Posts about neural netwoks written by Ian Channing

Network engineering has spent the better part of a decade trying to figure out how to fit artificial intelligence into workflows.

I have been meaning for a while to establish a setup to implement neural network based algorithms on smaller microcontrollers. After reviewing existing solutions, I felt there is no solution that I really felt comfortable with. One obvious issue is that often flexibility is traded for overhead. As always, for a really optimized solution you

Kiran Gopinathan Computer Science PhD Candidate @ NUS Formal Methods and Programming Languages Gop-Net - Neural Networks in C August 23 2017 #c #projects #machine #learning During the first year of my undergraduate, I had relatively little exposure to Computer Science topics, and this had lead me to develop an infatuation with programming in C (against my better knowledge, but that's a story for another time). Given the popularity of Machine learning at the time (it was being heavily pushed on us as undergr

Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critical scenarios that require knowing why certain links are predicted. Despite various methods proposed for the explainability of GNNs, most of them are post-hoc explainers developed for explaining node classification. Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc ex

The combination of linear transformations and nonlinear activation functions forms the foundation of most modern deep neural networks, enabling them to approximate highly

NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center Paper ID: 3292 Title: Intrinsic dimension of data representations in deep neural networks ### Reviewer 1 The paper examines the intrinsic dimension of representations learned by neural networks. This direction is a natural and worthwhile one, in order to understand the behaviour and complexity of DNNs from a geometric angle. The paper is reasonably clearly written overall. Treatment of related work is brief and not well

Generative adversarial networks (GANs), formed in 2014 [1], is a state of the art deep neural network with many applications. Unlike the

Skip to main content Breadcrumb Papers Predictive coding networks for temporal prediction. Predictive coding networks for temporal prediction. Our brains are constantly trying to predict the next moment based on current and past experiences. We developed a computer model that captures this ability of our brains to predict over time. Our model can learn and predict changes in the environment almost as accurately as advanced mathematical systems, but with a simpler approach that mimics natural brain activitie

Aller au contenu principal Pierre Yger Computational neuroscience and cortical plasticity Recherche Menu principal Research Neural Networks Cortical Plasticity Software Notebooks 2D network Realistic networks [bibshow file=library.bib key_format=cite] [wpcol_1half id="" class="" style=""] Patchy lateral connectivity in macaque primary visual cortex: axons from an injection labelling 320 cells in the superficial layers of V1 are super-imposed upon the optical imaging orientation map for that portion of corte