ACL Anthology About Announcements Communication channels Related work Copyright Credits Volunteer Development Feedback Using Citing papers Links in the Anthology Data access All FAQs Details Anthology identifiers Names ORCID iDs DOIs Verified authors Contributions Submissions Corrections Author pages Attachments GitHub BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP (2024) Volumes Show all abstractsHide all abstracts up pdf (full) bib (full) Proceedings of the 7th BlackboxNLP Workshop: Analy
Feed-forward neural networks with a single hidden layer and lagged inputs for forecasting univariate time series
Python, machine learning, neural networks
This blog post will teach you how to build a neural network from scratch with Pytorch. We'll cover everything from loading data to training and evaluating the
EN Get an estimate SlidesLive Categories EN - NeurIPS - NIPS 2019 - 2019 West 217 - 219 - Context and Compositionality in Biological and Artificial Neural Systems - Uncovering the compositional structure of vector representations with Role Learning Networks ## Uncovering the compositional structure of vector representations with Role Learning Networks Dec 14, 2019 ### Speakers ### Organizer ### Categories Learn more ### Sharing Copy share link Show advanced options ### Interested in talks lik
← Back to homepage Which regularizer do I need for training my neural network? January 26, 2020 by Chris There are three widely known regularization techniques for neural networks: L1 (or Lasso) regularization, L2 (or Ridge) regularization and Elastic Net regularization, which combines the two, and is also called L1+L2. But which regularizer to use in your neural network? Especially for larger machine learning projects and for people who just started with machine learning, this is an often confusing
Fine-tuning uses weights of a trained neural network to train a new network. Start with a pretrained model, then modify with Intel® Neural Compressor
Fine-tuning uses weights of a trained neural network to train a new network. Start with a pretrained model, then modify with Intel® Neural Compressor
The discussion revolves around training neural networks for forex trading, emphasizing the need for proper data division, crossvalidation, and handling of metrics like Sharpe ratio. The user mentions challenges with RNeat, the importance of model evaluation, and the preference for custom solutions over existing packages. They also compare different approaches, such as using caret and GA packages, and highlight the complexity of integrating neural networks into trading systems
NeurIPS Proceedings Search What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman, Chiyuan Zhang Advances in Neural Information Processing Systems 33 (NeurIPS 2020) Abstract Deep learning algorithms are well-known to have a propensity for fitting the training data very well and often fit even outliers and mislabeled data points. Such fitting requires memorization of training data labels, a phenomenon that has attracted significant research interest but has n