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Found 7 papers, 4 papers with code
Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-terminal Coding Sequences
The NCS optimization problem is to find an NCS that maximizes gene expression.
StableSSM: Alleviating the Curse of Memory in State-space Models through Stable Reparameterization
In this paper, we investigate the long-term memory learning capabilities of state-space models (SSMs) from the perspective of parameterization.
HyperSNN: A new efficient and robust deep learning model for resource constrained control applications
In light of the increasing adoption of edge computing in areas such as intelligent furniture, robotics, and smart homes, this paper introduces HyperSNN, an innovative method for control tasks that uses spiking neural networks (SNNs) in combination with hyperdimensional computing.
Improve Long-term Memory Learning Through Rescaling the Error Temporally
To the best of our knowledge, this is the first work that quantitatively analyzes different errors' memory bias towards short-term memory in sequence modelling.
Inverse Approximation Theory for Nonlinear Recurrent Neural Networks
We prove an inverse approximation theorem for the approximation of nonlinear sequence-to-sequence relationships using recurrent neural networks (RNNs).
A Brief Survey on the Approximation Theory for Sequence Modelling
We survey current developments in the approximation theory of sequence modelling in machine learning.
Efficient Hyperdimensional Computing
Hyperdimensional computing (HDC) is a method to perform classification that uses binary vectors with high dimensions and the majority rule.
