F. Anselmi

发表

Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? pdf

Lorenzo Rosasco, Joel Z. Leibo, Tomaso A. Poggio, 2014 .

On Invariance and Selectivity in Representation Learning pdf

Lorenzo Rosasco, Tomaso A. Poggio, Fabio Anselmi, 2015, ArXiv.

Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex

Joel Z. Leibo, T. Poggio, A. Tacchetti, 2017 .

Does invariant recognition predict tuning of neurons in sensory cortex ?

Joel Z. Leibo, T. Poggio, A. Tacchetti, 2013 .

Unsupervised learning of invariant representations

Lorenzo Rosasco, Joel Z. Leibo, Tomaso A. Poggio, 2016, Theor. Comput. Sci..

Symmetry-adapted representation learning

Lorenzo Rosasco, Tomaso A. Poggio, Georgios Evangelopoulos, 2019, Pattern Recognit..

Memo No . 63 May 26 , 2017 Symmetry Regularization

T. Poggio, L. Rosasco, F. Anselmi, 2017 .

No . 041 December 29 , 2015 I-theory on depth vs width : hierarchical function composition by Tomaso Poggio with

L. Rosasco, F. Anselmi, 2015 .

Memo No . 35 August 5 , 2015 Deep Convolutional Networks are Hierarchical Kernel Machines

Lorenzo Rosasco, Tomaso Poggio, Cheston Tan, 2015 .

Local copying of orthogonal entangled quantum states

F. Anselmi, M. Plenio, A. Chefles, 2004, quant-ph/0407168.

Tradeoff between extractable mechanical work, accessible entanglement, and ability to act as a reference system, under arbitrary superselection rules

F. Anselmi, J. Vaccaro, H. Wiseman, 2005, quant-ph/0501121.

ph ] 1 4 M ar 2 01 1 Pauli graphs , Riemann hypothesis , Goldbach pairs

F. Anselmi, P. Solé, M. Planat, 2013 .

Графы Паули, гипотеза Римана и гольдбаховы пары@@@Pauli graphs, Riemann hypothesis, and Goldbach pairs

F. Anselmi, M. Planat, P. Solé, 2012 .

Pauli graphs, Riemann hypothesis, and Goldbach pairs

F. Anselmi, P. Solé, M. Planat, 2011, 1103.2608.

ENTANGLEMENT OF IDENTICAL PARTICLES AND REFERENCE PHASE UNCERTAINTY

F. Anselmi, H. Wiseman, John A.Vaccaro, 2003, quant-ph/0311028.

Representation Learning in Sensory Cortex: A Theory

Tomaso Poggio, Fabio Anselmi, T. Poggio, 2014, IEEE Access.

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2014, bioRxiv.

The invariance hypothesis implies domain-specific regions in visual cortex

Tomaso Poggio, Joel Z. Leibo, Qianli Liao, 2014 .

View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation

Joel Z. Leibo, Tomaso A. Poggio, Qianli Liao, 2016, Current Biology.

A computational model for grid maps in neural populations

Micah M. Murray, Fabio Anselmi, Benedetta Franceschiello, 2019, Journal of Computational Neuroscience.

Unsupervised Learning of Invariant Representations in Hierarchical Architectures

Joel Z. Leibo, T. Poggio, A. Tacchetti, 2013, ArXiv.

Robust deep learning object recognition models rely on low frequency information in natural images

Ankit B. Patel, M. Bethge, A. Tolias, 2022, bioRxiv.

Complementarity between extractable mechanical work, accessible entanglement, and ability to act as a reference frame, under arbitrary superselection rules

Howard Mark Wiseman, Fabio Anselmi, Kurt Jacobs, 2005 .

Deep Convolutional Networks are Hierarchical Kernel Machines pdf

Lorenzo Rosasco, Tomaso A. Poggio, Cheston Tan, 2015, ArXiv.

Data Symmetries and Learning in Fully Connected Neural Networks

L. Manzoni, F. Anselmi, L. Bortolussi, 2023, IEEE Access.

Neurally plausible mechanisms for learning selective and invariant representations

Lorenzo Rosasco, Fabio Anselmi, Ankit Patel, 2020, Journal of mathematical neuroscience.

Visual Cortex and Deep Networks: Learning Invariant Representations

Fabio Anselmi, Tomaso Poggio, T. Poggio, 2016 .

Machine learning algorithms on eye tracking trajectories to classify patients with spatial neglect

Tommaso Di Noto, M. Murray, F. Anselmi, 2022, Comput. Methods Programs Biomed..

Convolutional neural networks on eye tracking trajectories in patients with spatial neglect

Micah M. Murray, Astrid Minier, Fabio Anselmi, 2020, medRxiv.

Convolutional Neural Networks on Eye Tracking Trajectories Classify Patients with Spatial Neglect

M. Murray, F. Anselmi, P. Bartolomeo, 2021, SSRN Electronic Journal.

Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples

Ankit B. Patel, Wieland Brendel, F. Anselmi, 2020, 2006.11440.

I-theory on depth vs width: hierarchical function composition

Tomaso Poggio, Lorenzo Rosasco, Fabio Anselmi, 2015 .

Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?

Tomaso Poggio, Lorenzo Rosasco, Joel Z. Leibo, 2013, 1311.4158.

Genuine Personality Recognition from Highly Constrained Face Images

Lorenzo Rosasco, Nicoletta Noceti, Robert Ward, 2019, ICIAP.

Representation Learning in Sensory Cortex: A Theory

T. Poggio, F. Anselmi, 2014, IEEE Access.

Understanding Robustness and Generalization of Artificial Neural Networks Through Fourier Masks

Ankit B. Patel, A. Tolias, F. Anselmi, 2022, Frontiers in Artificial Intelligence.

Notes on Hierarchical Splines, DCLNs and i-theory

Tomaso Poggio, Lorenzo Rosasco, Amnon Shashua, 2015 .

Unsupervised Learning of Invariant Representations in Hierarchical Architectures

Joel Z. Leibo, A. Tacchetti, Jim Mutch, 2013, ArXiv.