What are some open theoretical questions about the capabilities and limitations of recurrent neural networks?

Jan 1, 2025 ... RNN works as a series of time-unfolding events. Each time the neural network is triggered, it demands an activation function to activate its ... an ANN is a type of Neural Network. ANN is essentially a feed-forward network because of the way information travels through it — from one layer to another ... Jun 22, 2024 ... An RNN cell contains multiple neurons arranged in such a way that they can maintain and update a hidden state and it has recurrent connections. Feb 10, 2019 ... The per-time-step outputs of R1 are not important. For R2, it's inputs are the last word it output; i.e., xt=at−1 for R2. The sequence a=(a1,…,a ... LSTM networks are a specialized form of the RNN architecture. RNNs use past information to improve the performance of a neural network on current and future ... Jun 6, 2017 ... 3 Answers 3 · RNN's "batch size" is to speed up computation (as there're multiple lanes in parallel computation units); it's not mini-batch for ... Jul 17, 2024 ... xLSTM represents a significant evolution in the LSTM architecture, addressing past limitations and setting a new standard for sequence modeling. Feb 7, 2021 ... ... Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Machine Learning, Internet of Things (IoT), Ultra Low Power, Approximate ... Discover this question from the Deep learning MCQ, addressing the following topics: neural networks, recurrent neural networks. What is Recurrent Neural Network. Chapter 3. Histopathological Image Analysis in Medical Decision Making. A recurrent neural network is an artificial neural ...