Abstract page for arXiv paper 2306.17844: The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
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CSP Test --> Main Navigation ICML My Stuff Login Select Year: (2025) 2026 2025 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2002 1996 IMLS Archives Socials Exhibitors Oral Algorithm Development in Neural Networks: Insights from the Streaming Parity Task Loek van Rossem ⋅ Andrew Saxe 2025 Oral [ OpenReview ] Abstract Even when massively overparameterized, deep neural networks show a remarkable ability to generalize. Research on this phenomenon has
Cognitive Decision Neural Networks based on evidence accumulation framework enable in silico modeling of human decision-making, providing a novel approach to understanding cognitive processes
Deep compositional networks Subtopic of Deep structured models Researchers Domen Tabernik, PhD Matej Kristan, PhD Aleš Leonardis, PhD We have developed a novel form of deep neural networks that combines the advantages of compositional hierarchies and deep convolutional networks (ConvNets or CNNs). Both approaches have its benefits and its drawbacks. For instance, compositional hierarchies have explicit structure that directly explains what is a feature or a category. This makes approach partially
# CHAPTER 6 # Deep learning - Warm up: a fast matrix-based approach to computing the output from a neural network - The two assumptions we need about the cost function - The Hadamard product, $s \odot t$ - The four fundamental equations behind backpropagation - Proof of the four fundamental equations (optional) - The backpropagation algorithm - The code for backpropagation - In what sense is backpropagation a fast algorithm? - Backpropagation: the big picture In the last chapter we learned that deep neur
We present a novel method of compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters. The proposed method reduces the number of parameters of each convolutional layer by learning a 1D vector termed Filter Summary (FS). The convolutional filters are located in FS as overlapping 1D segments, and nearby filters in FS share weights in their overlapping regions in a natural way. The resultant neural network based on such weight sharing sche
7. Convolutional Neural Networks navigate_next 7.5. Pooling 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 Implementation from Scratch 3.5. Con
Neural Network Hidden Layer From GM-RKB A Neural Network Hidden Layer is a neural network layer in between the Neural Network Input Layer and the Neural Network Output Layer . Context: It is composed by Hidden Neuron that are determined by a activation function and a weight funtions . It can have a Hidden Layer State that represents a learned combination of input features (see: kernel learning ). It can range from being a Linear Hidden Layer to being a Non-Linear Hidden Layer . It can be defined mathematica