
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Can deep learning solve multiple tasks simultaneously, even when they ar...
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For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions
It is well established that training deep neural networks gives useful r...
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Learning the gravitational force law and other analytic functions
Large neural network models have been successful in learning functions o...
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How does the Mind store Information?
How we store information in our mind has been a major intriguing open qu...
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Recursive Sketches for Modular Deep Learning
We present a mechanism to compute a sketch (succinct summary) of how a c...
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On the Learnability of Deep Random Networks
In this paper we study the learnability of deep random networks from bot...
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Learning Two layer Networks with Multinomial Activation and High Thresholds
Giving provable guarantees for learning neural networks is a core challe...
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Sparse Matrix Factorization
We investigate the problem of factorizing a matrix into several sparse m...
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Optimal amortized regret in every interval
Consider the classical problem of predicting the next bit in a sequence ...
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The Mind Grows Circuits
There is a vast supply of prior art that study models for mental process...
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Rina Panigrahy
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