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Inductive biases of neural network modularity in spatial navigation

ML @ CMU

We use a model-free actor-critic approach to learning, with the actor and critic implemented using distinct neural networks. Since computing beliefs about the evolving state requires integrating evidence over time, a network capable of computing belief must possess some form of memory.

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Kneron’s auto-grade KL730 NPU chip revolutionises edge AI

AI News

With its unprecedented efficiency and support for transformer neural networks, we are empowering users across industries to unlock the full potential of AI without compromising on data privacy and security.” A simple re-appropriation of adjacent technologies, such as graphics-dedicated GPU chips, simply isn’t going to do the job.

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Convolutional neural network for colorimetric glucose detection using a smartphone and novel multilayer polyvinyl film microfluidic device

Flipboard

Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms.

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An Overview of Advancements in Deep Reinforcement Learning (Deep RL)

Marktechpost

Image Source One of the first successful applications of RL with neural networks was TD-Gammon, a computer program developed in 1992 for playing backgammon. The computer player is a neural network trained using a deep RL algorithm, a deep version of Q-learning called deep Q-networks (DQN), with the game score as the reward.

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The Intuition behind Adversarial Attacks on Neural Networks

ML Review

Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3%

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Why GPUs Are Great for AI

NVIDIA

An AI model, also called a neural network, is essentially a mathematical lasagna, made from layer upon layer of linear algebra equations. The latest GPUs include Tensor Cores that are 60x more powerful than the first-generation designs for processing the matrix math neural networks use.

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A Guide to Convolutional Neural Networks

Heartbeat

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.