{"list":"other","total":52,"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":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2307.09288","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","created":"2023-07-18","cited_by_count":13028},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2312.00752","title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","created":"2023-12-01","cited_by_count":4718},{"id":"2111.00396","title":"Efficiently Modeling Long Sequences with Structured State Spaces","created":"2021-10-31","cited_by_count":2465},{"id":"2005.08100","title":"Conformer: Convolution-augmented Transformer for Speech Recognition","created":"2020-05-16","cited_by_count":1863},{"id":"1901.02860","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","created":"2019-01-09","cited_by_count":1850},{"id":"2110.13900","title":"WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing","created":"2021-10-26","cited_by_count":1657},{"id":"2103.15691","title":"ViViT: A Video Vision Transformer","created":"2021-03-29","cited_by_count":1620},{"id":"2401.09417","title":"Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model","created":"2024-01-17","cited_by_count":1056},{"id":"2103.10385","title":"GPT Understands, Too","created":"2021-03-18","cited_by_count":716},{"id":"2110.13985","title":"Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers","created":"2021-10-26","cited_by_count":608},{"id":"2008.07669","title":"HiPPO: Recurrent Memory with Optimal Polynomial Projections","created":"2020-08-17","cited_by_count":605},{"id":"1804.00015","title":"ESPnet: End-to-End Speech Processing Toolkit","created":"2018-03-30","cited_by_count":558},{"id":"1510.08484","title":"MUSAN: A Music, Speech, and Noise Corpus","created":"2015-10-28","cited_by_count":425},{"id":"2206.11893","title":"On the Parameterization and Initialization of Diagonal State Space Models","created":"2022-06-23","cited_by_count":424},{"id":"2403.19887","title":"Jamba: A Hybrid Transformer-Mamba Language Model","created":"2024-03-28","cited_by_count":401},{"id":"2203.14343","title":"Diagonal State Spaces are as Effective as Structured State Spaces","created":"2022-03-27","cited_by_count":350},{"id":"1609.06773","title":"Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning","created":"2016-09-21","cited_by_count":257},{"id":"2208.05830","title":"Speech Enhancement and Dereverberation with Diffusion-based Generative Models","created":"2022-08-11","cited_by_count":197},{"id":"2006.12847","title":"Real Time Speech Enhancement in the Waveform Domain","created":"2020-06-23","cited_by_count":186},{"id":"1703.09452","title":"SEGAN: Speech Enhancement Generative Adversarial Network","created":"2017-03-28","cited_by_count":166},{"id":"1804.03619","title":"Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation","created":"2018-04-10","cited_by_count":162},{"id":"2008.00264","title":"DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement","created":"2020-08-01","cited_by_count":141},{"id":"2104.03538","title":"MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement","created":"2021-04-08","cited_by_count":114},{"id":"2212.11851","title":"StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation","created":"2022-12-22","cited_by_count":105},{"id":"1905.04874","title":"MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement","created":"2019-05-13","cited_by_count":104},{"id":"2207.02971","title":"Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding","created":"2022-07-06","cited_by_count":91},{"id":"2305.13686","title":"MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra","created":"2023-05-23","cited_by_count":52},{"id":"1911.04697","title":"PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network","created":"2019-11-12","cited_by_count":51},{"id":"2007.05916","title":"The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results","created":"2020-07-12","cited_by_count":44},{"id":"2006.05694","title":"HiFi-GAN: High-Fidelity Denoising and Dereverberation Based on Speech Deep Features in Adversarial Networks","created":"2020-06-10","cited_by_count":40},{"id":"1910.06762","title":"T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement","created":"2019-10-13","cited_by_count":34},{"id":"1811.00241","title":"On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition","created":"2018-11-01","cited_by_count":30},{"id":"2210.14567","title":"Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization","created":"2022-10-26","cited_by_count":29},{"id":"2002.05873","title":"Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention","created":"2020-02-14","cited_by_count":28},{"id":"2403.07675","title":"Multichannel Long-Term Streaming Neural Speech Enhancement for Static and Moving Speakers","created":"2024-03-12","cited_by_count":28},{"id":"2106.08672","title":"DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement","created":"2021-06-16","cited_by_count":23},{"id":"2209.11112","title":"CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement","created":"2022-09-22","cited_by_count":23},{"id":"2001.05532","title":"Improving GANs for Speech Enhancement","created":"2020-01-15","cited_by_count":22},{"id":"2309.16953","title":"Enhancing Code-switching Speech Recognition with Interactive Language Biases","created":"2023-09-29","cited_by_count":21},{"id":"2111.08387","title":"S-DCCRN: Super Wide Band DCCRN with learnable complex feature for speech enhancement","created":"2021-11-16","cited_by_count":14},{"id":"2403.05887","title":"Aligning Speech to Languages to Enhance Code-switching Speech Recognition","created":"2024-03-09","cited_by_count":14},{"id":"2202.07790","title":"Speech Denoising in the Waveform Domain with Self-Attention","created":"2022-02-15","cited_by_count":13},{"id":"2308.08926","title":"Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement","created":"2023-08-17","cited_by_count":13},{"id":"2004.04098","title":"WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement","created":"2020-04-06","cited_by_count":12},{"id":"2401.09686","title":"An Empirical Study on the Impact of Positional Encoding in Transformer-based Monaural Speech Enhancement","created":"2024-01-18","cited_by_count":5},{"id":"2102.12078","title":"Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks","created":"2021-02-24","cited_by_count":4},{"id":"2305.08541","title":"Ripple sparse self-attention for monaural speech enhancement","created":"2023-05-15","cited_by_count":3}],"next_cursor":null}