A technical guide to convolutional neural networks: convolution, pooling, stride, padding, classic architectures, CNNs vs vision transformers
If a neural network is <em>modular</em>, that means it consists of clusters (modules) of neurons, such that the neurons within the cluster are strongly connected to each other, but only weakly connected to the rest of the network. Making networks more modular is useful to us if the modules represent concepts which we can understand because this
Introduction Deep neural networks (DNNs) are great at fitting data, but terrible at predicting when they don’t know the right answer. Tackling this latter challenge (often called uncertainty quantification) is at the core of various problems in AI/ML. For example, reducing hallucinations in large language models, 1 or the classical balancing of exploration versus exploitation in reinforcement learning, neural bandits, and Bayesian optimization. A popular way to aim for both reliable predictions and
← Back to homepage Neural network Activation Visualization with tf-explain April 27, 2020 by Chris Deep learning models and especially neural networks have been used thoroughly over the past few years. There are many success cases in the press about the application of those models. One of the primary categories in which those are applied is the field of computer vision, mainly thanks to the 2012 revolution in Convolutional Neural Networks . However, until recently, it was very difficult to understand how
A meta-learning approach called CNN-INTE is proposed to interpret deep convolutional neural networks by generating faithful explanations for test instances while
Guide to Application on Neural Network . Here we also discuss introduction and their their top three application respectively
# Computer Science > Neural and Evolutionary Computing arXiv:1412.3555 (cs) [Submitted on 11 Dec 2014] # Title:Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling Authors: Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , Yoshua Bengio Download PDF > Abstract: In this paper we compare different types of recurrent units in recurrent > neural networks (RNNs). Especially, we focus on more sophisticated units that > implement a gating mechanism, such as a long short-term memory
Machine learning must always balance flexibility and prior assumptions about the data. In neural networks, the network architecture codifies these prior assumptions, yet the precise relationship between them is opaque. Deep learning solutions are therefore difficult to build without a lot of trial and error, and neural nets are far from an out-of-the-box solution for most applications
Abstract page for arXiv paper 2109.09300: Feature Correlation Aggregation: on the Path to Better Graph Neural Networks
Large-scale neural network models form the basis of many AI-based technologies such as neuromorphic chips, which are inspired by the human brain. Training these networks can be tedious, time-consuming, and energy-inefficient