Search Results for author:
Found 8 papers, 1 papers with code
Efficient kernel surrogates for neural network-based regression
For the regression problem of smooth functions and logistic regression classification, we show that the CK performance is only marginally worse than that of the NTK and, in certain cases, is shown to be superior.
Computational and Systems Biology Advances to Enable Bioagent-Agnostic Signatures
Enumerated threat agent lists have long driven biodefense priorities.
Foundation Model's Embedded Representations May Detect Distribution Shift
Sampling biases can cause distribution shifts between train and test datasets for supervised learning tasks, obscuring our ability to understand the generalization capacity of a model.
Robust Nonparametric Hypothesis Testing to Understand Variability in Training Neural Networks
Training a deep neural network (DNN) often involves stochastic optimization, which means each run will produce a different model.
Exploring Learned Representations of Neural Networks with Principal Component Analysis
Understanding feature representation for deep neural networks (DNNs) remains an open question within the general field of explainable AI.
Minibatching Offers Improved Generalization Performance for Second Order Optimizers
Training deep neural networks (DNNs) used in modern machine learning is computationally expensive.
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks.
TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models
We introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework.
