From the 1 of 11 linked papers with an AI index.
1 citations · 1 across the 1 of their papers we have counts for
11 papers
Subjective functions
Samuel J. Gershman
The paper introduces the notion of a subjective function—a higher‑order, agent‑defined objective such as expected prediction error—and discusses how humans generate new goals and h…
Preemptive Solving of Future Problems: Multitask Preplay in Humans and Machines
Wilka Carvalho, Sam Hall-McMaster, Honglak Lee +1
Humans can pursue a near-infinite variety of tasks, but typically can only pursue a small number at the same time. We hypothesize that humans leverage experience on one task to pre…
Fast weight programming and linear transformers: from machine learning to neurobiology
Kazuki Irie, Samuel J. Gershman
Recent advances in artificial neural networks for machine learning, and language modeling in particular, have established a family of recurrent neural network (RNN) architectures t…
A Variational Manifold Embedding Framework for Nonlinear Dimensionality Reduction
John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Dimensionality reduction algorithms like principal component analysis (PCA) are workhorses of machine learning and neuroscience, but each has well-known limitations. Variants of PC…
Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules
John J. Vastola, Samuel J. Gershman, Kanaka Rajan
Learning rules -- prescriptions for updating model parameters to improve performance -- are typically assumed rather than derived. Why do some learning rules work better than other…
Blending Complementary Memory Systems in Hybrid Quadratic-Linear Transformers
Kazuki Irie, Morris Yau, Samuel J. Gershman
We develop hybrid memory architectures for general-purpose sequence processing neural networks, that combine key-value memory using softmax attention (KV-memory) with fast weight m…