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On Invariance and Selectivity in Representation Learning

On Invariance and Selectivity in Representation Learning

19 March 2015
Fabio Anselmi
Lorenzo Rosasco
T. Poggio
ArXivPDFHTML

Papers citing "On Invariance and Selectivity in Representation Learning"

12 / 12 papers shown
Title
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
Mircea Petrache
Shubhendu Trivedi
42
23
0
27 May 2023
Evaluating the effect of data augmentation and BALD heuristics on
  distillation of Semantic-KITTI dataset
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset
Ngoc Phuong Anh Duong
Alexandre Almin
Léo Lemarié
B. R. Kiran
27
0
0
21 Feb 2023
On the Strong Correlation Between Model Invariance and Generalization
On the Strong Correlation Between Model Invariance and Generalization
Weijian Deng
Stephen Gould
Liang Zheng
OOD
32
16
0
14 Jul 2022
LiDAR dataset distillation within bayesian active learning framework:
  Understanding the effect of data augmentation
LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation
Ngoc Phuong Anh Duong
Alexandre Almin
Léo Lemarié
B. R. Kiran
21
3
0
06 Feb 2022
GENEOnet: A new machine learning paradigm based on Group Equivariant
  Non-Expansive Operators. An application to protein pocket detection
GENEOnet: A new machine learning paradigm based on Group Equivariant Non-Expansive Operators. An application to protein pocket detection
Giovanni Bocchi
Patrizio Frosini
Alessandra Micheletti
A. Pedretti
Carmen Gratteri
Filippo Lunghini
A. Beccari
Carmine Talarico
11
7
0
31 Jan 2022
Three approaches to facilitate DNN generalization to objects in
  out-of-distribution orientations and illuminations
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
Akira Sakai
Taro Sunagawa
Spandan Madan
Kanata Suzuki
Takashi Katoh
Hiromichi Kobashi
Hanspeter Pfister
Pawan Sinha
Xavier Boix
Tomotake Sasaki
19
2
0
30 Oct 2021
BIGDML: Towards Exact Machine Learning Force Fields for Materials
BIGDML: Towards Exact Machine Learning Force Fields for Materials
H. E. Sauceda
Luis E Gálvez-González
Stefan Chmiela
L. O. Paz-Borbón
K. Müller
A. Tkatchenko
AI4CE
34
47
0
08 Jun 2021
Machine Learning Force Fields
Machine Learning Force Fields
Oliver T. Unke
Stefan Chmiela
H. E. Sauceda
M. Gastegger
I. Poltavsky
Kristof T. Schütt
A. Tkatchenko
K. Müller
AI4CE
34
888
0
14 Oct 2020
Towards a topological-geometrical theory of group equivariant
  non-expansive operators for data analysis and machine learning
Towards a topological-geometrical theory of group equivariant non-expansive operators for data analysis and machine learning
M. Bergomi
Patrizio Frosini
D. Giorgi
Nicola Quercioli
AI4CE
22
47
0
31 Dec 2018
Universal approximations of invariant maps by neural networks
Universal approximations of invariant maps by neural networks
Dmitry Yarotsky
32
205
0
26 Apr 2018
Generalization Error of Invariant Classifiers
Generalization Error of Invariant Classifiers
Jure Sokolić
Raja Giryes
Guillermo Sapiro
M. Rodrigues
15
77
0
14 Oct 2016
View-tolerant face recognition and Hebbian learning imply
  mirror-symmetric neural tuning to head orientation
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
Joel Z Leibo
Q. Liao
W. Freiwald
Fabio Anselmi
T. Poggio
CVBM
18
56
0
05 Jun 2016
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