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Explore spreading activation in psychology, its role in neural networks, cognitive processes, and future applications in neuroscience and AI
Opening the black box of deep neural networks via information Schwartz-Viz & Tishby, ICRI-CI 2017 In my view, this paper fully justifies all of the excitement surrounding it. We get three things here: (i) a theory we can use to reason about what happens during deep learning, (ii) a study of DNN learning during training
Recent theoretical works based on the neural tangent kernel (NTK) have shed light on the optimization and generalization of over-parameterized networks, and partially bridge the gap between their practical success and classical learning theory. Especially, using the NTK-based approach, the following three representative results were obtained: (1) A training error bound was derived to show that networks can fit any finite training sample perfectly by reflecting a tighter characterization of training speed de
Abstract page for arXiv paper 2201.05293: Structure Enhanced Graph Neural Networks for Link Prediction
The Android Neural Networks API (NNAPI) is a C API for running computationally intensive machine learning operations on Android devices, designed to serve as a base layer for higher-level frameworks like TensorFlow Lite, though it was deprecated in Android 15
In this work, we argue that this underlying cause is the binding problem: The inability of existing neural networks to dynamically and flexibly bind information that is distributed throughout the network. The binding problem affects their a
Building and Training Neural Networks in Emacs Lisp Yes you read that correctly. This blog post showcases nn.el , my (zero dependency) emacs lisp package that allows you to build and train neural networks. To answer the initial question as to “why on God’s green earth would you write a machine learning framework in emacs lisp?”, the answer is really just for fun (and to cause a bit of a splash, i mean emacs lisp is the last thing you’d associate with AI right now). This particular flavour is really
AI: Artificial Intelligence. Index AI: Artificial Neural Network is Nasty (2016) By Xah Lee. Date: 2016-03-13. Last updated: 2020-09-21. artificial neural network is nasty. Google's got TensorFlow, and SyntaxNet, AlphaGo … all based on neural networks aka “deep learning”. When neural networks solves a problem, such as language translation, it undestands nothings, and we can learn nothing from it. No theory, no reasoning, nothing. Zero. It simply shot a answer back at you. it doesn't have any insights
There’s a neural network framework called pix2pix that can be trained to learn how to transform one type of image into another. It’s been used for example to convert satellite images into line drawings and vice versa (think Google maps satellite view vs the one that show boxes where all the buildings are