Exploring Complex Networks - Strogatz 2001 Network anatomy is important to characterize because structure always affects function... Written in 2001, this article - recently recommended by Werner Vogels in his 'Back-to-Basics' series - explores the topic of complex networks. It turns out that the behaviour of individual nodes, and the way that we connect them
Bob Atkey Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers (2022) PDF link: Matthew L. Daggit, Wen Kokke, Robert Atkey, Luca Arnaboldi, and Ekaterina Komendantskaya. Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers. ArXiv. 2022. ArXiv: 2202.05207 Abstract Verification of neural networks is currently a hot topic in automated theorem proving. Progress has been rapid and there are now a wide range of tools available that can verify properties of netwo
# Graph Neural Networks MeSH Descriptor Data 2026 - MeSH Heading - Graph Neural Networks - Tree Number(s) - G17.485.688 - L01.224.050.375.605.688 - Unique ID D000098422 - RDF Unique Identifier - http://id.nlm.nih.gov/mesh/D000098422 - Scope Note Neural networks designed to process signals supported on graphs. - Previous Indexing - Neural Networks, Computer (2009-2024) - Public MeSH Note 2025 - History Note 2025 - Date Introduced - 2025/01/01 - Last Updated - 2025/01/01 - Neural Networks, Computer [G17.485
We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction defined as the firing of neurons in specific paths
Wherein Neural Networks Are Presented as Layers Defined by Fixed‑point Optimisations, and Gradients Are Obtained via the Implicit Function Theorem, With Convex Optimisation Layers Exposed as Differentiable Modules
There has been an increasing interest in modeling continuous-time dynamics of temporal graph data. Previous methods encode time-evolving relational information into a low-dimensional representation by specifying discrete layers of neural networks, while real-world dynamic graphs often vary continuously over time. Hence, we propose Continuous Temporal Graph Networks (CTGNs) to capture the continuous dynamics of temporal graph data. We use both the link starting timestamps and link duration as evolving inform
The text discusses the challenges and techniques involved in training neural networks for various tasks, including multi-layer networks, backpropagation, optimization, overfitting, and object recognition. It covers the use of different learning methods such as mini-batch training, weight decay, and early stopping to improve model performance. The text also explores the application of neural networks in natural language processing, speech recognition, and computer vision, highlighting the importance of featu
Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs mainly excel in leveraging short-range interactions (SRI) but struggle to
In the previous article, we explored the reward system in reinforcement learning In this article, we... Tagged with ai, machinelearning.
Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space. Motivated by the premise that the tuning of individual units may be important, there has been recent interest in developing stricter notions of representational (dis)similarity that require neurons to be individually matched across networks. When two networks have the same size (i.e. same number of neurons), a distance metric can be formulated by optimizing o