10. Modern Recurrent Neural Networks navigate_next 10.4. Bidirectional Recurrent Neural Networks search Quick search code Show Source Table Of Contents 1. Introduction 2. Preliminaries 2.1. Data Manipulation 2.2. Data Preprocessing 2.3. Linear Algebra 2.4. Calculus 2.5. Automatic Differentiation 2.6. Probability and Statistics 2.7. Documentation 3. Linear Neural Networks for Regression 3.1. Linear Regression 3.2. Object-Oriented Design for Implementation 3.3. Synthetic Regression Data 3.4. Linear Regression
Uncertainty estimation in Neural Networks (NNs) is vital in improving reliability and confidence in predictions, particularly in safety-critical applications. Bayesian Neural Networks (BayNNs) with Dropout as an approximation offer a systematic approach to quantifying uncertainty, but they inherently suffer from high hardware overhead in terms of power, memory, and computation. Thus, the applicability of BayNNs to edge devices with limited resources or to high-performance applications is challenging. Some o
Spiking neural networks become robust to various perturbations of their parameters if their voltages are confined to a lower-dimensional subspace, and both dynamics and robustness can be visualised in this voltage subspace
← Unraveling Human-AI Teaming: A Review and Outlook International Scientific Report on the Safety of Advanced AI (Interim Report) → # Learning in Spiking Neural Networks with a Calcium-based Hebbian Rule for Spike-timing-dependent Plasticity 投稿日: 2025年4月10日 作成者: jarxiv
We introduce Deduction-Projection Estimators: a family of methods for measuring properties of neural networks inspired by the notion of a "deductive heuristic estimator" introduced in Christiano et al. (2022). Unlike traditional techniques used in machine learning, a DPE produces its estimate by mechanistically tracking how activations are processed throughout a neural network. This allows us to understand how a model behaves over an entire input distribution without having to generalize from observed behav
Backbone in Deep Learning: How to Train Neural Networks - A comprehensive guide to understanding and using the backbone in deep learning
# Using Feature Weights to Improve Performance of Neural Networks Iqbal, Ridwan Al (2011) Using Feature Weights to Improve Performance of Neural Networks. [Preprint] PDF - Submitted Version 166Kb Different features have different relevance to a particular learning problem. Some features are less relevant; while some very important. Instead of selecting the most relevant features using feature selection, an algorithm can be given this knowledge of feature importance based on expert opinion or prior learn
mailitics Deep Neural Networks as Iterated Function Systems and a Generalization Bound Deep Neural Networks as Iterated Function Systems and a Generalization Bound arXiv:2601.19958v1 Announce Type: new Abstract: Deep neural networks (DNNs) achieve remarkable performance on a wide range of tasks, yet their mathematical analysis remains fragmented: stability and generalization are typically studied in disparate frameworks and on a case-by-case basis. Architecturally, DNNs rely on the recursive application of
Understanding LLM Architecture A Practical Guide to Neural Networks - Delve into the core of LLM architecture. This guide breaks down the complex world of neural networks, offering a practical and accessible understanding of how these models function. Uncover the key principles and components that drive LLMs, making advanced concepts easy to grasp
Distill Computing Receptive Fields of Convolutional Neural Networks Mathematical derivations and open-source library to compute receptive fields of convnets, enabling the mapping of extracted features to input signals. Authors Affiliations André Araujo Google Research Wade Norris Perception Labs Jack Sim Google Research Published Nov. 4, 2019 DOI 10.23915/distill.00021 Contents Overview of the article Problem setup Single-path networks Arbitrary computation graphs Discussion: receptive fields of modern