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Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models
Benjamin L. Badger, Ethan Roland
Transformer-based large language models are in some respects limited by the quadratic time and space computational complexity of attention. We introduce the Toeplitz MLP Mixer (TMM…
Adversarial Examples from Dimensional Invariance
Benjamin L. Badger
Adversarial examples have been found for various deep as well as shallow learning models, and have at various times been suggested to be either fixable model-specific bugs, or else…
Why Deep Learning Generalizes
Benjamin L. Badger
Very large deep learning models trained using gradient descent are remarkably resistant to memorization given their huge capacity, but are at the same time capable of fitting large…
Small Language Models for Tabular Data
Benjamin L. Badger
Supervised deep learning is most commonly applied to difficult problems defined on large and often extensively curated datasets. Here we demonstrate the ability of deep representat…