The attention mechanism allows us to merge a variable-length sequence of vectors into a fixed-size context vector. What if we could use this mechanism to entirely replace recurrence for sequential modeling? This blog post covers the Transformer architecture which explores such an approach.
I have created a knowledgebase/graph neural network architecture (GIAANN prototype) which can be used to predict the next token in a sequence. It trains a set of columns for every new noun encountered in a textual corpus
This paper shows that feature learning enhances neural scaling laws by nearly doubling exponents for hard tasks and informing compute-efficient strategies in deep DNN training
tnet » Weighted Networks » Node Centrality The centrality of nodes, or the identification of which nodes are more "central" than others, has been a key issue in network analysis (Freeman, 1978; Bonacich, 1987; Borgatti, 2005; Borgatti et al., 2006). Freeman (1978) argued that central nodes were those "in the thick of things" or focal
The Architecture of Modular Neural Pipelines Optimizing modular neural network pipelines requires a rigorous approach to how individual components
The user discusses challenges with scaling and centering data, the implications of correlation near zero, the importance of model generalization, feature engineering in trading, and seeks examples of neural networks handling multi-dimensional input arrays for forex prediction
田中専務 拓海さん、最近若手が『この論文読んでみてください』って言うんですが、タイトルがまた硬くて。要するに何…
Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Machine Learning 7 Steps to Design a Basic Neural Network (part 1 of 2) A somewhat less math-intensive, step-by-step guide for building a one hidden-layer neural network from the ground up Gabe Verzino May 4, 2021 9 min read Share Image by Lindsay Henwood on Unsplash This two-part article takes a more holistic, overarching (and yes, less
Skip to content ICANN 2022 31st International Conference on Artificial Neural Networks Menu Contributors Venue About Conference topics ICANN 2022 is a dual-track conference featuring tracks in Brain Inspired Computing and Machine Learning and Artificial Neural Networks, with strong cross-disciplinary interactions and applications. All research fields dealing with Neural Networks will be present at the conference. A non-exhaustive list of topics includes the following. Machine Learning Deep Learning Neural N
Convolutional Networks Unreasonable Effectiveness of ConvNets on Object Recognition A short-course on convolutional neural networks. The focus of this course is to familiarize students with the key ideas that underpin convolutional neural networks (for object recognition). The material is divided into two parts. Part 1 covers the beginnings of neural networks and tries to make a case for the importance of understanding basic techniques, such as line fitting, i.e., linear regression, in order to truly apprec