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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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10/31/2022
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Studying data memorization in neural language models helps us understand...
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06/21/2022
The Privacy Onion Effect: Memorization is Relative
Machine learning models trained on private datasets have been shown to l...
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05/26/2022
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures
This paper considers the Pointer Value Retrieval (PVR) benchmark introdu...
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04/15/2022
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
With the increasing adoption of NLP models in real-world products, it be...
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02/15/2022
Quantifying Memorization Across Neural Language Models
Large language models (LMs) have been shown to memorize parts of their t...
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12/24/2021
Counterfactual Memorization in Neural Language Models
Modern neural language models widely used in tasks across NLP risk memor...
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10/15/2021
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
We investigate the robustness of vision transformers (ViTs) through the ...
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08/19/2021
Do Vision Transformers See Like Convolutional Neural Networks?
Convolutional neural networks (CNNs) have so far been the de-facto model...
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07/27/2021
Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization
The successes of deep learning critically rely on the ability of neural ...
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07/14/2021
Deduplicating Training Data Makes Language Models Better
We find that existing language modeling datasets contain many near-dupli...
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03/15/2021
Understanding invariance via feedforward inversion of discriminatively trained classifiers
A discriminatively trained neural net classifier achieves optimal perfor...
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02/11/2021
On Deep Learning with Label Differential Privacy
In many machine learning applications, the training data can contain hig...
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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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08/09/2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Deep learning algorithms are well-known to have a propensity for fitting...
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02/08/2020
Exploring the Memorization-Generalization Continuum in Deep Learning
Human learners appreciate that some facts demand memorization whereas ot...
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02/14/2019
Transfusion: Understanding Transfer Learning with Applications to Medical Imaging
With the increasingly varied applications of deep learning, transfer lea...
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02/13/2019
Identity Crisis: Memorization and Generalization under Extreme Overparameterization
We study the interplay between memorization and generalization of overpa...
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02/06/2019
Are All Layers Created Equal?
Understanding learning and generalization of deep architectures has been...
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09/22/2018
Unrestricted Adversarial Examples
We introduce a two-player contest for evaluating the safety and robustne...
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04/18/2018
A Study on Overfitting in Deep Reinforcement Learning
Recent years have witnessed significant progresses in deep Reinforcement...
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02/21/2018
Machine Theory of Mind
Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'...
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01/07/2018
Theory of Deep Learning IIb: Optimization Properties of SGD
In Theory IIb we characterize with a mix of theory and experiments the o...
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12/03/2015
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
MXNet is a multi-language machine learning (ML) library to ease the deve...
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06/17/2015
Learning with a Wasserstein Loss
Learning to predict multi-label outputs is challenging, but in many prob...
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04/01/2014