{"total":34,"rows":[{"id":"2406.06484","title":"Parallelizing Linear Transformers with the Delta Rule over Sequence Length","created":"2024-06-10","cited_by_count":396,"area":null},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2306.13867","title":"Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems","created":"2023-06-24","cited_by_count":28,"area":null},{"id":"2106.01202","title":"Framing RNN as a kernel method: A neural ODE approach","created":"2021-06-02","cited_by_count":23,"area":null},{"id":"2507.17343","title":"Principled Multimodal Representation Learning","created":"2025-07-23","cited_by_count":19,"area":null},{"id":"2105.13205","title":"Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design","created":"2021-05-27","cited_by_count":18,"area":null},{"id":"2405.02670","title":"From Generalization Analysis to Optimization Designs for State Space Models","created":"2024-05-04","cited_by_count":11,"area":null},{"id":"2101.01864","title":"Constrained Block Nonlinear Neural Dynamical Models","created":"2021-01-06","cited_by_count":9,"area":null},{"id":"2102.07927","title":"Structured Dropout Variational Inference for Bayesian Neural Networks","created":"2021-02-16","cited_by_count":7,"area":null},{"id":"2106.09121","title":"Scaling-up Diverse Orthogonal Convolutional Networks with a Paraunitary Framework","created":"2021-06-16","cited_by_count":7,"area":null},{"id":"2103.04217","title":"Spectral Tensor Train Parameterization of Deep Learning Layers","created":"2021-03-07","cited_by_count":6,"area":null},{"id":"2105.02953","title":"Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network","created":"2021-04-20","cited_by_count":6,"area":null},{"id":"2109.14142","title":"On the Provable Generalization of Recurrent Neural Networks","created":"2021-09-29","cited_by_count":6,"area":null},{"id":"2112.08950","title":"Stable Long-Term Recurrent Video Super-Resolution","created":"2021-12-16","cited_by_count":6,"area":null},{"id":"2405.20980","title":"Neural Gaussian Scale-Space Fields","created":"2024-05-31","cited_by_count":6,"area":null},{"id":"2307.08012","title":"Householder Projector for Unsupervised Latent Semantics Discovery","created":"2023-07-16","cited_by_count":5,"area":null},{"id":"2111.04601","title":"On the Stochastic Stability of Deep Markov Models","created":"2021-11-08","cited_by_count":4,"area":null},{"id":"2211.03721","title":"Streaming, fast and accurate on-device Inverse Text Normalization for Automatic Speech Recognition","created":"2022-11-07","cited_by_count":4,"area":null},{"id":"2303.16816","title":"PAC-Bayesian bounds for learning LTI-ss systems with input from empirical loss","created":"2023-03-29","cited_by_count":4,"area":null},{"id":"2410.07427","title":"A Generalization Bound for a Family of Implicit Networks","created":"2024-10-09","cited_by_count":4,"area":null},{"id":"2108.02428","title":"A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare","created":"2021-08-05","cited_by_count":3,"area":null},{"id":"2306.17184","title":"State space models can express n-gram languages","created":"2023-06-20","cited_by_count":3,"area":null},{"id":"2405.20278","title":"Length independent generalization bounds for deep SSM architectures via Rademacher contraction and stability constraints","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2411.02784","title":"Generalization and Risk Bounds for Recurrent Neural Networks","created":"2024-11-05","cited_by_count":2,"area":null},{"id":"2505.19827","title":"Revisiting Glorot Initialization for Long-Range Linear Recurrences","created":"2025-05-26","cited_by_count":2,"area":null},{"id":"2212.14838","title":"PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models","created":"2022-12-30","cited_by_count":1,"area":null},{"id":"2402.04012","title":"Quantized Approximately Orthogonal Recurrent Neural Networks","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2103.02770","title":"SVMax: A Feature Embedding Regularizer","created":"2021-03-04","cited_by_count":0,"area":null},{"id":"2106.04088","title":"A Lightweight and Gradient-Stable Neural Layer","created":"2021-06-08","cited_by_count":0,"area":null},{"id":"2210.01348","title":"Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks","created":"2022-10-04","cited_by_count":0,"area":null},{"id":"2305.18423","title":"On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences","created":"2023-05-28","cited_by_count":0,"area":null},{"id":"2502.00047","title":"HadamRNN: Binary and Sparse Ternary Orthogonal RNNs","created":"2025-01-28","cited_by_count":0,"area":null},{"id":"2602.01996","title":"Optimizing Tensor Train Decomposition in DNNs for RISC-V Architectures Using Design Space Exploration and Compiler Optimizations","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2602.07581","title":"$\\partial$CBDs: Differentiable Causal Block Diagrams","created":"2026-02-07","cited_by_count":0,"area":null}],"next_cursor":null}