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Found 4 papers, 0 papers with code
Decomposing spiking neural networks with Graphical Neural Activity Threads
A satisfactory understanding of information processing in spiking neural networks requires appropriate computational abstractions of neural activity.
Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability
In response to this, we discuss a parallel extension of a widely used format for efficiently representing sparse matrices, the compressed sparse row (CSR), in the context of supporting the simulation and serialization of large-scale SNNs.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
MIKE - Multi-task Implicit Knowledge Embeddings by Autoencoding through a Shared Input Space
In this work, we introduce a method of learning Multi-task Implicit Knowledge Embeddings (MIKE) from a set of source (or "teacher") networks by autoencoding through a shared input space.
