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
Kriegeskorte and Golan introduce neural network models to biologists, demonstrating deep architectures, backpropagation, and their role in understanding brain computations
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
In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However
Having recovered somewhat from the last push on deep learning papers, it's time this week to tackle the next batch of papers from the 'top 100 awesome deep learning papers.' Recall that the plan is to cover multiple papers per day, in a little less depth than usual per paper, to give you a broad…
Neural networks are inspired by the human brain, consisting of interconnected nodes called neurons that mimic biological neural connections. Each node