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Found 7 papers, 3 papers with code
Producing Plankton Classifiers that are Robust to Dataset Shift
For example, a MobileNet with a 92% nominal test accuracy shows a 77% OOD accuracy.
Initial Guessing Bias: How Untrained Networks Favor Some Classes
Understanding and controlling biasing effects in neural networks is crucial for ensuring accurate and fair model performance.
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.
A Theoretical Analysis of the Learning Dynamics under Class Imbalance
We find that GD is not guaranteed to decrease the loss for each class but that this problem can be addressed by performing a per-class normalization of the gradient.
Predicting Chemical Hazard across Taxa through Machine Learning
We used our approach with standard machine learning models (K-nearest neighbors, random forests and deep neural networks), as well as the recently proposed Read-Across Structure Activity Relationship (RASAR) models, which were very successful in predicting chemical hazards to mammals based on chemical similarity.
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
In the vicinity of this transition, properties of the curvature of the minima of the loss are critical.
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems.
