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07/29/2023
A Theory for Emergence of Complex Skills in Language Models
A major driver of AI products today is the fact that new skills emerge i...
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07/03/2023
Trainable Transformer in Transformer
Recent works attribute the capability of in-context learning (ICL) in la...
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05/27/2023
Fine-Tuning Language Models with Just Forward Passes
Fine-tuning language models (LMs) has yielded success on diverse downstr...
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03/14/2023
Do Transformers Parse while Predicting the Masked Word?
Pre-trained language models have been shown to encode linguistic structu...
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03/02/2023
Why (and When) does Local SGD Generalize Better than SGD?
Local SGD is a communication-efficient variant of SGD for large-scale tr...
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02/13/2023
Task-Specific Skill Localization in Fine-tuned Language Models
Pre-trained language models can be fine-tuned to solve diverse NLP tasks...
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11/05/2022
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound
Saliency methods compute heat maps that highlight portions of an input t...
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10/11/2022
A Kernel-Based View of Language Model Fine-Tuning
It has become standard to solve NLP tasks by fine-tuning pre-trained lan...
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10/03/2022
Understanding Influence Functions and Datamodels via Harmonic Analysis
Influence functions estimate effect of individual data points on predict...
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07/08/2022
Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent
As part of the effort to understand implicit bias of gradient descent in...
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06/14/2022
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction
Normalization layers (e.g., Batch Normalization, Layer Normalization) we...
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05/20/2022
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms
Approximating Stochastic Gradient Descent (SGD) as a Stochastic Differen...
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05/19/2022
Understanding Gradient Descent on Edge of Stability in Deep Learning
Deep learning experiments in Cohen et al. (2021) using deterministic Gra...
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03/02/2022
Adaptive Gradient Methods with Local Guarantees
Adaptive gradient methods are the method of choice for optimization in m...
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02/28/2022
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Contrastive learning is a popular form of self-supervised learning that ...
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11/30/2021
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Gradient inversion attack (or input recovery from gradient) is an emergi...
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11/28/2021
On Predicting Generalization using GANs
Research on generalization bounds for deep networks seeks to give ways t...
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10/26/2021
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias
The generalization mystery of overparametrized deep nets has motivated e...
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10/13/2021
What Happens after SGD Reaches Zero Loss? –A Mathematical Framework
Understanding the implicit bias of Stochastic Gradient Descent (SGD) is ...
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02/25/2021
Rip van Winkle's Razor: A Simple Estimate of Overfit to Test Data
Traditional statistics forbids use of test data (a.k.a. holdout data) du...
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02/24/2021
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)
It is generally recognized that finite learning rate (LR), in contrast t...
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10/16/2020
Why Are Convolutional Nets More Sample-Efficient than Fully-Connected Nets?
Convolutional neural networks often dominate fully-connected counterpart...
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10/12/2020
TextHide: Tackling Data Privacy in Language Understanding Tasks
An unsolved challenge in distributed or federated learning is to effecti...
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10/07/2020
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
Autoregressive language models pretrained on large corpora have been suc...
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10/06/2020
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
Recent works (e.g., (Li and Arora, 2020)) suggest that the use of popula...
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10/06/2020
InstaHide: Instance-hiding Schemes for Private Distributed Learning
How can multiple distributed entities collaboratively train a shared dee...
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03/04/2020
Privacy-preserving Learning via Deep Net Pruning
This paper attempts to answer the question whether neural network prunin...
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02/25/2020
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
One popular trend in meta-learning is to learn from many training tasks ...
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02/24/2020
Provable Representation Learning for Imitation Learning via Bi-level Optimization
A common strategy in modern learning systems is to learn a representatio...
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02/16/2020
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality
Adversarial training is a popular method to give neural nets robustness ...
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11/03/2019
Enhanced Convolutional Neural Tangent Kernels
Recent research shows that for training with â„“_2 loss, convolutional neu...
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10/16/2019
An Exponential Learning Rate Schedule for Deep Learning
Intriguing empirical evidence exists that deep learning can work well wi...
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10/03/2019
Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Recent research shows that the following two models are equivalent: (a) ...
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06/14/2019
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets
Mode connectivity is a surprising phenomenon in the loss landscape of de...
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05/31/2019
Implicit Regularization in Deep Matrix Factorization
Efforts to understand the generalization mystery in deep learning have l...
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05/27/2019
A Simple Saliency Method That Passes the Sanity Checks
There is great interest in *saliency methods* (also called *attribution ...
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04/26/2019
On Exact Computation with an Infinitely Wide Neural Net
How well does a classic deep net architecture like AlexNet or VGG19 clas...
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02/25/2019
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Recent empirical works have successfully used unlabeled data to learn fe...
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01/24/2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Recent works have cast some light on the mystery of why deep nets fit an...
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12/10/2018
Theoretical Analysis of Auto Rate-Tuning by Batch Normalization
Batch Normalization (BN) has become a cornerstone of deep learning acros...
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10/04/2018
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
We analyze speed of convergence to global optimum for gradient descent t...
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05/14/2018
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Motivations like domain adaptation, transfer learning, and feature learn...
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03/05/2018
An Analysis of the t-SNE Algorithm for Data Visualization
A first line of attack in exploratory data analysis is data visualizatio...
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02/19/2018
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Conventional wisdom in deep learning states that increasing depth improv...
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02/14/2018
Stronger generalization bounds for deep nets via a compression approach
Deep nets generalize well despite having more parameters than the number...
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11/07/2017
Theoretical limitations of Encoder-Decoder GAN architectures
Encoder-decoder GANs architectures (e.g., BiGAN and ALI) seek to add an ...
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06/14/2017
Provable benefits of representation learning
There is general consensus that learning representations is useful for a...
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04/29/2017
Extending and Improving Wordnet via Unsupervised Word Embeddings
This work presents an unsupervised approach for improving WordNet that b...
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03/02/2017
Generalization and Equilibrium in Generative Adversarial Nets (GANs)
We show that training of generative adversarial network (GAN) may not ha...
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12/28/2016
