Andrea Tacchetti
Andrea Tacchetti
Research Scientist - DeepMind
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
11332018
Visual interaction networks: Learning a physics simulator from video
N Watters, D Zoran, T Weber, P Battaglia, R Pascanu, A Tacchetti
Advances in neural information processing systems 30, 4539-4547, 2017
1442017
Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?
F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio
Center for Brains, Minds and Machines (CBMM), arXiv, 2014
96*2014
Visual interaction networks
N Watters, A Tacchetti, T Weber, R Pascanu, P Battaglia, D Zoran
arXiv preprint arXiv:1706.01433, 2017
882017
Unsupervised learning of invariant representations in hierarchical architectures
F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio
arXiv preprint arXiv:1311.4158, 2013
752013
Unsupervised learning of invariant representations
F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio
Theoretical Computer Science 633, 112-121, 2016
712016
GURLS: A Least Squares Library for Supervised Learning
A Tacchetti, P Mallapragada, M Santoro, R Rosasco
Journal of Machine Learning Research 14, 3201-3205, 2013
602013
The computational magic of the ventral stream: sketch of a theory (and why some deep architectures work).
T Poggio, J Mutch, J Leibo, L Rosasco, A Tacchetti
382013
Relational forward models for multi-agent learning
A Tacchetti, HF Song, PAM Mediano, V Zambaldi, NC Rabinowitz, ...
arXiv preprint arXiv:1809.11044, 2018
342018
GURLS: a toolbox for large scale multiclass learning
A Tacchetti, P Mallapragada, M Santoro, L Rosasco
NIPS 2011 workshop on parallel and large-scale machine learning. http://cbcl …, 2011
27*2011
Fast, invariant representation for human action in the visual system
L Isik, A Tacchetti, T Poggio
Journal of Neurophysiology 119 (2), 631-640, 2018
252018
Magic materials: a theory of deep hierarchical architectures for learning sensory representations
F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio
CBCL paper, 16, 2013
232013
Invariant recognition shapes neural representations of visual input
A Tacchetti, L Isik, TA Poggio
Annual review of vision science 4, 403-422, 2018
132018
Relational inductive biases, deep learning, and graph networks.(2018)
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 1806
131806
Invariant recognition drives neural representations of action sequences
A Tacchetti, L Isik, T Poggio
PLoS computational biology 13 (12), e1005859, 2017
122017
Regularization by early stopping for online learning algorithms
L Rosasco, A Tacchetti, S Villa
stat 1050, 30, 2014
122014
A neural architecture for designing truthful and efficient auctions
A Tacchetti, DJ Strouse, M Garnelo, T Graepel, Y Bachrach
arXiv preprint arXiv:1907.05181, 2019
72019
Does invariant recognition predict tuning of neurons in sensory cortex?
T Poggio, J Mutch, F Anselmi, A Tacchetti, L Rosasco, JZ Leibo
72013
Learning to play no-press diplomacy with best response policy iteration
T Anthony, T Eccles, A Tacchetti, J Kramár, I Gemp, TC Hudson, N Porcel, ...
arXiv preprint arXiv:2006.04635, 2020
62020
Spatio-temporal convolutional neural networks explain human neural representations of action recognition
A Tacchetti, L Isik, T Poggio
arXiv preprint arXiv:1606.04698 2, 2016
62016
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Articles 1–20