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07/18/2023
Can Neural Network Memorization Be Localized?
Recent efforts at explaining the interplay of memorization and generaliz...
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02/27/2023
The Role of Pre-training Data in Transfer Learning
The transfer learning paradigm of model pre-training and subsequent fine...
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12/08/2022
Leveraging Unlabeled Data to Track Memorization
Deep neural networks may easily memorize noisy labels present in real-wo...
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11/18/2022
Layer-Stack Temperature Scaling
Recent works demonstrate that early layers in a neural network contain u...
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11/15/2022
REPAIR: REnormalizing Permuted Activations for Interpolation Repair
In this paper we look into the conjecture of Entezari et al.(2021) which...
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11/15/2022
Teaching Algorithmic Reasoning via In-context Learning
Large language models (LLMs) have shown increasing in-context learning c...
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06/22/2022
Understanding the effect of sparsity on neural networks robustness
This paper examines the impact of static sparsity on the robustness of a...
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01/11/2022
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance
Real-world machine learning deployments are characterized by mismatches ...
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10/12/2021
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks
In this paper, we conjecture that if the permutation invariance of neura...
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10/05/2021
Exploring the Limits of Large Scale Pre-training
Recent developments in large-scale machine learning suggest that by scal...
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06/10/2021
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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10/16/2020
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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08/26/2020
What is being transferred in transfer learning?
One desired capability for machines is the ability to transfer their kno...
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12/02/2019
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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05/29/2019
Size-free generalization bounds for convolutional neural networks
We prove bounds on the generalization error of convolutional networks. T...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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01/07/2019
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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05/26/2018
The Singular Values of Convolutional Layers
We characterize the singular values of the linear transformation associa...
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12/12/2016
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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03/03/2016
Training Input-Output Recurrent Neural Networks through Spectral Methods
We consider the problem of training input-output recurrent neural networ...
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06/28/2015
Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods
Training neural networks is a challenging non-convex optimization proble...
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03/16/2015
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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12/19/2014
Score Function Features for Discriminative Learning
Feature learning forms the cornerstone for tackling challenging learning...
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12/09/2014
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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12/09/2014
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning...
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12/08/2014
Provable Methods for Training Neural Networks with Sparse Connectivity
We provide novel guaranteed approaches for training feedforward neural n...
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02/20/2014
