One of the main problems encountered so far with recurrent neural networks is that they struggle to retain long-time information dependencies in their recurrent connections. Neural Turing Machines (NTMs) attempt to mitigate this issue by providing the neural network with an external portion of memory, in which information can be stored and manipulated later on. The whole mechanism is differentiable end-to-end, allowing the network to learn how to utilise this long-term memory via stochastic gradient descent

What Neural Operator Structural Health Monitoring Actually Means Neural operator structural health monitoring refers to the application of neural

NeurIPS Proceedings Search Character-level Convolutional Networks for Text Classification Xiang Zhang, Junbo Zhao, Yann LeCun Advances in Neural Information Processing Systems 28 (NIPS 2015) Abstract This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered aga

If you're interested in learning about artificial intelligence and related technologies, you may have come across the terms "neural network," "deep learning

Leveraging Radial Basis Function Networks for Enhanced Enterprise AI Decision-Making. Leveraging Radial Basis Function Networks for Enhanced Enterprise

Home Page Papers Submissions Editorial Board Special Issues Open Source Software Proceedings (PMLR) Data (DMLR) Transactions (TMLR) Search Statistics Login Frequently Asked Questions Contact Us Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be Valerio Biscione, Jeffrey S. Bowers; 22(229):1−28, 2021. Abstract When seeing a new object, humans can immediately recognize it across different retinal locations: the internal object representation is invariant to translation

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts (2017) ( arxiv.org ) 60 points by georgehill on Dec 8, 2023 | hide | past | favorite | 10 comments filterfiber on Dec 8, 2023 | next [–] > Previous State-of-the-Art: [...] The number of parameters in the LSTM layers of these models vary from 2 million to 151 million. > We present model architectures in which a MoE with up to 137 billion parameters Back in 2017 most models were well under 1B, GPT2 (2019) was one of the first "big

# A new type of deep neural network that has no layers Hello Algorithm readers, If there’s one thing you learn from spending a week with AI researchers, it’s how much uncertainty exists in the field. We still don’t really know how neural networks work, how to improve their accuracy (besides just feeding them more data), or how to fix their biases. But bit by bit, people are working together to find answers to these questions. It’s both terrifying and exciting to observe the frontlines. At NeurIPS

This video covers the architecture of a Convolutional Neural Network, focusing on the concept of "filters

Using the weights of trained Neural Network (NN) models as data modality has recently gained traction as a research field - dubbed Weight Space Learning (WSL). Multiple recent works propose WSL