Author

A. Fuller

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Preprint Sep 2026

Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learning

Self-supervised learning (SSL) removes the need for annotations and makes models that are capable across more domains than supervised learning. The autoencoder SSL framework learns by reconstructing its own input after information loss through a bottleneck or noise injection. Masked autoencoders (MAE) are the most succ...

A. Fuller, Scott C. Lowe, Daniel G. Kyrollos et al. · 0 citations

#machine learning Preprint Sep 2026

Planetary Feature Fields are Scalable Earth Representations

Satellite observations, precomputed embeddings, and map products describe the same evolving Earth, yet are stored as independent, petabyte-scale data products. Their continued growth calls for compact representations of multiple products while preserving spatial and temporal detail. We introduce Planetary Feature Field...

Arjun Rao, Sebastian Loeschcke, A. Fuller et al. · 0 citations

#machine learning Preprint Sep 2026

LookThere! Sparse Vision by Reinforced Selection

Vision transformers typically treat every image token as equally important, yet for most tasks in computer vision only a fraction are needed. Adaptive computation methods accelerate inference by choosing which tokens to process, but existing methods struggle at extreme sparsity and require heuristics that may not gener...

Sreehari Rammohan, Yousef Yassin, A. Fuller et al. · 0 citations