8 citations · 12 across the 7 of their papers we have counts for
3 papers · 1 filter
How Diffusion Models Learn to Factorize and Compose
Qiyao Liang, Ziming Liu, Mitchell Ostrow +1
Diffusion models are capable of generating photo-realistic images that combine elements which likely do not appear together in the training set, demonstrating the ability to \texti…
Delay Embedding Theory of Neural Sequence Models
Mitchell Ostrow, Adam Eisen, Ila Fiete
To generate coherent responses, language models infer unobserved meaning from their input text sequence. One potential explanation for this capability arises from theories of delay…
Bridging Associative Memory and Probabilistic Modeling
Rylan Schaeffer, Nika Zahedi, Mikail Khona +9
Associative memory and probabilistic modeling are two fundamental topics in artificial intelligence. The first studies recurrent neural networks designed to denoise, complete and r…