8 citations · 13 across the 5 of their papers we have counts for
6 papers
High-Dimensional Non-Convex Landscapes and Gradient Descent Dynamics
Tony Bonnaire, Davide Ghio, Kamesh Krishnamurthy +3
In these lecture notes we present different methods and concepts developed in statistical physics to analyze gradient descent dynamics in high-dimensional non-convex landscapes. Ou…
Trainability, Expressivity and Interpretability in Gated Neural ODEs
Timothy Doyeon Kim, Tankut Can, Kamesh Krishnamurthy
Understanding how the dynamics in biological and artificial neural networks implement the computations required for a task is a salient open question in machine learning and neuros…
Emergence of robust memory manifolds
Tankut Can, Kamesh Krishnamurthy
The ability to store continuous variables in the state of a biological system (e.g. a neural network) is critical for many behaviours. Most models for implementing such a memory ma…
Theory of gating in recurrent neural networks
Kamesh Krishnamurthy, Tankut Can, David J. Schwab
Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) and neuroscience. Prior theoretical work has focused on RNNs with additive inte…
Gating creates slow modes and controls phase-space complexity in GRUs and LSTMs
Tankut Can, Kamesh Krishnamurthy, David J. Schwab
Recurrent neural networks (RNNs) are powerful dynamical models for data with complex temporal structure. However, training RNNs has traditionally proved challenging due to explodin…
Disorder and the neural representation of complex odors: smelling in the real world
Kamesh Krishnamurthy, Ann M Hermundstad, Thierry Mora +2
Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors a…