{"list":"other","total":66,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"2205.14135","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","created":"2022-05-27","cited_by_count":3673},{"id":"2004.05150","title":"Longformer: The Long-Document Transformer","created":"2020-04-10","cited_by_count":3413},{"id":"1609.07843","title":"Pointer Sentinel Mixture Models","created":"2016-09-26","cited_by_count":3096},{"id":"2307.08691","title":"FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning","created":"2023-07-17","cited_by_count":2598},{"id":"2111.00396","title":"Efficiently Modeling Long Sequences with Structured State Spaces","created":"2021-10-31","cited_by_count":2465},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"2001.04451","title":"Reformer: The Efficient Transformer","created":"2020-01-13","cited_by_count":1668},{"id":"2006.04768","title":"Linformer: Self-Attention with Linear Complexity","created":"2020-06-08","cited_by_count":1474},{"id":"2405.21060","title":"Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality","created":"2024-05-31","cited_by_count":1336},{"id":"2305.13048","title":"RWKV: Reinventing RNNs for the Transformer Era","created":"2023-05-22","cited_by_count":827},{"id":"1804.03209","title":"Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition","created":"2018-04-09","cited_by_count":752},{"id":"2011.04006","title":"Long Range Arena: A Benchmark for Efficient Transformers","created":"2020-11-08","cited_by_count":700},{"id":"1901.09948","title":"Surrogate Gradient Learning in Spiking Neural Networks","created":"2019-01-28","cited_by_count":626},{"id":"2307.08621","title":"Retentive Network: A Successor to Transformer for Large Language Models","created":"2023-07-17","cited_by_count":613},{"id":"2008.07669","title":"HiPPO: Recurrent Memory with Optimal Polynomial Projections","created":"2020-08-17","cited_by_count":605},{"id":"1705.03122","title":"Convolutional Sequence to Sequence Learning","created":"2017-05-08","cited_by_count":552},{"id":"2412.06464","title":"Gated Delta Networks: Improving Mamba2 with Delta Rule","created":"2024-12-09","cited_by_count":532},{"id":"2312.06635","title":"Gated Linear Attention Transformers with Hardware-Efficient Training","created":"2023-12-11","cited_by_count":514},{"id":"2212.14052","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models","created":"2022-12-28","cited_by_count":499},{"id":"2206.11893","title":"On the Parameterization and Initialization of Diagonal State Space Models","created":"2022-06-23","cited_by_count":424},{"id":"1808.03314","title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network","created":"2018-08-09","cited_by_count":420},{"id":"1706.02609","title":"Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks","created":"2017-06-08","cited_by_count":406},{"id":"2403.19887","title":"Jamba: A Hybrid Transformer-Mamba Language Model","created":"2024-03-28","cited_by_count":401},{"id":"2406.06484","title":"Parallelizing Linear Transformers with the Delta Rule over Sequence Length","created":"2024-06-10","cited_by_count":396},{"id":"2203.14343","title":"Diagonal State Spaces are as Effective as Structured State Spaces","created":"2022-03-27","cited_by_count":350},{"id":"2209.15425","title":"Spikformer: When Spiking Neural Network Meets Transformer","created":"2022-09-29","cited_by_count":271},{"id":"2206.13947","title":"Long Range Language Modeling via Gated State Spaces","created":"2022-06-27","cited_by_count":265},{"id":"2109.12894","title":"Training Spiking Neural Networks Using Lessons From Deep Learning","created":"2021-09-27","cited_by_count":259},{"id":"1506.00019","title":"A Critical Review of Recurrent Neural Networks for Sequence Learning","created":"2015-05-29","cited_by_count":224},{"id":"1608.08782","title":"Training Deep Spiking Neural Networks using Backpropagation","created":"2016-08-31","cited_by_count":205},{"id":"2406.07522","title":"Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling","created":"2024-06-11","cited_by_count":174},{"id":"1504.00941","title":"A Simple Way to Initialize Recurrent Networks of Rectified Linear Units","created":"2015-04-03","cited_by_count":130},{"id":"2407.18921","title":"Mobile Edge Intelligence for Large Language Models: A Contemporary Survey","created":"2024-07-09","cited_by_count":128},{"id":"2404.03663","title":"Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips","created":"2024-02-15","cited_by_count":103},{"id":"2302.13939","title":"SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks","created":"2023-02-27","cited_by_count":88},{"id":"2308.10873","title":"SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation","created":"2023-08-21","cited_by_count":48},{"id":"2404.18508","title":"Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models","created":"2024-04-29","cited_by_count":31},{"id":"2406.03287","title":"SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms","created":"2024-06-05","cited_by_count":31},{"id":"2407.04752","title":"SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking","created":"2024-07-05","cited_by_count":31},{"id":"2401.02020","title":"Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket","created":"2024-01-04","cited_by_count":30},{"id":"2206.06178","title":"Efficient recurrent architectures through activity sparsity and sparse back-propagation through time","created":"2022-06-13","cited_by_count":26},{"id":"2309.03641","title":"Spiking Structured State Space Model for Monaural Speech Enhancement","created":"2023-09-07","cited_by_count":23},{"id":"2411.10741","title":"MetaLA: Unified Optimal Linear Approximation to Softmax Attention Map","created":"2024-11-16","cited_by_count":21},{"id":"2401.00955","title":"Learning Long Sequences in Spiking Neural Networks","created":"2023-12-14","cited_by_count":19},{"id":"2408.14909","title":"SpikingSSMs: Learning Long Sequences with Sparse and Parallel Spiking State Space Models","created":"2024-08-27","cited_by_count":19},{"id":"2308.15122","title":"SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation","created":"2023-08-29","cited_by_count":17},{"id":"2404.17099","title":"Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND","created":"2024-04-26","cited_by_count":17},{"id":"2310.17638","title":"Generative Fractional Diffusion Models","created":"2023-10-26","cited_by_count":12},{"id":"2406.02923","title":"P-SpikeSSM: Harnessing Probabilistic Spiking State Space Models for Long-Range Dependency Tasks","created":"2024-06-05","cited_by_count":12},{"id":"2402.04882","title":"LMUFormer: Low Complexity Yet Powerful Spiking Model With Legendre Memory Units","created":"2024-01-20","cited_by_count":11},{"id":"2502.06663","title":"EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models","created":"2025-02-10","cited_by_count":10},{"id":"2401.04331","title":"Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study","created":"2024-01-09","cited_by_count":9},{"id":"2410.17268","title":"SPikE-SSM: A Sparse, Precise, and Efficient Spiking State Space Model for Long Sequences Learning","created":"2024-10-07","cited_by_count":9},{"id":"2306.12465","title":"Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inference","created":"2023-06-21","cited_by_count":7},{"id":"2409.02111","title":"Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions","created":"2024-08-19","cited_by_count":7},{"id":"2411.05274","title":"Distributed-Order Fractional Graph Operating Network","created":"2024-11-08","cited_by_count":7},{"id":"2503.16207","title":"Neural Variable-Order Fractional Differential Equation Networks","created":"2025-03-20","cited_by_count":6},{"id":"2503.16666","title":"Efficient Training of Neural Fractional-Order Differential Equation via Adjoint Backpropagation","created":"2025-03-20","cited_by_count":3},{"id":"2507.16937","title":"Fractional-order Spiking Neural Network","created":"2025-07-22","cited_by_count":3},{"id":"2511.10208","title":"Fractional neural attention for efficient multiscale sequence processing","created":"2025-11-13","cited_by_count":2}],"next_cursor":null}