Neural Network における "Attention" の概念をうみだした機械翻訳の論文 "Neura

Neural networks are interpreted as dynamical systems on a latent manifold, enabling the analysis of generalization, prior knowledge extraction, and out-of-distribution

Chord Intelligence mkIV: Training the Deep Neural Network Thursday, November 12, 2020 • Chris Liscio The software that trains Capo’s chord detection engine can learn from hundreds of songs per minute, and chews through more than a month worth of audio in a little over an hour. This throughput is possible thanks to a combination of GPU hardware, and the data pump that keeps it busy. Talking to the GPU Using Apple’s MPS (Metal Performance Shaders) neural network APIs, I only need to describe a deep

Defining hyperparameters effectively is crucial for improving neural network performance Includes practical examples and decisions for master neural network performance

Chronic pain and the interoceptive and control networks From How Emotions Are Made Chapter 10 endnote 26, from How Emotions are Made: The Secret Life of the Brain by Lisa Feldman Barrett . Some context is: Emotion, acute pain, chronic pain, and stress are constructed in the same networks, the same neural pathways to and from the body, and most likely the same primary sensory region of cortex, so it is completely plausible that we distinguish emotion and pain by concept ​— ​that is, via the concepts

This paper presents SEAL, a GNN-based framework that learns link prediction heuristics from local subgraphs, boosting accuracy and scalability beyond traditional methods.

A neural network is a stack of mathematical layers that learn to map inputs to outputs by adjusting millions of small numbers. Despite the name, it doesn't work like biological brains — but the metaphor was sticky and the math turned out to work

Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. Here, to observe the effect of these processes, we introduce the spatially embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a three-dimensional Euclidean space, where the communication of

The widespread finding of neural populations apparently tuned to specific, identifiable fea- tures of our external environment (e.g., faces, places, speech) suggests that brains may favor certain representational axes over others. But despite decades of research, we have no formal understanding of whether and why brains use privileged bases for representing the natural world. Here, we develop a formal framework for investigating the extent to which a repre- sentational system has privileged axes. First, we

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