Abstract page for arXiv paper 1806.10758: A Benchmark for Interpretability Methods in Deep Neural Networks
A first example of a neural network · Tensors and tensor operations · How neural networks learn via backpropagation and gradient descent
NeurIPS Proceedings Search Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Aaron Voelker, Ivana Kajić, Chris Eliasmith Advances in Neural Information Processing Systems 32 (NeurIPS 2019) Abstract We propose a novel memory cell for recurrent neural networks that dynamically maintains information across long windows of time using relatively few resources. The Legendre Memory Unit~(LMU) is mathematically derived to orthogonalize its continuous-time history -- doing so by
NeurIPS Proceedings Search Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks Aaron Voelker, Ivana Kajić, Chris Eliasmith Advances in Neural Information Processing Systems 32 (NeurIPS 2019) Abstract We propose a novel memory cell for recurrent neural networks that dynamically maintains information across long windows of time using relatively few resources. The Legendre Memory Unit~(LMU) is mathematically derived to orthogonalize its continuous-time history -- doing so by
Explore the intricate neural networks involved in decision-making, from the prefrontal cortex to the limbic system, and their impact on human behavior
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Lorentz' dev blog Writing a neural network from scratch in C (part 2) 30 Jul 2025 - Lorentz Vedeler This post is part 2 in a series. Part 1: Linear regression Part 2: Neural networks In this part we will take a look at how perceptrons and neural networks imitate the human brain. Perceptrons The perceptron is an artificial neuron and is the most basic building block in neural networks. Similar to how a neuron receives signals and “fires” when triggered, a perceptron receives inputs and may or may not
Spiking neural networks (SNNs) capture the most important aspects of brain information processing. They are considered a promising approach for next-generation artificial intelligence. However, the biggest problem restricting
To theoretically understand the behavior of trained deep neural networks, it is necessary to study the dynamics induced by gradient methods from a random initialization. However, the nonlinear and compositional structure of these models make these dynamics difficult to analyze. To overcome these challenges, large-width asymptotics have recently emerged as a fruitful viewpoint and led to practical insights on real-world deep networks. For two-layer neural networks, it has been understood via these asymptotic