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08/07/2023
Studying Large Language Model Generalization with Influence Functions
When trying to gain better visibility into a machine learning model in o...
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06/12/2023
Benchmarking Neural Network Training Algorithms
Training algorithms, broadly construed, are an essential part of every d...
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02/07/2023
Efficient Parametric Approximations of Neural Network Function Space Distance
It is often useful to compactly summarize important properties of model ...
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12/07/2022
Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
Variational autoencoders (VAEs) are powerful tools for learning latent r...
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09/12/2022
If Influence Functions are the Answer, Then What is the Question?
Influence functions efficiently estimate the effect of removing a single...
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02/28/2022
Amortized Proximal Optimization
We propose a framework for online meta-optimization of parameters that g...
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04/22/2021
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
Linear interpolation between initial neural network parameters and conve...
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10/26/2020
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians
Hyperparameter optimization of neural networks can be elegantly formulat...
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11/30/2018
Eigenvalue Corrected Noisy Natural Gradient
Variational Bayesian neural networks combine the flexibility of deep lea...
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10/01/2018
