The text discusses a conversation about neural networks, training methods, and the use of tools like TensorBoard. It also touches on programming languages and a package related to hidden Markov models
Introduction This is week 3 of Quintin's Alignment Papers Roundup. This week, I'm focusing on papers that use interpretability to guide a neural netw
New research on brain dynamics in depression shows a significant shift in neural activity, with less time spent in self-focus networks and more in sensory states
This explores Titans-style neural memory modules that decide what to store by surprise — and where that idea sits among the corpus's other answers to 'how should a model remember across long horizons
Select Page BI 183 Dan Goodman: Neural Reckoning Facebook Twitter LinkedIn Brain Inspired BI 183 Dan Goodman: Neural Reckoning Play Episode Pause Episode 1x 00:00 / 01:28:54 Subscribe Share RSS Feed Share Link Embed <blockquote class="wp-embedded-content" data-secret="B5uxNZmPgt"><a href="https://braininspired.co/podcast/183/">BI 183 Dan Goodman: Neural Reckoning</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://braininspired.co/podcast/183/embed/#?secret=B5uxNZmPgt" width
Deep Learning Course Lecture 01: Introduction to Neural Networks Lecture 02: Training Neural Networks Lecture 03: Architecture Lecture 04: Tuning Lecture 05: CNN Section 1: Convolutional Neural Networks (CNN) & Transfer Learning Section 1: Convolutional Neural Networks (CNN) & Transfer Learning Section 1 Questions Convolutions Finite Length Discrete Convolutions 1D Convolution Example – Zero Padding 1D Convolution Example – Zero Padding and Strides 2D Convolutions 2D Convolutions with Padding and
We analyze the generalization properties of two-layer neural networks in the neural tangent kernel (NTK) regime, trained with gradient descent (GD). For early stopped GD we derive fast rates of convergence that are known to be minimax optimal in the framework of non-parametric regression in reproducing kernel Hilbert spaces. On our way, we precisely keep track of the number of hidden neurons required for generalization and improve over existing results. We further show that the weights during training remai
NeurIPS 2020 Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning Review 1 Summary and Contributions: [edit: see "additional feedback" section for response to author rebuttal] This paper considers the problem of learning representations of specific subgraphs of a larger graph, in such a way that subgraphs of non-isomorphic graphs should be assigned different representations. Specifically, they note that most GNNs can only distinguish things that the 1-WL test ca
Abstract page for arXiv paper 2005.08129: Neural Collaborative Reasoning
In this video, we explain the concept of training an artificial neural network