Recurrent Neural Networks

Also known as RNN

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Overview

Recurrent neural networks study how a model can process ordered inputs by repeatedly updating an internal state, which is useful for streaming data, long-term memory, and decisions under partial observability. Common approaches include gated LSTMs and GRUs, linear and state-space recurrences, continuous-time and spiking networks, and weight-shared loops for iterative reasoning; training ranges from backpropagation through time to online learning and parallel scans. Researchers test these models on state-tracking and recall tasks, language modeling, time-series forecasting, control, and physical-system prediction. A central tension is how to preserve useful information and stable learning over long sequences while keeping computation efficient and matching the accuracy and flexibility of Transformers.

Latest papers

CardsList

302 papers47 papers in the last four weeks, up 15% on the four weeks before. 0.3% of all new papers.

  1. Data-driven and physics-guided hindcasting of measured wave spectra

    Oct 21, 2026Sander RikkaTime Series ForecastingPhysics-Informed ML

    Department of Marine Systems, Tallinn University of Technology

  2. Gate Initialisation versus Recurrent Dropout and Zoneout in Learning Long Dependencies: A Controlled Study of LSTM and GRU Networks on Synthetic Tasks

    Oct 21, 2026Oleksandr Kholodniak, Oleksandr ProkhorovRecurrent Neural NetworksLong Short-Term Memory Networks

    National Aerospace University “Kharkiv Aviation Institute”, Kharkiv, Ukraine

  3. Truncate or Not? A Scaling Study of Looped Transformers with Truncated Backpropagation

    Oct 19, 2026Xuyuan Xiong, Siyu Chen, Zihan Shao +5Truncated Backpropagation Through TimeEfficient Language Model Training

    MMLab, The University of Hong Kong · Yale University

  4. A Spiking Ring-Attractor Architecture for Representation and Control of Revolute Robot Joints

    Oct 19, 2026Aung Htet, Alejandro Rodriguez Jimenez, Ruidong Ma +1Continuous Attractor Neural NetworksRobotic Control

    Sheffield Hallam University, Sheffield, UK

  5. Multi-site Learning for Forecasting Day-Ahead Wind-Power Production for Onshore Wind Farms in Great Britain with Deep Neural Networks

    Oct 18, 2026James Kean, Aidan O'SullivanWind Power ForecastingTime Series Forecasting

    UCL Energy Institute, University College London

  6. Learning volatility-adaptive forgetting via GARCH-modulated LSTM for stock price forecasting

    Oct 17, 2026Xin-Rui Lu, Da-Qing ZhangStock Price ForecastingTime Series Forecasting

    School of Science, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, PR China

  7. A decomposition-integration framework coupling singular spectrum analysis with a volatility-adaptive LSTM for stock price forecasting

    Oct 17, 2026Xin-Rui Lu, Da-Qing ZhangStock Price ForecastingTime Series Forecasting

    School of Science, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, PR China

  8. Flash RNN-T: Memory-Efficient Joint Network and Transducer Loss Computation

    Oct 16, 2026Mahmoud AshrafMemory-Efficient TrainingStreaming ASR

    Tarteel AI

  9. RTSM: Adapting Recursive Models to Multivariate Time-Series Classification

    Oct 16, 2026Georg Tenzing, Anna Tegon, Luca Benini +1Time Series ClassificationMultivariate Time Series Classification

    Integrated Systems Laboratory, ETH Zürich, Switzerland

  10. BRICE: Brain-inspired Recurrent Illusory Contour Encoder for Point Cloud Completion

    Oct 16, 2026Yunlei Wang, Dawei Li, Xue-song TangPoint Cloud CompletionRecurrent Neural Networks

    School of Information and Intelligent Science, Donghua University, Shanghai, China 201620

  11. Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series

    Oct 13, 2026Maximilian Kirsch, Christine PreisachInterpretable Time-Series ClassificationTree Species Classification

    Intelligent Systems Research Group (ISRG), Karlsruhe University of Applied Sciences, Germany

  12. Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine

    Oct 13, 2026Thomas HannaganRecurrent Neural NetworksImage Classification

    Stellantis

  13. ST-GRU PredRNN-V2: A GRU-Based Reformulation of PredRNN-V2 for Video Prediction

    Oct 13, 2026Augusta van Haren, Hanna Hoogen, Luca Pattavina +1Video PredictionRecurrent Neural Networks

    Radboud University

  14. Compression Through Time: An Interplay of Separability, Stationarity and Capacity

    Oct 12, 2026Nikolaus Kopp, Franz Pernkopf, Bernhard GeigerNeural Network GeneralizationRecurrent Neural Networks

    Graz University of Technology, Graz, Austria · Graz University of Technology & Know Center Research GmbH, Graz, Austria

