{"total":77,"rows":[{"id":"2303.06349","title":"Resurrecting Recurrent Neural Networks for Long Sequences","created":"2023-03-11","cited_by_count":408,"area":null},{"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":"2104.07167","title":"Orthogonalizing Convolutional Layers with the Cayley Transform","created":"2021-04-14","cited_by_count":105,"area":null},{"id":"2110.07238","title":"On the difficulty of learning chaotic dynamics with RNNs","created":"2021-10-14","cited_by_count":67,"area":null},{"id":"2103.05487","title":"UnICORNN: A recurrent model for learning very long time dependencies","created":"2021-03-09","cited_by_count":56,"area":null},{"id":"2110.04840","title":"Heavy Ball Neural Ordinary Differential Equations","created":"2021-10-10","cited_by_count":42,"area":null},{"id":"2110.04744","title":"Long Expressive Memory for Sequence Modeling","created":"2021-10-10","cited_by_count":41,"area":null},{"id":"2301.12832","title":"Deep networks for system identification: a Survey","created":"2023-01-30","cited_by_count":37,"area":null},{"id":"2102.07432","title":"Fast and accurate optimization on the orthogonal manifold without retraction","created":"2021-02-15","cited_by_count":35,"area":null},{"id":"2104.05097","title":"Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks","created":"2021-04-11","cited_by_count":30,"area":null},{"id":"2203.05483","title":"projUNN: efficient method for training deep networks with unitary matrices","created":"2022-03-10","cited_by_count":27,"area":null},{"id":"2405.21064","title":"Recurrent neural networks: vanishing and exploding gradients are not the end of the story","created":"2024-05-31","cited_by_count":25,"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":"2101.05186","title":"MC-LSTM: Mass-Conserving LSTM","created":"2021-01-13","cited_by_count":17,"area":null},{"id":"2103.00673","title":"Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training","created":"2021-03-01","cited_by_count":15,"area":null},{"id":"2112.09046","title":"Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach","created":"2021-12-16","cited_by_count":12,"area":null},{"id":"2205.14173","title":"Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport","created":"2022-05-27","cited_by_count":11,"area":null},{"id":"2406.10019","title":"Group and Shuffle: Efficient Structured Orthogonal Parametrization","created":"2024-06-14","cited_by_count":11,"area":null},{"id":"2503.05431","title":"Quantum-PEFT: Ultra parameter-efficient fine-tuning","created":"2025-03-07","cited_by_count":11,"area":null},{"id":"2110.07034","title":"How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies","created":"2021-10-13","cited_by_count":10,"area":null},{"id":"2509.09679","title":"ButterflyQuant: Ultra-low-bit LLM Quantization through Learnable Orthogonal Butterfly Transforms","created":"2025-09-11","cited_by_count":10,"area":null},{"id":"2303.11835","title":"Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian","created":"2023-03-20","cited_by_count":9,"area":null},{"id":"2310.13225","title":"Scalable Neural Network Kernels","created":"2023-10-20","cited_by_count":9,"area":null},{"id":"2402.01344","title":"Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks","created":"2024-02-02","cited_by_count":9,"area":null},{"id":"2410.05499","title":"Unitary convolutions for learning on graphs and groups","created":"2024-10-07","cited_by_count":8,"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":"2210.00973","title":"NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning","created":"2022-10-03","cited_by_count":7,"area":null},{"id":"2405.20980","title":"Neural Gaussian Scale-Space Fields","created":"2024-05-31","cited_by_count":6,"area":null},{"id":"2407.07239","title":"RotRNN: Modelling Long Sequences with Rotations","created":"2024-07-09","cited_by_count":6,"area":null},{"id":"2605.12492","title":"Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation","created":"2026-05-12","cited_by_count":6,"area":null},{"id":"2108.00051","title":"Coordinate descent on the orthogonal group for recurrent neural network training","created":"2021-07-30","cited_by_count":5,"area":null},{"id":"2205.08385","title":"Feedback Gradient Descent: Efficient and Stable Optimization with Orthogonality for DNNs","created":"2022-05-12","cited_by_count":5,"area":null},{"id":"2210.01245","title":"Random orthogonal additive filters: a solution to the vanishing/exploding gradient of deep neural networks","created":"2022-10-03","cited_by_count":5,"area":null},{"id":"2312.17306","title":"Gradient Flossing: Improving Gradient Descent through Dynamic Control of Jacobians","created":"2023-12-28","cited_by_count":5,"area":null},{"id":"2403.11199","title":"Graph Unitary Message Passing","created":"2024-03-17","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":"2208.06496","title":"Orthogonal Gated Recurrent Unit with Neumann-Cayley Transformation","created":"2022-08-12","cited_by_count":4,"area":null},{"id":"2604.02309","title":"go-$m$HC: Direct Parameterization of Manifold-Constrained Hyper-Connections via Generalized Orthostochastic