
Second Order Optimization Made Practical
Optimization in machine learning, both theoretical and applied, is prese...
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Proximity Preserving Binary Code using Signed GraphCut
We introduce a binary embedding framework, called Proximity Preserving C...
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Convolutional Bipartite Attractor Networks
In human perception and cognition, the fundamental operation that brains...
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Identity Crisis: Memorization and Generalization under Extreme Overparameterization
We study the interplay between memorization and generalization of overpa...
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Are All Layers Created Equal?
Understanding learning and generalization of deep architectures has been...
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Exponentiated Gradient Meets Gradient Descent
The (stochastic) gradient descent and the multiplicative update method a...
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MemoryEfficient Adaptive Optimization for LargeScale Learning
Adaptive gradientbased optimizers such as AdaGrad and Adam are among th...
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The Well Tempered Lasso
We study the complexity of the entire regularization path for least squa...
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Shampoo: Preconditioned Stochastic Tensor Optimization
Preconditioned gradient methods are among the most general and powerful ...
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A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization
We describe a framework for deriving and analyzing online optimization a...
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Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
We develop a general duality between neural networks and compositional k...
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Train faster, generalize better: Stability of stochastic gradient descent
We show that parametric models trained by a stochastic gradient method (...
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Using Web Cooccurrence Statistics for Improving Image Categorization
Object recognition and localization are important tasks in computer visi...
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Update Rules for Parameter Estimation in Bayesian Networks
This paper reexamines the problem of parameter estimation in Bayesian n...
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Switching Portfolios
A constant rebalanced portfolio is an asset allocation algorithm which k...
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Matrix Approximation under Local LowRank Assumption
Matrix approximation is a common tool in machine learning for building a...
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Yoram Singer
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