Kernel Memory Networks: A Unifying Framework for Memory Modeling
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Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, ...
At initialization, artificial neural networks (ANNs) are equivalent to Gaussian processes in the infinite-width limit [12, 9], thus connecting them to kernel methods. We prove that the evolution of an ANN during training can also be described by a kernel: ...
setuid system calls enable critical functions such as user authentications and modular privileged components. Such operations must only be executed after careful validation. However, current systems do not perform rigorous checks, allowing exploitation of ...
For decades, neuroscientists and psychologists have observed that animal performance on spatial navigation tasks suggests an internal learned map of the environment. More recently, map-based (or model-based) reinforcement learning has become a highly activ ...
Humans are able to learn and compose complex, yet beautiful, pieces of music as seen in e.g. the highly complicated works of J.S. Bach. However, how our brain is able to store and produce these very long temporal sequences is still an open question. Long s ...
Our brain has the capacity to analyze a visual scene in a split second, to learn how to play an instrument, and to remember events, faces and concepts. Neurons underlie all of these diverse functions. Neurons, cells within the brain that generate and trans ...
We investigate a vocoder based on artificial neural networks using a phonological speech representation. Speech decomposition is based on the phonological encoders, realised as neural network classifiers, that are trained for a particular language. The spe ...
We investigate a vocoder based on artificial neural networks using a phonological speech representation. Speech decomposition is based on the phonological encoders, realised as neural network classifiers, that are trained for a particular language. The spe ...
Associative memory problem: Find the closest stored vector (in Hamming distance) to a given query vector. There are different ways to implement an associative memory, including the neural networks and DNA strands. Using neural networks, connection weights ...
We present a proposal of a kernel-based model for large vocabulary continuous speech recognizer. The continuous speech recognition is described as a problem of finding the best phoneme sequence and its best time span, where the phonemes are generated from ...