Matrices","created":"2026-04-02","cited_by_count":4,"area":null},{"id":"2102.09589","title":"A Differential Geometry Perspective on Orthogonal Recurrent Models","created":"2021-02-18","cited_by_count":3,"area":null},{"id":"2112.01453","title":"Target Propagation via Regularized Inversion","created":"2021-12-02","cited_by_count":3,"area":null},{"id":"2505.12378","title":"Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method","created":"2025-05-18","cited_by_count":3,"area":null},{"id":"2102.11855","title":"Deep Unitary Convolutional Neural Networks","created":"2021-02-23","cited_by_count":2,"area":null},{"id":"2109.13090","title":"Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing","created":"2021-09-14","cited_by_count":2,"area":null},{"id":"2203.05082","title":"Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes","created":"2022-03-09","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":"2310.03915","title":"Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control","created":"2023-10-05","cited_by_count":2,"area":null},{"id":"2408.13211","title":"Optimal Quantum Circuit Design via Unitary Neural Networks","created":"2024-08-23","cited_by_count":2,"area":null},{"id":"2410.22258","title":"LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers","created":"2024-10-29","cited_by_count":2,"area":null},{"id":"2508.12121","title":"Time-Scale Coupling Between States and Parameters in Recurrent Neural Networks","created":"2025-08-16","cited_by_count":2,"area":null},{"id":"2605.11508","title":"LiBrA-Net: Lie-Algebraic Bilateral Affine Fields for Real-Time 4K Video Dehazing","created":"2026-05-12","cited_by_count":2,"area":null},{"id":"2401.05967","title":"Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding","created":"2024-01-11","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":"2405.20390","title":"Quantitative Convergences of Lie Group Momentum Optimizers","created":"2024-05-30","cited_by_count":1,"area":null},{"id":"2510.25557","title":"Hybrid Quantum-Classical Recurrent Neural Networks","created":"2025-10-29","cited_by_count":1,"area":null},{"id":"2602.18417","title":"Subgroups of $U(d)$ Induce Natural RNN and Transformer Architectures","created":"2026-02-20","cited_by_count":1,"area":null},{"id":"2605.11855","title":"Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2608.14691","title":"The Quantum Shortcut: Complex Phase-State Dynamics Reduce the Optimization Steps of Sequence Models","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2106.06097","title":"Neural Optimization Kernel: Towards Robust Deep Learning","created":"2021-06-11","cited_by_count":0,"area":null},{"id":"2304.10630","title":"Ellipsoid fitting with the Cayley transform","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2304.11790","title":"Adaptive-saturated RNN: Remember more with less instability","created":"2023-04-24","cited_by_count":0,"area":null},{"id":"2310.12032","title":"Exact and general decoupled solutions of the LMC Multitask Gaussian Process model","created":"2023-10-18","cited_by_count":0,"area":null},{"id":"2501.03471","title":"Hyperbolic Binary Neural Network","created":"2025-01-07","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":"2505.00929","title":"Compact Recurrent Transformer with Persistent Memory","created":"2025-05-02","cited_by_count":0,"area":null},{"id":"2602.12021","title":"Improved state mixing in higher-order and block diagonal linear recurrent networks","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2602.15283","title":"Complex-Valued Unitary Representations as Classification Heads for Improved Uncertainty Quantification in Deep Neural Networks","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2605.06729","title":"The E$\\Delta$-MHC-Geo Transformer: Adaptive Geodesic Operations with Guaranteed Orthogonality","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.10317","title":"Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.14889","title":"SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.30364","title":"Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2606.15085","title":"An Integrable Token Mixing Layer from the Generalized Yang Baxter Equation","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2607.04535","title":"ManifoldFlow: Spectrum-Learnable Stiefel Optimization via SPD Relaxation","created":"2026-07-05","cited_by_count":0,"area":null},{"id":"2607.22117","title":"Projection Pursuit CPCANet for Domain Generalization","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2609.01550","title":"Linear Reusable Neural Bases Architecture for Network Compression","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.30973","title":"LipSSM: Structurally Lipschitz-Bounded Cascaded State-Space Model via Metric Transfer between Consecutive SSM Layers","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.39739","title":"Stable Transformers for Graph Generation","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.01230","title":"HHR: Hierarchical Hash Retrieval for Efficient LLM Generation","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}