I'm interested in getting people reactions to this paper: The world as a neural network. Here's a fragment of the abstract that omits the technical parts: We discuss a possibility that the entire universe on its most fundamental level is a neural network...This shows that the learning dynamics of a neural network can indeed exhibit

「メモリネットワーク」は代表的な記憶装置付きニューラルネットワークである． 本稿ではメモリモデル (記憶装置付きニューラルネットワーク) をいくつか概説し，論文 2 紙 (1) Memory Networks, (2) Towards AI-Complete Question Answering

这次回顾ECE408 Lecture 12，这次介绍了卷积神经网络。 课程主页： https://wiki.illinois.edu/wiki/display/ECE408 搬运视频： https://www.youtube.com/playlist?list=PL6RdenZrxrw-UKfRL5smPfFFpeqwN3Dsz

The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated pla

A novel deep-learning framework shows how to interpret and decode raw neural recordings, avoiding the need for strong prior hypotheses, revealing a novel representation of head direction

"The answer to that question matters enormously. If you think biological neural networks are doing something special — that they contain some secret sauce that computers lack — you’ll tend to have strong priors against AGI being around the corner." Good point

Neural network models are artificial intelligence (AI) programs inspired by the biology of the human brain that allow machines to make intelligent decisions. Learn about different types of neural network models and how they work—and can work for

A major goal in systems neuroscience is to build computational models that capture the primate brain’s internal representations. Standard evaluations of artificial neural networks (ANNs) emphasize forward predictivity—how well model features predict neural responses—without testing whether model representations are themselves predictable from neural activity. Here we develop a diagnostic metric, reverse predictivity, that quantifies how well macaque inferior temporal cortex responses predict ANN unit

Portfolio and writing on data and software

pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration