Fetching the paper…
Reading the bibliography…
The present phase of Machine Learning is characterized by supervised learning algorithms relying on large sets of labeled examples ($n \to \infty$).
Some theorems on distribution functions
H. Cramer and H. Wold · 1936
Earlier work this paper cites.
A logical calculus of the ideas immanent in the nervous activity
W.S. McCulloch, W. Pitts · 1943
Earlier work this paper cites.
Theory of communication. Part 1: The analysis of information
D. Gabor · 1946
Earlier work this paper cites.
How we know universals the perception of auditory and visual forms
W. Pitts, W. Mcculloch · 1947
Earlier work this paper cites.
On the determination of probability distributions of more dimensions by their projections
A. Heppes · 1956
Earlier work this paper cites.
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
D.H. Hubel and T.N. Wiesel · 1962
Earlier work this paper cites.
On optimal nonlinear associative recall
T. Poggio · 1975
Earlier work this paper cites.
From understanding computation to understanding neural circuitry
D. Marr, T. Poggio · 1976
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
Spatiotemporal energy models for the perception of motion
E. Adelson and J. Bergen · 1985
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel · 1989
Earlier work this paper cites.
Uncertainty principles and signal recovery
D. L. Donoho, P. B. Stark · 1989
Earlier work this paper cites.
A theory of how the brain might work
T. Poggio · 1990
Earlier work this paper cites.
A network that learns to recognize three-dimensional objects
T. Poggio, S. Edelmann · 1990
Earlier work this paper cites.
Holographic Reduced Representations: Convolution Algebra for Compositional Distributed Representations,
T. Plate, · 1991
Earlier work this paper cites.
Learning invariance from transformation sequences
P. Földiák · 1991
Earlier work this paper cites.
Recognition and structure from one 2D model view: Observations on prototypes, object classes and symmetries, 1992
T. Poggio, T. Vetter, and M. I. O. T. C. A. I. LAB · 1992
Earlier work this paper cites.
Constructing invariant features by averaging techniques
H. Schulz-Mirbach · 1994
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
Y. LeCun and Y. Bengio · 1995
Earlier work this paper cites.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images,
B.A. Olshausen et al · 1996
Earlier work this paper cites.
Models of object recognition
M. Riesenhuber and T. Poggio · 2000
Earlier work this paper cites.
Effects of temporal association on recognition memory
G. Wallis and H. H. Bülthoff · 2001
Cited alongside, same era.
On the mathematical foundations of learning
Felipe Cucker and Steve Smale, · 2002
Cited alongside, same era.
Slow feature analysis: Unsupervised learning of invariances
L. Wiskott, T.J. Sejnowski · 2002
Cited alongside, same era.
Faces and objects in macaque cerebral cortex
D.Y. Tsao, W.A. Freiwald, · 2003
Cited alongside, same era.
The mathematics of learning: Dealing with data
T. Poggio and S. Smale · 2003
Cited alongside, same era.
Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. Huang, and L. Bottou · 2004
Cited alongside, same era.
Mathematics of the neural response
S. Smale, L. Rosasco, J. Bouvrie, A. Caponnetto, and T. Poggio · 2010
Later among the works it cites.
What and where: A Bayesian inference theory of attention
S. S. Chikkerur, T. Serre, C. Tan, and T. Poggio · 2010
Later among the works it cites.
Functional specificity in the human brain: a window into the functional architecture of the mind,
N. Kanwisher, · 2010
Later among the works it cites.
Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex
N. Li and J. J. DiCarlo · 2010
Later among the works it cites.
Automated home-cage behavioural phenotyping of mice
H. Jhuang, E. Garrote, J. Mutch, X. Yu, V. Khilnani, T. Poggio, A. Steele and T. Serre, · 2010
Later among the works it cites.
Learning Generic Invariances in Object Recognition: Translation and Scale
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Preserving properties of object shape by computations in primary visual cortex
C. Stevens · 2004
Cited alongside, same era.
Computation in the higher visual cortices: Map-seeking circuit theory and application to machine vision
D. Arathorn · 2004
Cited alongside, same era.
A theory of object recognition: computations and circuits in the feedforward path of the ventral stream in primate visual cortex
T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, and T. Poggio · 2005
Cited alongside, same era.
A hierarchical bayesian model of invariant pattern recognition in the visual cortex
D. George and J. Hawkins · 2005
Cited alongside, same era.
Histograms of Oriented Gradients for Human Detection
N. Dalal and B. Triggs · 2005
Cited alongside, same era.
Invariance and selectivity in the ventral visual pathway
S. Geman · 2006
Cited alongside, same era.
J.Z. Leibo, J. Mutch, L. Rosasco, S. Ullman, T. Poggio, · 2010
Later among the works it cites.
Why The Brain Separates Face Recognition From Object Recognition
J. Z. Leibo, J. Mutch, and T. Poggio · 2011
Later among the works it cites.
S. Soatto · 2011
Later among the works it cites.
Building high-level features using large scale unsupervised learning
Q. V. Le, R. Monga, M. Devin, G. Corrado, K. Chen, M. Ranzato, J. Dean, and A. Y. Ng · 2011
Later among the works it cites.
On Random Weights and Unsupervised Feature Learning,
A. Saxe, P.W. Koh, Z. Chen, M. Bhand, B. Suresh, A. Ng, · 2011
Later among the works it cites.
Video-based descriptors for object recognition
T. Lee and S. Soatto · 2012
Later among the works it cites.
Group invariant scattering
S. Mallat · 2012
Later among the works it cites.
Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition
O. Abdel-Hamid, A. Mohamed, H. Jiang, and G. Penn · 2012
Later among the works it cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Later among the works it cites.
Combined scattering for rotation invariant texture, 2012
L. Sifre, S. Mallat, and P. France · 2012
Later among the works it cites.
Learning and disrupting invariance in visual recognition with a temporal association rule
L. Isik, J.Z. Leibo, T. Poggio · 2012
Later among the works it cites.
Magic Materials: a theory of deep hierarchical architectures for learning sensory representations
F. Anselmi, J.Z. Leibo, L. Rosasco, J. Mutch, A. Tacchetti, T. Poggio · 2013
Closest in time.
Learning invariant representations and applications to face verification
Q. Liao, J.Z. Leibo, T. Poggio · 2013
Closest in time.
View-invariance and mirror-symmetric tuning in a model of the macaque face-processing system
J.Z. Leibo, F. Anselmi, J. Mutch, A.F. Ebihara, W. Freiwald, T. Poggio, · 2013
Closest in time.
The timing of invariant object recognition in the human visual system
L. Isik, E. M. Meyers, J. Z. Leibo, and T. Poggio · 2013
Closest in time.
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream
D. Yamins, H. Hong, C.F. Cadieu and J.J. DiCarlo · 2013
Closest in time.