Learning to protect communications with adversarial neural cryptography Abadi & Anderson, arXiv 2016 This paper manages to be both tremendous fun and quite thought-provoking at the same time. If I tell you that the central cast contains Alice, Bob, and Eve, you can probably already guess that we're going to be talking about cryptography (that
Batch normalization is a deep learning technique that normalizes activations per mini-batch, accelerating convergence and boosting accuracy across various tasks.
Settings About How Emotions Are Made Search Intrinsic networks Watch Chapter 4 endnote 5, from How Emotions are Made: The Secret Life of the Brain by Lisa Feldman Barrett . Some context is: Intrinsic networks are considered one of neuroscience’s great discoveries of the past decade. An intrinsic brain network is a population of neurons that fire synchronously (in the same pattern) so that their firing is strongly related over time. [1] [2] The neurons that make up an intrinsic network coordinate their
Skip to content Publish AI, ML & data-science insights to a global community of data professionals. Sign in Submit an Article Toggle Mobile Navigation Toggle Search Search Artificial Intelligence What is Neural Architecture Search? And Why Should You Care? A Neural Network created by an algorithm Thomas G. Sep 18, 2021 6 min read Share Photo by CDC on Unsplash Democratization of Deep Learning The use of deep learning models is becoming more and more democratic every day and is becoming indispensable in many
We propose Attentive Regularization (AR), a method to constrain the activation maps of kernels in Convolutional Neural Networks (CNNs) to specific regions of interest (ROIs). Each kernel learns a location of specialization along with its weights through standard
Abstract page for arXiv paper 2302.14040: Permutation Equivariant Neural Functionals
Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks PLoS Computational Biology, July 2015 DOI 10.1371/journal.pcbi.1004347 26217933 , Alison L. Barth , Ziv Bar-Joseph Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since a
Author Summary Two observations about the cortex have puzzled and fascinated neuroscientists for a long time. First, neural responses are highly variable. Second, the level of excitation and inhibition received by each neuron is tightly balanced at all times. Here, we demonstrate that both properties are necessary consequences of neural networks representing information reliably and with a small number of spikes. To achieve such efficiency, spikes of individual neurons must communicate prediction errors abo
Understanding neural network training dynamics at scale is an important open problem. Although realistic model architectures, optimizers, and data interact in complex ways
Yup, it's a very good question. I'm thinking of a very specific definition of learning, Tom Mitchell's definition, which is basically any system that gets better at doing some task when exposed to more experience. In this sense, the NN underlying the LLM is definitely not learning, but the chatbot as a system can be seen as a learning system, because with more experience (more data to do RAG from) it gets better at answering questions. So yes, nuances :)