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Found 5 papers, 4 papers with code
Continual Learning In Environments With Polynomial Mixing Times
The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios.
NoisET: Noise learning and Expansion detection of T-cell receptors
High-throughput sequencing of T- and B-cell receptors makes it possible to track immune repertoires across time, in different tissues, in acute and chronic diseases and in healthy individuals.
On Lyapunov Exponents for RNNs: Understanding Information Propagation Using Dynamical Systems Tools
Recurrent neural networks (RNNs) have been successfully applied to a variety of problems involving sequential data, but their optimization is sensitive to parameter initialization, architecture, and optimizer hyperparameters.
Inferring the immune response from repertoire sequencing
High-throughput sequencing of B- and T-cell receptors makes it possible to track immune repertoires across time, in different tissues, and in acute and chronic diseases or in healthy individuals.
Quantitative Methods
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
A recent strategy to circumvent the exploding and vanishing gradient problem in RNNs, and to allow the stable propagation of signals over long time scales, is to constrain recurrent connectivity matrices to be orthogonal or unitary.
