Recurrent Neural Network
Recurrent Neural Networks (RNNs) are a class of neural networks designed to process sequential data by maintaining an internal state that is updated over time. Current research focuses on improving RNN efficiency and stability, exploring variations like LSTMs and GRUs, and investigating their application in diverse fields such as time series forecasting, natural language processing, and dynamical systems modeling. This includes developing novel architectures like selective state space models for improved memory efficiency and exploring the use of RNNs in conjunction with other architectures, such as transformers and convolutional neural networks. The resulting advancements have significant implications for various applications requiring sequential data processing, offering improved accuracy, efficiency, and interpretability.
Papers
Efficient Deep Reinforcement Learning with Predictive Processing Proximal Policy Optimization
Burcu Küçükoğlu, Walraaf Borkent, Bodo Rueckauer, Nasir Ahmad, Umut Güçlü, Marcel van Gerven
Integrated Convolutional and Recurrent Neural Networks for Health Risk Prediction using Patient Journey Data with Many Missing Values
Yuxi Liu, Shaowen Qin, Antonio Jimeno Yepes, Wei Shao, Zhenhao Zhang, Flora D. Salim
Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems
Debdipta Goswami
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes, Nadav Cohen, Amir Globerson
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection
Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang