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Understand GNNs that operate on graph-structured data, learning through message passing between nodes and edges.
Abstract page for arXiv paper 2307.13609: Dendritic Integration Based Quadratic Neural Networks Outperform Traditional Aritificial Ones
Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown
# CHAPTER 4 ## NEURAL NETWORKS We have discussed evolution in ecosystems, from a self-organizational rather than a strict Darwinist point of view. It is time to move on to our main topic of interest: the structure of the brain and the mind. In this chapter we shall begin our trek in this direction, with a crude yet tantalizing model of the brain: the formal neural network. Contemporary brain science is extremely fragmented. Before one can even attempt to understand it, one must accept that every complex
Opening the black box of deep neural networks via information Schwartz-Viz & Tishby, ICRI-CI 2017 Yesterday we looked at the information theory of deep learning, today in part II we’ll be diving into experiments using that information theory to try and understand what is going on inside of DNNs. The experiments are done on a
A practitioner's blog on Graph Neural Networks — message passing, GCN/GAT/GraphSAGE/GIN, real applications, and where the field is heading in 2026
← Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory Analysis of Distributed Optimization Algorithms on a Real Processing-In-Memory System → # Using Neural Networks to Model Hysteretic Kinematics in Tendon-Actuated Continuum Robots 深層学習アプローチを使用して、腱で作動する連続体ロボットの機械的ヒステリシス動作を正確にモデル化する機能は、ますます関心が高まっている分野です。 この論文では、2
NIPS 2018 Sun Dec 2nd through Sat the 8th, 2018 at Palais des Congrès de Montréal Paper ID: 1911 Title: Learning sparse neural networks via sensitivity-driven regularization Reviewer 1 This paper studies the sensitivity-based regularization and pruning of neural networks. Authors have introduced a new update rule based on the sensitivity of parameters and derived an overall regularization term based on this novel update rule. The main idea of this paper is indeed novel and interesting. The paper is
Announcing the release of TensorFlow GNN 1.0, a production-tested library for building GNNs at Google scale, supporting both modeling and training.