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.ICDT.2025.9 Query Languages for Neural Networks Authors Martin Grohe
Abstract page for arXiv paper 2408.15136v1: Low-Budget Simulation-Based Inference with Bayesian Neural Networks
Skip to content TheLinuxCode Software Menu Toggle Distros Menu Toggle SysAdmin Menu Toggle Residential Proxies Residential Proxies TheLinuxCode Main Menu Menu Bidirectional Recurrent Neural Networks (BRNNs) in Practice: When Future Context Matters Leave a Comment / By Linux Code / February 2, 2026 I still remember the first time a sequence model “misread” something that any human would get right. The input was a short sentence for entity tagging, and the model had to decide whether a word was a place or
Explains how LLMs suddenly acquire capabilities through emergence. Topics include phase transitions, scaling behaviors, and the ongoing metric artifact debate.
# Non-Zero Initial States for Recurrent Neural Networks Sun 20 November 2016 The default approach to initializing the state of an RNN is to use a zero state. This often works well, particularly for sequence-to-sequence tasks like language modeling where the proportion of outputs that are significantly impacted by the initial state is small. In some cases, however, it makes sense to (1) train the initial state as a model parameter, (2) use a noisy initial state, or (3) both. This post examines the rational
We systematically explore regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve
Skip to content Stephen R. Martin, PhD Data Science. Statistics. Bayesian Nerd. Menu Posted on February 5, 2021February 5, 2021 by Stephen Martin Neural Networks in Stan: Or how I was utterly surprised that it worked at all. Feed-forward neural networks are a staple in machine learning. The basic feed-forward NN (which I’ll just call a NN from here on out) is a relatively simple idea. The tale is as old as (statistical) time: You have a set of "features" (covariates) and you want to predict an outcome
A mechanism-first reading of PaTAS, a Subjective Logic framework that treats neural-network trust as something propagated through data, parameters, and inference paths—not guessed from accuracy
jarxiv Japanese arxiv コンテンツへスキップ ホーム ← Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders Freestyle Sketch-in-the-Loop Image Segmentation → Dimensions underlying the representational alignment of deep neural networks with humans 投稿日: 2025年1月28日 作成者: jarxiv 要約 人間と人工知能（AI
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