{"total":62,"rows":[{"id":"2110.13985","title":"Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers","created":"2021-10-26","cited_by_count":608,"area":null},{"id":"2206.11893","title":"On the Parameterization and Initialization of Diagonal State Space Models","created":"2022-06-23","cited_by_count":424,"area":null},{"id":"2302.10866","title":"Hyena Hierarchy: Towards Larger Convolutional Language Models","created":"2023-02-21","cited_by_count":393,"area":null},{"id":"2209.15616","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","created":"2022-09-30","cited_by_count":197,"area":null},{"id":"2306.15794","title":"HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution","created":"2023-06-27","cited_by_count":194,"area":null},{"id":"2209.10655","title":"Mega: Moving Average Equipped Gated Attention","created":"2022-09-21","cited_by_count":179,"area":null},{"id":"2203.06717","title":"Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs","created":"2022-03-13","cited_by_count":155,"area":null},{"id":"2209.12951","title":"Liquid Structural State-Space Models","created":"2022-09-26","cited_by_count":136,"area":null},{"id":"2210.09298","title":"What Makes Convolutional Models Great on Long Sequence Modeling?","created":"2022-10-17","cited_by_count":87,"area":null},{"id":"2402.15584","title":"State Space Models for Event Cameras","created":"2024-02-23","cited_by_count":75,"area":null},{"id":"2302.06646","title":"Simple Hardware-Efficient Long Convolutions for Sequence Modeling","created":"2023-02-13","cited_by_count":62,"area":null},{"id":"2302.03130","title":"Spatial Functa: Scaling Functa to ImageNet Classification and Generation","created":"2023-02-06","cited_by_count":57,"area":null},{"id":"2205.14623","title":"SKFlow: Learning Optical Flow with Super Kernels","created":"2022-05-29","cited_by_count":56,"area":null},{"id":"2309.12252","title":"Parallelizing non-linear sequential models over the sequence length","created":"2023-09-21","cited_by_count":45,"area":null},{"id":"2204.07178","title":"Relaxing Equivariance Constraints with Non-stationary Continuous Filters","created":"2022-04-14","cited_by_count":41,"area":null},{"id":"2210.06583","title":"S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces","created":"2022-10-12","cited_by_count":41,"area":null},{"id":"2403.05369","title":"Frequency-Adaptive Dilated Convolution for Semantic Segmentation","created":"2024-03-08","cited_by_count":34,"area":null},{"id":"2311.05908","title":"FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores","created":"2023-11-10","cited_by_count":33,"area":null},{"id":"2310.18780","title":"Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions","created":"2023-10-28","cited_by_count":24,"area":null},{"id":"2112.03740","title":"Dilated convolution with learnable spacings","created":"2021-12-07","cited_by_count":20,"area":null},{"id":"2305.01638","title":"Sequence Modeling with Multiresolution Convolutional Memory","created":"2023-05-02","cited_by_count":19,"area":null},{"id":"2204.07554","title":"Efficient Architecture Search for Diverse Tasks","created":"2022-04-15","cited_by_count":18,"area":null},{"id":"2301.10540","title":"Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN","created":"2023-01-25","cited_by_count":18,"area":null},{"id":"2206.03398","title":"Towards a General Purpose CNN for Long Range Dependencies in $N$D","created":"2022-06-07","cited_by_count":16,"area":null},{"id":"2210.03651","title":"Understanding the Covariance Structure of Convolutional Filters","created":"2022-10-07","cited_by_count":16,"area":null},{"id":"2309.08045","title":"Traveling Waves Encode the Recent Past and Enhance Sequence Learning","created":"2023-09-03","cited_by_count":16,"area":null},{"id":"2305.11203","title":"PDP: Parameter-free Differentiable Pruning is All You Need","created":"2023-05-18","cited_by_count":12,"area":null},{"id":"2312.10531","title":"How to Train Neural Field Representations: A Comprehensive Study and Benchmark","created":"2023-12-16","cited_by_count":12,"area":null},{"id":"2212.06096","title":"Implicit Convolutional Kernels for Steerable CNNs","created":"2022-12-12","cited_by_count":9,"area":null},{"id":"2204.00740","title":"Path Development Network with Finite-dimensional Lie Group Representation","created":"2022-04-02","cited_by_count":8,"area":null},{"id":"2210.06965","title":"CUF: Continuous Upsampling