Andrea Tacchetti
Andrea Tacchetti
Research Scientist - DeepMind
Verified email at google.com - Homepage
TitleCited byYear
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
3452018
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, 4539-4547, 2017
1082017
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
662013
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
542013
Unsupervised learning of invariant representations
F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio
Theoretical Computer Science 633, 112-121, 2016
492016
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
352013
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
262014
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
242011
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, 2013
202013
Fast, invariant representation for human action in the visual system
L Isik, A Tacchetti, T Poggio
Journal of Neurophysiology 119 (2), 631-640, 2018
152018
Regularization by early stopping for online learning algorithms
L Rosasco, A Tacchetti, S Villa
stat 1050, 30, 2014
92014
Invariant recognition drives neural representations of action sequences
A Tacchetti, L Isik, T Poggio
PLoS computational biology 13 (12), e1005859, 2017
72017
Does invariant recognition predict tuning of neurons in sensory cortex?
T Poggio, J Mutch, F Anselmi, A Tacchetti, L Rosasco, JZ Leibo
72013
Relational forward models for multi-agent learning
A Tacchetti, HF Song, PAM Mediano, V Zambaldi, NC Rabinowitz, ...
arXiv preprint arXiv:1809.11044, 2018
62018
Invariant recognition shapes neural representations of visual input
A Tacchetti, L Isik, TA Poggio
Annual review of vision science 4, 403-422, 2018
52018
Invariant recognition predicts tuning of neurons in sensory cortex
J Mutch, F Anselmi, A Tacchetti, L Rosasco, JZ Leibo, T Poggio
Computational and Cognitive Neuroscience of Vision, 85-104, 2017
52017
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
52016
Magic Materials: a theory of deep hierarchical architectures for learning sensory representations CBCL paper
F Anselmi, JZ Leibo, L Rosasco, J Mutch, A Tacchetti, T Poggio
Massachusetts Institute of Technology, Cambridge, MA, 2013
52013
Implementation and tuning of the extended Kalman filter for a sensorless drive working with arbitrary stepper motors and cable lengths
M Butcher, A Masi, M Martino, A Tacchetti
2012 XXth International Conference on Electrical Machines, 2216-2222, 2012
52012
Invariances determine the hierarchical architecture and the tuning properties of the ventral stream
T Poggio, J Mutch, F Anselmi, JZ Leibo, L Rosasco, A Tacchetti
Technical Report available online, MIT CBCL, 2013. Previously released as …, 2011
52011
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Articles 1–20