Series LIPIcs – Leibniz International Proceedings in Informatics OASIcs – Open Access Series in Informatics Dagstuhl Follow-Ups Schloss Dagstuhl Jahresbericht Discontinued Series Journals DARTS – Dagstuhl Artifacts Series Dagstuhl Reports Dagstuhl Manifestos LITES – Leibniz Transactions on Embedded Systems TGDK – Transactions on Graph Data and Knowledge Conferences Artifacts Metadata Export Document https://doi.org/10.4230/LIPIcs.CSL.2024.9 Descriptive Complexity for Neural Networks via Boolean

Multivariate Calibration Modeling - An Example Modeling with latent variables Validation of Models Multiple Regression PCA Factor Analysis PLS Neural Networks Natural Brains Mapping of Spaces Neural Networks - Model Selection Taxonomy of ANNs Models of Neural Networks Recurrent Networks Multilayer Perceptron Kohonen Networks RBF Neural Networks Generalization and Overtraining Extrapolation Growing Neural Networks Common Questions about ANNs Classification and Discrimination

Sequence-to-Sequence Neural Network From GM-RKB A Sequence-to-Sequence Neural Network is a sequence-to-* model that is a neural network . Context: It can range from being a Simple Sequence-to-Sequence Neural Network to being a Complex Sequence-to-Sequence Neural Network . ... Example(s): Model Architecture-Specific Sequence-to-Sequence Neural Networks : RNN-based Neural Networks : Jordan's Recurrent Neural Network (1986) , one of the earliest sequence-to-sequence models proposed by Michael I. Jordan, design

Ph. D. Thesis 8. Results � Growing Neural Network Framework 8.2. Application of the Growing Neural Networks 8.1. Modifications of the Growing Neural Network Algorithm 8.2. Application of the Growing Neural Networks 8.3. Growing Neural Network Algorithm Frameworks 8.4. Applications of the Growing Neural Network Frameworks 8.5. Conclusions and Comparison of the Different Methods ## 8.2. Application of the Growing Neural Networks For the application of the growing neural net algorithm, the calibration d

How to prepare a training set for neural network prediction: data splitting, normalization, input window selection, and avoiding overfitting

I have a (beginners) question about using Bayesian neural networks for classification. In the tutorial on Bayesian neural networks on the website, the output of the neural network is fed into a Gaussian random variable l

Search Log In Neural Networks learn Bloom Filters Alex Gibson 9 May 2026 20:32 UTC 64 points 1 comment 12 min read LW link Interpretability (ML & AI)  Contents Overview: The Task: Construction: Formal construction: Analysis of a single forward pass: Training: Behavioural analysis of the trained network: Mechanistic analysis of the trained network: Conclusion /​ Reflections: Related work: Further work: Overview: We train a tiny ReLU network to output sparse top- distributions over a vocabulary much

Modern deep learning models with great expressive power can be trained to overfit the training data but still generalize well. This phenomenon is referred to as \textit{benign overfitting}. Recently, a few studies have attempted to theoretically understand benign overfitting in neural networks. However, these works are either limited to neural networks with smooth activation functions or to the neural tangent kernel regime. How and when benign overfitting can occur in ReLU neural networks remains an open pr

On the difficulty of training Recurrent Neural Networks, Recurrent

Discrete-time recurrent neural networks for grammatical inference