arXiv Machine Learning
Jun 19

Linear Mode Connectivity under Data Shifts for Deep Ensembles of Image Classifiers

arXiv:2511. 04514v2 Announce Type: replace Abstract: The phenomenon of linear mode connectivity (LMC) links several aspects of deep learning, including training stability under noisy stochastic gradients, the smoothness and generalization of local minima (basins), the similarity and functional diversity of sampled models, and architectural effects on data processing.

By C. Hepburn, T. Zielke, A. P. Raulf
Hugging Face Trending Papers
Oct 29

Mode Dynamics: A Framework for Neural Network Learning

Mode Dynamics is a framework that links coordinated parameter motion during neural network training to changes in model outputs. By decomposing recorded parameter trajectories into collective modes and mapping each mode to its state‑dependent output effect, the framework uses decomposition, reconstruction, and concentration mechanisms to analyze how behavior emerges from parameter updates. Experiments on CIFAR‑100 autoencoding and a Pythia‑70M pretraining trajectory show that a small set of leading modes can reconstruct behavior, that induced effects depend on the training path, and that modal complexity varies with task and data.