33 citations · 74 across the 6 of their papers we have counts for
11 papers
The Calibration Generalization Gap
A. Michael Carrell, Neil Mallinar, James Lucas +1
Calibration is a fundamental property of a good predictive model: it requires that the model predicts correctly in proportion to its confidence. Modern neural networks, however, pr…
Optimizing Data Collection for Machine Learning
Rafid Mahmood, James Lucas, Jose M. Alvarez +2
Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting dat…
Causal Scene BERT: Improving object detection by searching for challenging groups of data
Cinjon Resnick, Or Litany, Amlan Kar +4
Modern computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection. These modules frequently have low…
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas, Juhan Bae, Michael R. Zhang +3
Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decreas…
Theoretical bounds on estimation error for meta-learning
James Lucas, Mengye Ren, Irene Kameni +2
Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning…
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Qiyang Li, Saminul Haque, Cem Anil +3
Lipschitz constraints under L2 norm on deep neural networks are useful for provable adversarial robustness bounds, stable training, and Wasserstein distance estimation. While heuri…