso, C Lucibello, L Saglietti, R Zecchina Physical review letters 115 (12), 128101, 2015 | 142 | 2015 |
| Simultaneous identification of specifically interacting paralogs and interprotein contacts by direct coupling analysis T Gueudré, C Baldassi, M Zamparo, M Weigt, A Pagnani Proceedings of the National Academy of Sciences 113 (43), 12186-12191, 2016 | 118 | 2016 |
| Shape similarity, better than semantic membership, accounts for the structure of visual object representations in a population of monkey inferotemporal neurons C Baldassi, A Alemi-Neissi, M Pagan, JJ DiCarlo, R Zecchina, D Zoccolan PLoS computational biology 9 (8), e1003167, 2013 | 118 | 2013 |
| Efficient supervised learning in networks with binary synapses C Baldassi, A Braunstein, N Brunel, R Zecchina Proceedings of the National Academy of Sciences 104 (26), 11079 -11084, 2007 | 117 | 2007 |
| Shaping the learning landscape in neural networks around wide flat minima C Baldassi, F Pittorino, R Zecchina Proceedings of the National Academy of Sciences 117 (1), 161-170, 2020 | 66 | 2020 |
| Local entropy as a measure for sampling solutions in constraint satisfaction problems C Baldassi, A Ingrosso, C Lucibello, L Saglietti, R Zecchina Journal of Statistical Mechanics: Theory and Experiment 2016 (2), 023301, 2016 | 58 | 2016 |
| Efficiency of quantum vs. classical annealing in nonconvex learning problems C Baldassi, R Zecchina Proceedings of the National Academy of Sciences 115 (7), 1457-1462, 2018 | 54 | 2018 |
| Properties of the geometry of solutions and capacity of multilayer neural networks with rectified linear unit activations C Baldassi, EM Malatesta, R Zecchina Physical review letters 123 (17), 170602, 2019 | 42 | 2019 |
| RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells C Bosia, F Sgrò, L Conti, C Baldassi, D Brusa, F Cavallo, FD Cunto, ... Genome biology 18 (1), 1-14, 2017 | 42 | 2017 |
| A behavioral characterization of the drift diffusion model and its multialternative extension for choice under time pressure C Baldassi, S Cerreia-Vioglio, F Maccheroni, M Marinacci, M Pirazzini Management Science 66 (11), 5075-5093, 2020 | 37 | 2020 |
| Learning may need only a few bits of synaptic precision C Baldassi, F Gerace, C Lucibello, L Saglietti, R Zecchina Physical Review E 93 (5), 052313, 2016 | 29 | 2016 |
| Role of synaptic stochasticity in training low-precision neural networks C Baldassi, F Gerace, HJ Kappen, C Lucibello, L Saglietti, E Tartaglione, ... Physical review letters 120 (26), 268103, 2018 | 26 | 2018 |
| Parle: parallelizing stochastic gradient descent P Chaudhari, C Baldassi, R Zecchina, S Soatto, A Talwalkar, A Oberman arXiv preprint arXiv:1707.00424, 2017 | 25 | 2017 |
| A max-sum algorithm for training discrete neural networks C Baldassi, A Braunstein Journal of Statistical Mechanics: Theory and Experiment 2015 (8), P08008, 2015 | 24 | 2015 |
| Generalization learning in a perceptron with binary synapses C Baldassi Journal of Statistical Physics 136 (5), 902-916, 2009 | 24 | 2009 |
| Entropic gradient descent algorithms and wide flat minima F Pittorino, C Lucibello, C Feinauer, G Perugini, C Baldassi, ... Journal of Statistical Mechanics: Theory and Experiment 2021 (12), 124015, 2021 | 23 | 2021 |
| A three-threshold learning rule approaches the maximal capacity of recurrent neural networks A Alemi, C Baldassi, N Brunel, R Zecchina PLoS computational biology 11 (8), e1004439, 2015 | 23 | 2015 |
Riccardo Zecchinaprofessor, theoretical physics, Bocconi UniversityZweryfikowany adres z unibocconi.it
