
Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
We focus on the problem of domain adaptation when the goal is shifting t...
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The Deep Bootstrap: Good Online Learners are Good Offline Generalizers
We propose a new framework for reasoning about generalization in deep le...
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What is being transferred in transfer learning?
One desired capability for machines is the ability to transfer their kno...
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The intriguing role of module criticality in the generalization of deep networks
We study the phenomenon that some modules of deep neural networks (DNNs)...
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Sizefree generalization bounds for convolutional neural networks
We prove bounds on the generalization error of convolutional networks. T...
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SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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On the effect of the activation function on the distribution of hidden nodes in a deep network
We analyze the joint probability distribution on the lengths of the vect...
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The Singular Values of Convolutional Layers
We characterize the singular values of the linear transformation associa...
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Knowledge Completion for Generics using Guided Tensor Factorization
Given a knowledge base (KB) rich in facts about common nouns or generics...
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Training InputOutput Recurrent Neural Networks through Spectral Methods
We consider the problem of training inputoutput recurrent neural networ...
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Beating the Perils of NonConvexity: Guaranteed Training of Neural Networks using Tensor Methods
Training neural networks is a challenging nonconvex optimization proble...
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Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods
Community detection in graphs has been extensively studied both in theor...
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Score Function Features for Discriminative Learning
Feature learning forms the cornerstone for tackling challenging learning...
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Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
We consider the problem of learning mixtures of generalized linear model...
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Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning...
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Provable Methods for Training Neural Networks with Sparse Connectivity
We provide novel guaranteed approaches for training feedforward neural n...
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MultiStep Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition
We propose an efficient ADMM method with guarantees for highdimensional...
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Hanie Sedghi
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