Abstract page for arXiv paper 2501.01158v2: Attending To Syntactic Information In Biomedical Event Extraction Via Graph Neural Networks
NIPS 2017 Mon Dec 4th through Sat the 9th, 2017 at Long Beach Convention Center Paper ID: 617 Title: Self-Normalizing Neural Networks Reviewer 1 I am putting "accept" because this paper already seems to be attracting a lot of attention in the DL community and I see no reason to squash it. However I do have some reservations. There is all this theory and a huge derivation, showing that under certain conditions this particular type of unit will lead to activation norms that don't blow up or get small. Basical
# Bayesian Networks (6) - DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models - Oct 23, 2020. Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset. DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models - Oct 28, 2019. Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent a
Inducing Causal Structure for Interpretable Neural NetworksAtticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thoma
Spiking neural networks (SNNs) have demonstrated significant potential in real-time multi-sensor perception tasks due to their event-driven and parameter-efficient
A Neural Network is a computing system loosely inspired by the human brain, consisting of interconnected layers of artificial neurons that process
ISCA Archive Interspeech 2021 ISCA Archive Interspeech 2021 Phoneme-to-Audio Alignment with Recurrent Neural Networks for Speaking and Singing Voice Yann Teytaut, Axel Roebel Phoneme-to-audio alignment is the task of synchronizing voice recordings and their related phonetic transcripts. In this work, we introduce a new system to forced phonetic alignment with Recurrent Neural Networks (RNN). With the Connectionist Temporal Classification (CTC) loss as training objective, and an additional reconstruction cos
(Images generated by BigGAN) Neural networks are a kind of machine learning algorithm that learn to imitate the examples I give them. They’re pretty good at picking up on the feel of craft beer names vs guinea pig names, or metal bands vs my little ponies
Despite deep neural networks' powerful representation learning capabilities, theoretical understanding of how networks can simultaneously achieve meaningful feature learning and global convergence remains elusive. Existing approaches like the neural tangent kernel (NTK) are limited because features stay close to their initialization in this parametrization, leaving open questions about feature properties during substantial evolution. In this paper, we investigate the training dynamics of infinitely wide, $L
↓ Skip to main content PLOS Article Metrics What is this page? Embed badge Share Shared spatiotemporal category representations in biological and artificial deep neural networks Overview of attention for article published in PLoS Computational Biology, July 2018 Altmetric Badge Mentioned by twitter 15 X users facebook 1 Facebook page Readers on mendeley 90 Mendeley Summary X Facebook Article details Title Shared spatiotemporal category representations in biological and artificial deep neural networks