Abstract page for arXiv paper 2003.04881: Pruned Neural Networks are Surprisingly Modular
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Thursday, March 4 , 2021 # Multimodal Neurons in Artificial Neural Networks We’ve discovered neurons in CLIP that respond to the same concept whether presented literally, symbolically, or conceptually. This may explain CLIP’s accuracy in classifying surprising visual renditions of concepts, and is also an important step toward understanding the associations and biases that CLIP and similar models learn. Through a series of carefully-constructed experiments, we demonstrate that we can exploit this
This post is the first of a three-part series in which we set out to derive the mathematics behind feedforward neural networks. They have
Researchers from Google, NYU, and U. Montreal demonstrated that deep neural networks are highly susceptible to imperceptible input perturbations, termed "adversarial examples," which cause
This paper introduces an efficient quantization scheme for neural networks that enables integer-only inference, reducing latency and memory usage on edge devices
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 Deep Learning The History of Convolutional Neural Networks for Image Classification (1989- Today) A tour through the history of Computer Vision! Avishek Biswas Jun 28, 2024 18 min read Share A visual tour of the greatest innovations in Deep Learning and Computer Vision. Before CNNs, the standard way to train a neural network to classify i
Backpropagation, also known as backward propagation, is an algorithm commonly used in neural networks. It performs a backward pass to adjust the model's parameters with the aim of minimizing the error. In our post, you can learn how forward and backward propagation work, discover the different types of backpropagation, and find out how to use it in your neural networks
DeepMind’s research shows how to understand the role of individual neurons in a neural network
Introduces a browser-based sandbox for building, training, visualizing, and experimenting with neural networks. Includes background information on the tool, usage information, technical implementation details, and a collection of observations and findings from using it myself