Biologically-Plausible Learning Algorithms Can Scale to Large Datasets by Will Xiao, Honglin Chen, Qianli Liao and Tomaso Poggio
 Department of Molecular and Cellular Biology, Harvard University
 Center for Brains, Minds, and Machines, MIT
Background Consider a layer in a feedforward neural network. Let xi denote the input to the i th neuron in the layer and yj the output of the j th neuron. Let W denote the feedforward weight matrix and Wij the connection between input xi and output y.
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This work aims to derive a non-trivial breakdown point for an algorithm for training a single hiddenlayer Neural Network. In pursuit of this goal, we propose two algorithms for training a network with ReLU activations. The first approach utilizes the partitioning property of the ReLU function while the second approach utilizes the convexity of the activation function.
There is a large disparity in the access to proper education at the school level. We seek to build a problem aware online tutoring system for students to help improve the situation. In this project, we have made a complete solution generator which, given a word problem on arithmetic at the levels of classes 6-8, extracts relevant information from the question and solves the problem in a step-by-step manner. Currently, we are handling only basic speed, time and distance problems.