Randall Balestriero

LeNEPA achieves faster representation learning without relying on data augmentation, outperforming traditional methods in both efficiency and adaptability across diverse datasets.

Aionoscope reveals that while many time-series models can identify basic signal components, they often fail to capture essential timing and amplitude details crucial for effective debugging.

Non-contrastive pretraining can yield dense semantic features that outperform traditional contrastive methods in vision-language tasks.

Transition effects can be disentangled into reusable primitives, leading to superior policy learning even in complex, ambiguous environments.

OctoSense outperforms conventional image-only models in multimodal robot perception, achieving robust performance even under degraded sensory conditions.

Achieving robust zero-shot sim-to-real transfer for quadrotors, this work redefines the boundaries of long-horizon prediction in robotic control.

Action-aligned representations can be achieved without complex training methods, enabling robust planning in dynamic environments.

A new phase diagram reveals that cross-modal training can be actively harmful in certain contexts, guiding practitioners to choose the right approach before training.

VISReg not only stabilizes embedding training but also achieves state-of-the-art performance with a fraction of the data used by competing methods.