Abstract page for arXiv paper 2207.02098: Neural Networks and the Chomsky Hierarchy
Deep Neural networks – also known as Multi Layer Perceptrons – are universal approximator, i.e. an ML approximator to any function in a bounded continuous domain. They’re the most important and relevant ML tool on the field of supervised learning as they’re included in models related to most supervised tasks. The structure of a basic NN is the following: one input layer of size \(D\), \(L\) hidden layers of size \(K\), and one output layer. It is a feedfoward network: the computation performed by
# Inference of Neural Dynamics Using Switching Recurrent Neural Networks Yongxu Zhang ⋅ Shreya Saxena 2024 Poster ### Abstract Neural population activity often exhibits distinct dynamical features across time, which may correspond to distinct internal processes or behavior. Linear methods and variations thereof, such as Hidden Markov Model (HMM) and Switching Linear Dynamical System (SLDS), are often employed to identify discrete states with evolving neural dynamics. However, these techniques may not be
This paper rethinks session-based recommendation using graph neural networks to model item transitions in short sessions, boosting prediction accuracy
In this article, I will explain how to perform Training Neural Networks Using Backpropagation. Backpropagation is the primary algorithm used to train neural networks. It is a supervised learning algorithm that involves iteratively adjusting the weights of a neural network to minimize the difference between the predicted output and the target output. Here are the
Developments in machine learning interpretability techniques over the past decade have provided new tools to observe the image regions that are most informative for classification and localization in artificial neural networks (ANNs). Are the same regions similarly informative to human observers? Using data from 79 new experiments and 7,810 participants, we show that passive attention techniques reveal a significant overlap with human visual selectivity estimates derived from 6 distinct behavioral tasks inc
A step forward in explainable AI, and why it is hard to adapt existing explanation methods to GNNs
Explore how the brain organizes information through neural networks and cognitive processes, from neuroanatomy to plasticity and adaptive learning
Learn how embeddings encode entities into low-dimensional vectors using neural networks for NLP, vision, and recommendation systems applications
Neural Network Backpropagation implementation issuesI've been reading up quite a bit on Neural Networks and training them with backprogpagation, primarily