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Found 6 papers, 2 papers with code
Using large language models to study human memory for meaningful narratives
One of the most impressive achievements of the AI revolution is the development of large language models that can generate meaningful text and respond to instructions in plain English with no additional training necessary.
Trainability, Expressivity and Interpretability in Gated Neural ODEs
Using this measure, we explore how the phase-space dimension of the nODEs and the complexity of the function modeling the flow field contribute to expressivity.
Flatter, faster: scaling momentum for optimal speedup of SGD
Commonly used optimization algorithms often show a trade-off between good generalization and fast training times.
Emergence of memory manifolds
The ability to store continuous variables in the state of a biological system (e. g. a neural network) is critical for many behaviours.
Theory of gating in recurrent neural networks
The gate modulating the dimensionality can induce a novel, discontinuous chaotic transition, where inputs push a stable system to strong chaotic activity, in contrast to the typically stabilizing effect of inputs.
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs
Here, we take the perspective of studying randomly initialized LSTMs and GRUs as dynamical systems, and ask how the salient dynamical properties are shaped by the gates.
