Abstract page for arXiv paper 1611.01576: Quasi-Recurrent Neural Networks
Skip to content QUANTITATIVE RESEARCH AND TRADING The latest theories, models and investment strategies in quantitative research and trading Menu Home Systematic Strategies About Tag: Neural Networks Posted on March 14, 2026March 14, 2026 Transformer Models for Alpha Generation: A Practical Guide A Practical Guide to Attention Mechanisms in Quantitative Trading Introduction Quantitative researchers have always sought new methods to extract meaningful signals from noisy financial data. Over the past decade
This paper uses NNGP theory to quantify uncertainty in infinite-width neural networks, achieving better calibration and reliability than finite-width models
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← Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning Confidence-based Estimators for Predictive Performance in Model Monitoring → # GraphXAIN: Narratives to Explain Graph Neural Networks 投稿日: 2025年2月13日 作成者: jarxiv グラフニューラルネットワーク（GNNS）は、グラフ構造データの機械学習の強力な手法ですが、解釈可能性に課題をもたらします。 既存のGNN説明方法は通常
NeurIPS Proceedings Search Reversible Recurrent Neural Networks Matthew MacKay, Paul Vicol, Jimmy Ba, Roger B Grosse Advances in Neural Information Processing Systems 31 (NeurIPS 2018) Abstract Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which can be trained. Reversible RNNs---RNNs for which the hidden-to-hidden transition can be reversed---offer a path to reduce the memory requi
\(\newcommand{\latexonlyrule}[2]{}\) A Visual Exploration of Random Neural Networks Tyler Neylon Published 163.2022 (first version written in 2018) This article is an illustrated tour of neural networks in their primordial, untrained state. Neural networks are notoriously difficult beasts to understand. My aim is to provide a peek into the inherent beauty of this challenging world, and to build your intuition for how to set up neural networks through informed hyperparameter choices. I assume that you know n
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# Forecasting Monthly Airline Passenger Numbers with Quasi-Randomized Neural Networks Posted on June 17, 2024 by T. Moudiki in Data science | 0 Comments This article was first published on T. Moudiki's Webpage - Python , and kindly contributed to python-bloggers . (You can report issue about the content on this page here ) This post is about forecasting airline passenger numbers with quasi-randomized neural networks, and most specifically using nnetsauce ’s class MTS. MTS stands for ‘Multivariate Time