Some example
papers.
This list is not exhaustive, these are just a few examples. Students can come up with other papers that they are interested in. The choice is entirely up to the students.
This list is not exhaustive, these are just a few examples. Students can come up with other papers that they are interested in. The choice is entirely up to the students.
T.
Toyoizumi, J.-P. Pfister, K. Aihara, W. Gerstner: Spike-timing
Dependent Plasticity and Mutual Information Maximization for a Spiking
Neuron Model
Advances in Neural Information Processing Systems 17, MIT Press
G Chechik, A Globerson, N Tishby,Y Weiss Information Bottleneck for Gaussian variables.
J. Machine Learning Research 6(Jan) p.165-188, 2005
D. MacKay: Information-based objective functions for active data selection, Neural Computation 4 4 589-603
V. Balasubramanian, Statistical Inference, Occam's Razor and Statistical Mechanics on The Space of Probability Distributions, Neural Computation, Vol.9, No.2, Feb. 1997
Occam factors and model-independent Bayesian learning of continuous distributions. I Nemenman & W Bialek, Phys Rev E 65, 026137 (2002)
S. Tong, D. Koller: Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research. Volume 2, pages 45-66. 2001
A Bordes, S Ertekin, J Weston, L Bottou, Fast Kernel Classifiers with Online and Active Learning, Journal of Machine Learning Research, vol. 6, pp. 1579-1619, Sep. 2005
Field theories for learning probability distributions. W Bialek, CG Callan & SP Strong, Phys Rev Lett 77, 4693-4697 (1996)
W Bialek, I Nemenman & N Tishby, Complexity through nonextensivity Physica A 302, 89-99 (2001)
R. Linsker, Self-organization in a perceptual network. Computer 21 (3) 105-17 (1988)
Chris Watkins and Peter Dayan. Q-Learning. Machine Learning, 8:279--292, 1992.
Google MathWorld
Advances in Neural Information Processing Systems 17, MIT Press
G Chechik, A Globerson, N Tishby,Y Weiss Information Bottleneck for Gaussian variables.
J. Machine Learning Research 6(Jan) p.165-188, 2005
D. MacKay: Information-based objective functions for active data selection, Neural Computation 4 4 589-603
V. Balasubramanian, Statistical Inference, Occam's Razor and Statistical Mechanics on The Space of Probability Distributions, Neural Computation, Vol.9, No.2, Feb. 1997
Occam factors and model-independent Bayesian learning of continuous distributions. I Nemenman & W Bialek, Phys Rev E 65, 026137 (2002)
S. Tong, D. Koller: Support Vector Machine Active Learning with Applications to Text Classification. Journal of Machine Learning Research. Volume 2, pages 45-66. 2001
A Bordes, S Ertekin, J Weston, L Bottou, Fast Kernel Classifiers with Online and Active Learning, Journal of Machine Learning Research, vol. 6, pp. 1579-1619, Sep. 2005
Field theories for learning probability distributions. W Bialek, CG Callan & SP Strong, Phys Rev Lett 77, 4693-4697 (1996)
W Bialek, I Nemenman & N Tishby, Complexity through nonextensivity Physica A 302, 89-99 (2001)
R. Linsker, Self-organization in a perceptual network. Computer 21 (3) 105-17 (1988)
Chris Watkins and Peter Dayan. Q-Learning. Machine Learning, 8:279--292, 1992.
Google MathWorld