Date  Topic/paper  Author  Presenter  Additional Links 
31 Aug 2015  Introduction   Bhiksha Raj [slides]  
21 Sep 2015  Torch, Theano and AWS   Danny Lan and Prasanna Muthukumar  [Prasanna's code]
[Danny's Slides] 
9 Sep 2015  Bain on Neural Networks. Brain and Cognition 33:295305, 1997  Alan L. Wilkes and Nicholas J. Wade  Stephanie Rosenthal [slides]  
 McCulloch, W.S. & Pitts, W.H. (1943). A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, 5:115137, 1943.  W.S. McCulloch and W.H. Pitts  Fatima Talib AlRaisi [slides]  Michael Marsalli's tutorial on the McCulloch and Pitts Neuron
The First Computational Theory of Mind and Brain: A Close Look at McCulloch and Pitts' "Logical Calculus of Ideas Immanent in Nervous Activity", Gualtiero Piccinini, Synthese 141: 175.215, 2004

14 Sep 2015  The Perceptron: A Probalistic Model For Information Storage And Organization In The Brain. Psychological Review 65 (6): 386.408, 1958.  F. Rosenblatt  Manu [slides] 
More about threshold logic. Proc. Second Annual Symposium on Switching Circuit Theory and Logical Design, 1961. R. O. Winder.

14 Sep 2015  The organization of Behavior, 1949.  D. O. Hebb  Srivaths R. [slides]  
16 Sep 2015  The Widrow Hoff learning rule (ADALINE and MADALINE).  Widrow  Xuanchong [slides]  
21 Sep 2015 
Backpropagation through time: what it does and how to do it., Proc. IEEE 1990 
P. Werbos 
Bernie [slides] 

21 Sep 2015 
On the problem of local minima in backpropagation, IEEE tran.
Pattern Analysis and Machine Intelligence, Vol 14(1), 7686, 1992.

M. Gori, A. Tesi 
Sai [slides] 
Training a 3node neural network is NPcomplete, Avrim Blum and Ron Rivest, COLT 88

23 Sep 2015 
Backpropagation fails where perceptrons succeed, IEEE Trans on circuits and systems. Vol. 36:5, May 1989

Martin Brady, Raghu Raghavan, Joseph Slawny 
Suruchi [slides] 

23 September 
Multilayer feedforward networks are universal approximators, Neural Networks, Vol:2(3), 359366, 1989

K. Hornik, M. Stinchcombe, H. White 
Aman Gupta [slides] 
Neural networks with a continuous squashing function in the output are universal approximators, J.L. Castro, C.J. Mantas, J.M. Benitez, Neural Networks, Vol 13, pp. 561563, 2000

28 September 
A visual illustration of how neural networks approximate functions

Michael Nielsen 
Nikolas Wolfe [slides] 

28 September 
A Simplified Neuron Model as a Principal Component Analyzer, J. Math. Biology (1982) 15:267273 
Erkki Oja 
Amir Zade [slides] 

30 September 
The selforganizing map. Proc. IEEE, Vol 79, 1464:1480, 1990
 Teuvo Kohonen 
Karishma Agrawal [slides] 

30 September 
SelfOrganizing Maps and Learning Vector Quantization for Feature Sequences
 Panu Somervuo, Teuvo Kohonen 
Aditya Sharma [slides] 

5 October 
Neural networks and physical systems with emergent collective computational abilities, Proc. Natl. Acad. Sciences, Vol 79, 25542558, 1982
 John Hopfield 
Hinton [slides] 

5 October 
A learning algorithm for Boltzmann machines, Cognitive Science, 9, 147169, 1985
 D. Ackley, G. Hinton, T. Sejnowski 
Shi Zong
[slides] 
Learning and Relearning in Boltzmann machines, T. Sejnowski and G. Hinton
Improved simulated annealing, Boltzmann machine, and attributed graph matching, Lei Xu and Erkii Oja, EURASIP Workshop on Neural Networks, vol 412, LNCS, Springer, pp: 151160, 1990 
5 October 
Phoneme recognition using timedelay neural networks, IEEE trans.
Acoustics, Speech Signal Processing, Vol 37(3), March 1989

Waibel, Hanazawa, Hinton, Shikano, Lang 
Allard Dupuis
[slides] 

7 October 
Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position, Pattern Recognition Vol. 15(6), pp. 455469, 1982 
Kunihiko Fukushima and Sei Miyake 
Chenchen Zhu
[slides] 

7 October 
An artificial neural network for spatiotemporal bipolar patterns: application to phoneme classification 
Toshiteru Homma 
Serim Park
[slides] 

7 October 
Gradient based learning applied to document recognition, Proceedings of IEEE, Vol 86:11, Nov 1998, pp 22782324. 
Yann Lecun, Leon Boton, Yohsua Bengio, Patrick Haffner 
Lu Jiang
[slides] 

12 Oct 
Supervised sequence labelling with recurrent neural networks, PhD
dissertation, T. U. Munchen, 2008, Chapters 4 and 7

Alex Graves 
Kazuya [slides] 

12 Oct 
Bidirectional Recurrent Neural Networks

Mike Schuster and Kuldip K. Paliwal 
Praveen Palanisamy [slides] 

12 Oct 
Long ShortTerm Memory

Sepp Hochreiter Jurgen Schmidhuber 
Zihang Dai [slides] 

14 Oct 
The CascadeCorrelation Learning Architecture

Scott E. Fahlman and Christian Lebiere 
Scott E. Fahlman [slides] 

14 Oct 
The Recurrent CascadeCorrelation Architecture

Scott E. Fahlman 
Scott E. Fahlman 

19 Oct 
ImageNet Classification with Deep Convolutional
Neural Networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton 
Guillaume Lample [slides] 

19 Oct 
Very Deep Convolutional Networks for LargeScale Image Recognition

Karen Simonyan, Andrew Zisserman 
Pradeep [slides] 

21 Oct 
Visualizing and Understanding Convolutional Networks

Matthew D Zeiler, Rob Fergus 
Wanli [slides] 

21 Oct 
Dropout: A Simple Way to Prevent Neural Networks from
Overfitting

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov 
Xuanchong 
 
21 Oct 
Maxout Network

Ian J. Goodfellow, David WardeFarley, Mehdi Mirza, Aaron Courville, Yoshua Bengio 
Sandeep 
 