14 papers
LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
Alexander Chemeris, Ming Jin, Randall Balestriero
Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self-supervised learning often d…
Aionoscope: Debugging Latent-State Accessibility in Time-Series Representations
Alexander Chemeris, Ming Jin, Randall Balestriero
Time-series models are often evaluated by what they can forecast or classify, but those scores do not show whether their representations preserve the process state a user may want…
VISReg: Variance-Invariance-Sketching Regularization for JEPA training
Haiyu Wu, Randall Balestriero, Morgan Levine
Self-supervised learning methods prevent embedding collapse via modeling heuristics or explicit regularization of the embedding space. Among the latter, VICReg decomposes regulariz…
Curvature Tuning: Provable Training-free Model Steering From a Single Parameter
Leyang Hu, Matteo Gamba, Randall Balestriero
The scaling of model and data sizes has reshaped the AI landscape, establishing finetuning pretrained models as the standard paradigm for solving downstream tasks. However, dominan…
stable-pretraining-v1: Foundation Model Research Made Simple
Randall Balestriero, Hugues Van Assel, Sami BuGhanem +1
Foundation models and self-supervised learning (SSL) have become central to modern AI, yet research in this area remains hindered by complex codebases, redundant re-implementations…
MapSAM2: Adapting SAM2 for Automatic Segmentation of Historical Map Images and Time Series
Xue Xia, Randall Balestriero, Tao Zhang +4
Historical maps are unique and valuable archives that document geographic features across different time periods. However, automated analysis of historical map images remains a sig…