{"total":66,"rows":[{"id":"2305.13048","title":"RWKV: Reinventing RNNs for the Transformer Era","created":"2023-05-22","cited_by_count":827,"area":null},{"id":"2109.12894","title":"Training Spiking Neural Networks Using Lessons From Deep Learning","created":"2021-09-27","cited_by_count":259,"area":null},{"id":"2104.05942","title":"Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness","created":"2021-04-13","cited_by_count":45,"area":null},{"id":"2102.04877","title":"Noisy Recurrent Neural Networks","created":"2021-02-09","cited_by_count":42,"area":null},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2209.11208","title":"A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases","created":"2022-09-22","cited_by_count":30,"area":null},{"id":"2210.04243","title":"Fine-Tuning Pre-trained Transformers into Decaying Fast Weights","created":"2022-10-09","cited_by_count":26,"area":null},{"id":"2112.04036","title":"DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs","created":"2021-12-07","cited_by_count":19,"area":null},{"id":"2105.11558","title":"Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems","created":"2021-05-24","cited_by_count":17,"area":null},{"id":"2103.14722","title":"Almost Surely Stable Deep Dynamics","created":"2021-03-26","cited_by_count":16,"area":null},{"id":"2205.01938","title":"DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs","created":"2022-05-04","cited_by_count":16,"area":null},{"id":"2403.12335","title":"Temporally Consistent Koopman Autoencoders for Forecasting Dynamical Systems","created":"2024-03-19","cited_by_count":15,"area":null},{"id":"2112.04219","title":"Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System","created":"2021-12-08","cited_by_count":14,"area":null},{"id":"2210.01407","title":"Homotopy-based training of NeuralODEs for accurate dynamics discovery","created":"2022-10-04","cited_by_count":14,"area":null},{"id":"2106.01680","title":"Convergent Graph Solvers","created":"2021-06-03","cited_by_count":12,"area":null},{"id":"2505.20659","title":"An Optimisation Framework for Unsupervised Environment Design","created":"2025-05-27","cited_by_count":10,"area":"deeprl"},{"id":"2106.08928","title":"RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks","created":"2021-06-16","cited_by_count":7,"area":null},{"id":"2403.09389","title":"Learning to optimize with convergence guarantees using nonlinear system theory","created":"2024-03-14","cited_by_count":7,"area":null},{"id":"2508.16817","title":"Predictability Enables Parallelization of Nonlinear State Space Models","created":"2025-08-22","cited_by_count":7,"area":null},{"id":"2112.08950","title":"Stable Long-Term Recurrent Video Super-Resolution","created":"2021-12-16","cited_by_count":6,"area":null},{"id":"2304.06193","title":"Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems (Extended Version)","created":"2023-04-12","cited_by_count":6,"area":null},{"id":"2311.06964","title":"Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels","created":"2023-11-12","cited_by_count":6,"area":null},{"id":"2103.01521","title":"On the Memory Mechanism of Tensor-Power Recurrent Models","created":"2021-03-02","cited_by_count":5,"area":null},{"id":"2108.04585","title":"Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems","created":"2021-08-10","cited_by_count":5,"area":null},{"id":"2311.04449","title":"Recursion in Recursion: Two-Level Nested Recursion for Length Generalization with Scalability","created":"2023-11-08","cited_by_count":5,"area":null},{"id":"2606.06479","title":"Pretraining Recurrent Networks without Recurrence","created":"2026-06-04","cited_by_count":5,"area":null},{"id":"2107.01390","title":"Memory and attention in deep learning","created":"2021-07-03","cited_by_count":4,"area":null},{"id":"2302.03663","title":"SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics","created":"2023-02-07","cited_by_count":4,"area":null},{"id":"2312.06211","title":"Structured state-space models are deep Wiener models","created":"2023-12-11","cited_by_count":4,"area":"ssm"},{"id":"2407.11038","title":"Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics","created":"2024-07-06","cited_by_count":4,"area":null},{"id":"2607.19719","title":"Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination","created":"2026-07-22","cited_by_count":4,"area":"wm"},{"id":"2102.09589","title":"A Differential Geometry Perspective on Orthogonal Recurrent Models","created":"2021-02-18","cited_by_count":3,"area":null},{"id":"2105.00996","title":"Robust Learning of Recurrent Neural Networks in Presence of Exogenous Noise","created":"2021-05-03","cited_by_count":3,"area":null},{"id":"2210.05212","title":"On Scrambling Phenomena for Randomly Initialized Recurrent Networks","created":"2022-10-11","cited_by_count":3,"area":null},{"id":"2405.01305","title":"Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware","created":"2024-05-02","cited_by_count":3,"area":null},{"id":"2509.09001","title":"Fast