Convolutional Neural Networks (CNN) has been a go-to model when it comes to image classification, object detection and many other

15 organizations in our directory are tagged Neural Network. Neural network is a computational model built from interconnected layers of artificial neurons

Abstract page for arXiv paper 1701.03441: Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks

Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks Christopher J. Cueva , Peter Y. Wang , Matthew Chin , Xue-Xin Wei Tuesday: Biology and ML Abstract: Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different. Her

optimization methods and other advanced topics, the reader is referred to other existing resources. In particular, the book by Bengio et al (2015) is highly recommended. Scope The focus is on applications of neural networks to language processing tasks. How-ever, some subareas of language processing with neural networks were decidedly left out of

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational

NeurIPS Proceedings Search Mutual exclusivity as a challenge for deep neural networks Kanishk Gandhi, Brenden M Lake Advances in Neural Information Processing Systems 33 (NeurIPS 2020) Abstract Strong inductive biases allow children to learn in fast and adaptable ways. Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming that if an object has one label then it does not need another. In this paper, we investigate whether or not vanilla neural architectures h

Recent technological advances have enabled measurement of the synaptic wiring diagram, or ‘connectome’, of large neural circuits or entire brains. However, the extent to which such data constrain models of neural dynamics and function is debated. In this study, we developed a theory of connectome-constrained neural networks in which a ‘student’ network is trained to reproduce the activity of a ground truth ‘teacher’, representing a neural system for which a connectome is available. Unlike

Range Queries and Updates, Neural Networks, and Constants in Go — John Bokma's tumblelog

Stochastic Gradient Descent is an optimizer algorithm that minimizes the loss function in machine learning and deep learning models.