While neural networks perform well on image, text, and audio datasets, they fall behind decision trees and their variations for tabular datasets. New research looked into why. What’s new: Léo Grinsztajn, Edouard Oyall
Given the importance of getting calibrated predictions and reliable uncertainty estimations, various post-hoc calibration methods have been developed for neural networks on standard multi-class classification tasks. However, these methods are not well suited for calibrating graph neural networks (GNNs), which presents unique challenges such as accounting for the graph structure and the graph-induced correlations between the nodes. In this work, we conduct a systematic study on the calibration qualities of G
Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR
Deep Learning: Machine learning using neural networks with many layers — the approach behind nearly every recent AI breakthrough
Regularizing Neural Networks via Stochastic Branch LayersWonpyo Park, Paul Hongsuck Seo, Bohyung Han, Minsu ChoWe introduce a novel stochastic
Abstract page for arXiv paper 2003.00330: Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
In this blog, I write about how I traveled to Japan with minimal AI experience, and left coding my own neural networks (NNs). This is the story of why I ventured to the opposite side of the world, and the steps I took to learn AI architecture
Hacking Metaphors, Frames and Other Ideas
Series LIPIcs – Leibniz International Proceedings in Informatics OASIcs – Open Access Series in Informatics Dagstuhl Follow-Ups Schloss Dagstuhl Jahresbericht Discontinued Series Journals DARTS – Dagstuhl Artifacts Series Dagstuhl Reports Dagstuhl Manifestos LITES – Leibniz Transactions on Embedded Systems TGDK – Transactions on Graph Data and Knowledge Conferences Artifacts Metadata Export Document https://doi.org/10.4230/LIPIcs.CSL.2024.9 Descriptive Complexity for Neural Networks via Boolean
Multivariate Calibration Modeling - An Example Modeling with latent variables Validation of Models Multiple Regression PCA Factor Analysis PLS Neural Networks Natural Brains Mapping of Spaces Neural Networks - Model Selection Taxonomy of ANNs Models of Neural Networks Recurrent Networks Multilayer Perceptron Kohonen Networks RBF Neural Networks Generalization and Overtraining Extrapolation Growing Neural Networks Common Questions about ANNs Classification and Discrimination