convolution – Andrew Fairless, Ph.D.

What I Read: Pooling, Graph Neural Networks

By Andrew Fairless on April 7, 2025December 29, 2024

https://gnn-pooling.notion.site/1-3-pooling-in-graph-neural-networks Pooling in Graph Neural NetworksBy FILIPPO MARIA BIANCHIDecember 2024 “Despite their success, scaling GNNs to handle larger and more complex graphs remains a significant challenge. Graph pooling plays aContinue readingWhat I Read: Pooling, Graph Neural Networks

What I Read: Mamba, State Space

By Andrew Fairless on February 11, 2025October 25, 2024

https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mamba-and-state A Visual Guide to Mamba and State Space ModelsMaarten GrootendorstFeb 19, 2024 “To further improve LLMs, new architectures are developed that might even outperform the Transformer architecture. One ofContinue readingWhat I Read: Mamba, State Space

What I Read: Kernel, Convolutional Representations

By Andrew Fairless on October 24, 2024July 22, 2024

https://logb-research.github.io/blog/2024/ckn Kernel Trick I – Deep Convolutional Representations in RKHSOussama Zekri, Ambroise OdonnatJuly 18, 2024 “…we focus on the Convolutional Kernel Network (CKN) architecture proposed in End-to-End Kernel Learning withContinue readingWhat I Read: Kernel, Convolutional Representations

What I Read: What can LLMs never do?

By Andrew Fairless on October 1, 2024July 14, 2024

https://www.strangeloopcanon.com/p/what-can-llms-never-do What can LLMs never do?Rohit KrishnanApr 23, 2024 “Every time over the past few years that we came up with problems LLMs can’t do, they passed them with flyingContinue readingWhat I Read: What can LLMs never do?

What I Read: Mamba, Easy Way

By Andrew Fairless on May 9, 2024March 7, 2024

https://jackcook.com/2024/02/23/mamba.html Mamba: The Easy WayJack CookFebruary 23, 2024 “Mamba appears to outperform similarly-sized Transformers while scaling linearly with sequence length…. If… you’re looking for a higher-level overview of Mamba’s bigContinue readingWhat I Read: Mamba, Easy Way

What I Read: Convolutions, Probability

By Andrew Fairless on February 13, 2023December 16, 2022

https://www.countbayesie.com/blog/2022/11/30/understanding-convolutions-in-probability-a-mad-science-perspective Understanding Convolutions in Probability: A Mad-Science PerspectiveWill Kurt, Count BayesieDecember 01, 2022 “If you’ve ever done much mad science you’ll know that a major problem is selectively determining theContinue readingWhat I Read: Convolutions, Probability

What I Read: Beyond Message Passing, Graph Neural Networks

By Andrew Fairless on June 6, 2022May 23, 2022

https://thegradient.pub/graph-neural-networks-beyond-message-passing-and-weisfeiler-lehman/ Beyond Message Passing: a Physics-Inspired Paradigm for Graph Neural NetworksMichael Bronstein07.May.2022 “…the “node and edge-centric” mindset of current graph deep learning schemes imposes strong limitations… we propose physics-inspired “continuous”Continue readingWhat I Read: Beyond Message Passing, Graph Neural Networks

What I Read: Understanding Convolutions on Graphs

By Andrew Fairless on September 29, 2021September 21, 2021

https://distill.pub/2021/understanding-gnns/ Understanding Convolutions on GraphsUnderstanding the building blocks and design choices of graph neural networks.Ameya DaigavaneBalaraman RavindranGaurav AggarwalGoogle ResearchSept. 2, 202110.23915/distill.00032 “Graph neural networks (GNNs) are a family of neuralContinue readingWhat I Read: Understanding Convolutions on Graphs

What I Read: Deep learning, black box

By Andrew Fairless on March 21, 2021March 7, 2021

https://bdtechtalks.com/2021/01/11/concept-whitening-interpretable-neural-networks/ Deep learning doesn’t need to be a black boxBen DicksonJanuary 11, 2021 “The innerworkings of neural networks are often a mystery…. scientists at Duke University propose “concept whitening,” aContinue readingWhat I Read: Deep learning, black box

What I Read: Interpretation for Image Recognition

By Andrew Fairless on March 12, 2021March 7, 2021

https://thegradient.pub/a-visual-history-of-interpretation-for-image-recognition/ A Visual History of Interpretation for Image Recognition16.Jan.2021by Ali Abdalla “…during the last decade, researchers developed many different methods to open the “black box” of deep learning, aiming toContinue readingWhat I Read: Interpretation for Image Recognition

Tag: convolution