Filters","created":"2022-10-13","cited_by_count":8,"area":null},{"id":"2307.10001","title":"As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain","created":"2023-07-19","cited_by_count":8,"area":null},{"id":"2407.08972","title":"Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness","created":"2024-07-12","cited_by_count":8,"area":null},{"id":"2205.15254","title":"Pooling Revisited: Your Receptive Field is Suboptimal","created":"2022-05-30","cited_by_count":6,"area":null},{"id":"2211.05151","title":"QuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression","created":"2022-11-09","cited_by_count":6,"area":null},{"id":"2304.02330","title":"SMPConv: Self-moving Point Representations for Continuous Convolution","created":"2023-04-05","cited_by_count":6,"area":null},{"id":"2311.11587","title":"LDConv: Linear deformable convolution for improving convolutional neural networks","created":"2023-11-20","cited_by_count":6,"area":null},{"id":"2407.03681","title":"HyperSpace: Hypernetworks for spacing-adaptive image segmentation","created":"2024-07-04","cited_by_count":6,"area":null},{"id":"2309.13600","title":"Multi-Dimensional Hyena for Spatial Inductive Bias","created":"2023-09-24","cited_by_count":5,"area":null},{"id":"2406.08128","title":"Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences","created":"2024-06-12","cited_by_count":5,"area":null},{"id":"2302.05400","title":"DNArch: Learning Convolutional Neural Architectures by Backpropagation","created":"2023-02-10","cited_by_count":4,"area":null},{"id":"2306.00817","title":"Dilated Convolution with Learnable Spacings: beyond bilinear interpolation","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2404.11163","title":"LongVQ: Long Sequence Modeling with Vector Quantization on Structured Memory","created":"2024-04-17","cited_by_count":4,"area":null},{"id":"2406.05400","title":"Metric Convolutions: A Unifying Theory to Adaptive Image Convolutions","created":"2024-06-08","cited_by_count":3,"area":null},{"id":"2410.12982","title":"Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2411.10649","title":"Deep Loss Convexification for Learning Iterative Models","created":"2024-11-16","cited_by_count":3,"area":null},{"id":"2207.06968","title":"DASS: Differentiable Architecture Search for Sparse neural networks","created":"2022-07-14","cited_by_count":2,"area":null},{"id":"2312.01286","title":"Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas","created":"2023-12-03","cited_by_count":2,"area":null},{"id":"2402.19305","title":"HyenaPixel: Global Image Context with Convolutions","created":"2024-02-29","cited_by_count":2,"area":null},{"id":"2403.15182","title":"PDE-CNNs: Axiomatic Derivations and Applications","created":"2024-03-22","cited_by_count":2,"area":null},{"id":"2408.09453","title":"Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling","created":"2024-08-18","cited_by_count":2,"area":null},{"id":"2509.20503","title":"Myosotis: structured computation for attention like layer","created":"2025-09-24","cited_by_count":2,"area":null},{"id":"2206.01178","title":"Discretization Invariant Networks for Learning Maps between Neural Fields","created":"2022-06-02","cited_by_count":1,"area":null},{"id":"2306.11982","title":"Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture","created":"2023-06-21","cited_by_count":1,"area":null},{"id":"2402.07152","title":"Explainable Global Wildfire Prediction Models using Graph Neural Networks","created":"2024-02-11","cited_by_count":1,"area":null},{"id":"2409.09875","title":"Scaling Continuous Kernels with Sparse Fourier Domain Learning","created":"2024-09-15","cited_by_count":1,"area":null},{"id":"2212.04351","title":"Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms","created":"2022-08-17","cited_by_count":0,"area":null},{"id":"2302.06247","title":"Continuous-time convolutions model of event sequences","created":"2023-02-13","cited_by_count":0,"area":null},{"id":"2401.17948","title":"HyperZ$\\cdot$Z$\\cdot$W Operator Connects Slow-Fast Networks for Full Context Interaction","created":"2024-01-31","cited_by_count":0,"area":null},{"id":"2603.17538","title":"Learning Coordinate-based Convolutional Kernels for Continuous SE(3) Equivariant and Efficient Point Cloud Analysis","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2607.19378","title":"Native Multi-Dimensional Subquadratic Operators via Input Dependent Long Convolutions","created":"2026-07-01","cited_by_count":0,"area":null},{"id":"2609.37015","title":"RBF-GNN: Rational Basis Functions for Pseudo-Coordinate based Graph Convolutions","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}