attention mechanisms: a tale of parallelism","created":"2025-09-10","cited_by_count":3,"area":null},{"id":"2203.05403","title":"Robustness Analysis of Classification Using Recurrent Neural Networks with Perturbed Sequential Input","created":"2022-03-10","cited_by_count":2,"area":null},{"id":"2307.14823","title":"Fading memory as inductive bias in residual recurrent networks","created":"2023-07-27","cited_by_count":2,"area":null},{"id":"2508.11659","title":"Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections","created":"2025-08-05","cited_by_count":2,"area":null},{"id":"2106.02543","title":"Accelerating Dynamical System Simulations with Contracting and Physics-Projected Neural-Newton Solvers","created":"2021-06-04","cited_by_count":1,"area":null},{"id":"2212.06001","title":"Reinforcement Learning and Tree Search Methods for the Unit Commitment Problem","created":"2022-12-12","cited_by_count":1,"area":null},{"id":"2304.01828","title":"Learning Stable and Robust Linear Parameter-Varying State-Space Models","created":"2023-04-04","cited_by_count":1,"area":null},{"id":"2305.15385","title":"Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2309.02873","title":"Learning Hybrid Dynamics Models With Simulator-Informed Latent States","created":"2023-09-06","cited_by_count":1,"area":null},{"id":"2401.00379","title":"DREAM: Debugging and Repairing AutoML Pipelines","created":"2023-12-31","cited_by_count":1,"area":null},{"id":"2505.20340","title":"Latent Trajectory Dynamics in Large Language Models: A Manifold Evolution Framework with Empirical Validation","created":"2025-05-24","cited_by_count":1,"area":null},{"id":"2506.05678","title":"Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions","created":"2025-06-06","cited_by_count":1,"area":"ssm"},{"id":"2602.07599","title":"Rational Transductors","created":"2026-02-07","cited_by_count":1,"area":null},{"id":"2602.10911","title":"Tuning the burn-in phase in training recurrent neural networks improves their performance","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2602.11183","title":"Mitigating Error Accumulation in Continuous Navigation via Memory-Augmented Kalman Filtering","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2607.27656","title":"Looped Transformers with Source-Centered State Evolution","created":"2026-07-30","cited_by_count":1,"area":null},{"id":"2607.29158","title":"Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations","created":"2026-07-31","cited_by_count":1,"area":null},{"id":"2106.00047","title":"Learning and Generalization in RNNs","created":"2021-05-31","cited_by_count":0,"area":null},{"id":"2107.14309","title":"Distributed Identification of Contracting and/or Monotone Network Dynamics","created":"2021-07-29","cited_by_count":0,"area":null},{"id":"2310.01571","title":"Contraction Properties of the Global Workspace Primitive","created":"2023-10-02","cited_by_count":0,"area":null},{"id":"2403.15832","title":"Time-series Initialization and Conditioning for Video-agnostic Stabilization of Video Super-Resolution using Recurrent Networks","created":"2024-03-23","cited_by_count":0,"area":null},{"id":"2410.14993","title":"Making Every Frame Matter: Continuous Activity Recognition in Streaming Video via Adaptive Video Context Modeling","created":"2024-10-19","cited_by_count":0,"area":null},{"id":"2411.08232","title":"Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach","created":"2024-11-12","cited_by_count":0,"area":"moe"},{"id":"2508.15451","title":"A Solvable Molecular Switch Model for Stable Temporal Information Processing","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2509.17898","title":"Lipschitz-Based Robustness Certification for Recurrent Neural Networks via Convex Relaxation","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2601.19019","title":"Embedding of Low-Dimensional Sensory Dynamics in Recurrent Networks: Implications for the Geometry of Neural Representation","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2605.25730","title":"DeCoDrift: Stabilizing Decoder Coupling in Closed-Loop Foundation Segmentation","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.08934","title":"Backward Coherence and Hidden-State Stability in Recurrent Neural Networks: A Quasi-Reverse-Martingale Theory","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.11988","title":"What Uncertainties Do We Need for Dynamical Systems?","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2607.21645","title":"Multi-Horizon Consistency as Geometry: When Latent Dynamics Contract, and When They Do Not","created":"2026-07-21","cited_by_count":0,"area":"wm"},{"id":"2609.23366","title":"What Can a Recurrent State Safely Forget?","created":"2026-09-20","cited_by_count":0,"area":null}],"next_cursor":null}