Edward Github Latent Space Models for Neural Data Many scientific fields involve the study of network data, including social networks, networks in statistical physics, biological networks, and information networks (Goldenberg, Zheng, Fienberg, & Airoldi, 2010; Newman, 2010). What we can learn about nodes in a network from their connectivity patterns? We can begin to study this using a latent space model (Hoff, Raftery, & Handcock, 2002). Latent space models embed nodes in the network in a latent space, wher

The paper introduces Broken Neural Scaling Laws, a novel piecewise linear model that accurately predicts neural network performance transitions, including double descent, across diverse tasks

Led by a team from the Institute of Automation, Chinese Academy of Sciences, a new study explores a novel frontier in machine learning. With the rise of large language models, AI is evolving from perceptual intelligence to ...

Units navigate_next Basic Convolutional Networks search Quick search code Show Source STAT 157, Spring 19 - 1. Ensuring Quality Conversations in Online Forums - 2. Image attribute classification using disentangled embeddings on multimodal data - 3. Deep Learning with NLP (Tacotron) - 4. Image captioning - 5. Explainable Electrocardiogram Classifications using Neural Networks - 7. Deep fitting room - 8. Bot controlled accounts - 9. Predicting Next Day Stock Returns After Earnings Reports Using Deep Lea

The Adaptive Neuro-Fuzzy Inference System (ANFIS)—also known as Adaptive Network-based Fuzzy Inference System—is a powerful computational model that seamlessly blends fuzzy logic with artificial neural network methods

When your Neural Network has more than one output, then it is very common to train with SoftMax and

YUV Video Super-Resolution Case Study

Open Menu Proceedings of the AAAI Conference on Artificial Intelligence Search Login Home / Archives / Vol. 36 No. 7: AAAI-22 Technical Tracks 7 / AAAI Technical Track on Machine Learning II Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model Authors Xin Qiu Cognizant AI Labs Risto Miikkulainen The University of Texas at Austin Cognizant AI Labs DOI: https://doi.org/10.1609/aaai.v36i7.20773 Keywords: Machine Learning (ML) Abstract As neural network classifiers are deployed in

NeurIPS Proceedings Search On Scrambling Phenomena for Randomly Initialized Recurrent Networks Vaggos Chatziafratis, Ioannis Panageas, Clayton Sanford, Stelios Stavroulakis Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track Abstract Recurrent Neural Networks (RNNs) frequently exhibit complicated dynamics, and their sensitivity to the initialization process often renders them notoriously hard to train. Recent works have shed light on such phenomena analyzing when explod

Close Menu Home » Technology »Demystifying Machine-Learning Systems: Automatically Describing Neural Network Components in Natural Language Technology Demystifying Machine-Learning Systems: Automatically Describing Neural Network Components in Natural Language By Adam Zewe, Massachusetts Institute of TechnologyFebruary 7, 2022 No Comments 7 Mins Read Share MIT researchers created a technique that can automatically describe the roles of individual neurons in a neural network with natural language. In this