  15. DeepThink: A Conceptual Model for Knowledge-Addressable Recurrent Language Modeling

    Oct 12, 2026Weiguo ChenRecurrent Language ModelsLanguage Modeling

  16. Interactive 4D Volumetric Liquid Forecasting under Moving-Solid Interaction

    Oct 12, 2026Jongwon Ryu, Sunjae Yoon, Junyeong KimFluid-Structure InteractionSpatiotemporal Forecasting

    Department of Artificial Intelligence Chung-Ang University

  17. Knowledge transfer between simple discrete worlds

    Oct 10, 2026Silvère Gangloff, Jan HulaLogical ReasoningCompositional Generalization

    University of Ostrava

  18. Fitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners

    Oct 10, 2026Amin Najafgholizadeh, Mohsen AfsharchiNeural Algorithmic ReasoningGraph Neural Networks

    University of Zanjan

  19. Manifold Navigation Learning Using Recurrent Networks: Efficient Training Techniques

    Oct 9, 2026Sarah Harkins Dayton, Sathya Rengaswami, Vasileios Maroulas +2Trajectory PredictionRecurrent Neural Networks

    Department of Math, University of Tennessee. · U.S. Army Combat Capabilities Development Command, Army Research Laboratory.

  20. The RNN Homunculus: Learning to Gate Long-Term Memory

    Oct 9, 2026Zeyu Fan, Alexandru-Dragos Manolache, Jan van GemertLong-Range Dependency ModelingRecurrent Neural Networks

    Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands

  21. Does Learning Recurrent Networks Matter in Partially Observable RL?

    Oct 9, 2026Zekang Wang, Zhe He, Gaojie JinPartially Observable RLPartially Observable Markov Decision Processes

    University of Macau Macau SAR, China

  22. Neural Networks for Temporal Pattern Recognition and Dynamic Arm Gesture Speed Estimation for Robot Control

    Oct 8, 2026Milán Zsolt Bagladi, László GulyásRecurrent Neural NetworksRobotic Perception

    Department of Artificial Intelligence ELTE Eötvös Loránd University Budapest, Hungary

  23. Reconstruction of Multiscale Plasma Dynamics Across Operating Regimes

    Oct 7, 2026Maryam Reza, Farbod FarajiRecurrent Neural NetworksMultiscale Time-Series Modeling

    Independent Researcher · Department of Computing, Huxley Building, Imperial College London, London SW7 2RH, United Kingdom

  24. Learning infinite context windows in recurrent architectures via spatial neural computing

    Oct 7, 2026Aleix Salvador-Pomarol, Arthur N. Montanari, Earl K. Miller +2Recurrent Neural NetworksLong-Context Modeling

    UC San Diego · Northwestern University · Massachusetts Institute of Technology

  25. Continuous Memory Machines

    Oct 6, 2026Ciaran Regan, Kai Arulkumaran, Luke Darlow +3Memory-Augmented Neural NetworksRecurrent Neural Networks

    University of Tsukuba · Sakana AI

  26. Forecast Accuracy Is Not Trading Profit: Evolving Small Recurrent Networks for Stock Return Prediction

    Oct 6, 2026Jonathan Chang, Zimeng LyuTime Series ForecastingRecurrent Neural Networks

    Union County Magnet High School Scotch Plains, New Jersey, USA · Kean University Union, New Jersey, USA

  27. Learned Monotone Recurrent Features in Governed Credit Scoring: The Price of the Frame and the Necessity of Macro Conditioning

    Oct 6, 2026Yew Lee TanCredit ScoringMonotonicity Constraints

    This work was conducted in a personal capacity; the views expressed are the author’s own.

  28. Learning to Remember: Distilling Memory Retention for Compact Recurrent Neural Networks

    Oct 3, 2026Nilushika Udayangania, Kishor Nandakishora, Marimuthu PalaniswamiKnowledge DistillationNeural Network Compression

    Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, 3052, VIC, Australia

  29. On architectural choices for interpretability and thermodynamic consistency in Physically Recurrent Neural Networks in the low-data regime

    Oct 2, 2026M. A. Maia, K. A. Meyer, A. M. C. M. van Gils +2Recurrent Neural NetworksNeural Network Interpretability

    Delft University of Technology, Department of Civil Engineering and Geosciences, P.O.Box 5048, 2600GA, Delft, The Netherlands · Chalmers University of Technology, Department of Mechanical Engineering, Gothenburg, Sweden

  30. Most-Recent Anchoring with Recurrent Ordering for Time Series Forecasting

    Oct 2, 2026Jung Min Choi, Ngoc Son Le, Ibram Abdelmalak +2Time Series ForecastingLong-Term Time Series Forecasting

    VWFS Data Analytics Research Center (VWFS-DARC), Hildesheim, Germany