Mode Connectivity Beyond Classifiers: Evidence from Generative and Contrastive Models
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
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.
arXiv:2606. 19941v1 Announce Type: new Abstract: Compositionality is believed to be the foundation for generalization, enabling models to reuse meaningful primitives in novel combinations.
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.
arXiv:2608.28948v1 Announce Type: cross Abstract: Over the course of the last decade, neural networks have grown from an academic curiosity to moving the markets of nations. Despite this explosion in...
arXiv:2607. 23970v1 Announce Type: cross Abstract: Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch.
arXiv:2504. 06407v2 Announce Type: replace Abstract: Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch.