This week, we were talking about neural networks for the first time, and I was saying that, in many illustrations of neural networks, there was a layer with fewer neurons than predictive variables, but sometimes, it could make sense to have more neurons in the layer than predictive variables, To illustrate, consider a simple example with one single variable , and a binary outcome set.seed(12345) n = 100 x = c(runif(n),1+runif(n),2+runif(n)) y = rep(c(0,1,0),each=n) We should insure that observations are in
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Current representation analysis tools like PCA and linear probing may systematically miss complex nonlinear computations while over-reporting simple linear features. This raises questions about whether our interpretability methods are actually capturing what networks compute
DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks入力された検索クエリに対して最も適切な文言の広告を出すタスク． 検索クエリおよび広告を単語の系列データとして， RNN などにもとづく neural network に通して適当な空間に写像し，その空間上でのクエリ/広告の内積を計算し，その値がクリックされたペアであれば大きくなるようにする． モデル 個の単語
The text discusses the challenges of developing profitable trading neural networks, emphasizing the need for research, understanding market mechanics, and iterative improvement rather than focusing solely on profit. It highlights the importance of learning to predict price movements, improving network quality, and avoiding reliance on indicators. The author shares personal experience with a working neural network that predicts price changes and criticizes the lack of fundamental understanding in many develo
## Documentation Index Fetch the complete documentation index at: /llms.txt Use this file to discover all available pages before exploring further. Skip to main content Edge Impulse Documentation home page Search... ⌘K Search... Navigation Neural networks Layers Knowledge Studio Hardware Tools APIs Tutorials Projects Datasets ### INTRODUCTION Welcome! ### OVERVIEW Knowledge FAQ Glossary ### GUIDES Getting started Advanced topics Optimization Reference designs ### CONCEPTS Data
# ICLR Poster NTK-SAP: Improving neural network pruning by aligning training dynamics Pruning neural networks before training has received increasing interest due to its potential to reduce training time and memory. One popular method is to prune the connections based on a certain metric, but it is not entirely clear what metric is the best choice. Recent advances in neural tangent kernel (NTK) theory suggest that the training dynamics of large enough neural networks is closely related to the spectrum of t
This paper from Gravity R&D, Telefonica Research, and Netflix introduces a GRU-based recurrent neural network model for session-based recommendations, overcoming limitations of traditional methods
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# Character-level Convolutional Networks for Text Classification Advances in Neural Information Processing Systems 28 (NIPS 2015) ## Abstract This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams a