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Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinformatics.
The art and practice of structure-based drug design: a molecular modeling perspective
Regine S Bohacek, Colin McMartin, and Wayne C Guida · 1996
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Hmdb: a large video database for human motion recognition
Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre · 2011
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The language of actions: Recovering the syntax and semantics of goal-directed human activities
Hilde Kuehne, Ali Arslan, and Thomas Serre · 2014
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Librispeech: An asr corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur · 2015
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Unitary evolution recurrent neural networks
Martin Arjovsky, Amar Shah, and Yoshua Bengio · 2016
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Wavenet: A generative model for raw audio
Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, Koray Kavukcuoglu, et al · 2016
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Deep clustering: Discriminative embeddings for segmentation and separation
John R Hershey, Zhuo Chen, Jonathan Le Roux, and Shinji Watanabe · 2016
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
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The" something something" video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al · 2017
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Chemical space mimicry for drug discovery
William Yuan, Dadi Jiang, Dhanya K Nambiar, Lydia P Liew, Michael P Hay, Joshua Bloomstein, Peter Lu, Brandon Turner, Quynh-Thu Le, Robert Tibshirani, et al · 2017
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De novo design of bioactive small molecules by artificial intelligence
Daniel Merk, Lukas Friedrich, Francesca Grisoni, and Gisbert Schneider · 2018
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Drew A. Hudson and Christopher D. Manning · 2019
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Timeception for complex action recognition
Noureldien Hussein, Efstratios Gavves, and Arnold WM Smeulders · 2019
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Videograph: Recognizing minutes-long human activities in videos
Noureldien Hussein, Efstratios Gavves, and Arnold WM Smeulders · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Reformer: The efficient transformer
Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya · 2019
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Compressive transformers for long-range sequence modelling
Jack W Rae, Anna Potapenko, Siddhant M Jayakumar, and Timothy P Lillicrap · 2019
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Fast transformer decoding: One write-head is all you need
Noam Shazeer · 2019
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Towards vqa models that can read
Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach · 2019
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Videobert: A joint model for video and language representation learning
Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid · 2019
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Coin: A large-scale dataset for comprehensive instructional video analysis
Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou · 2019
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Wham!: Extending speech separation to noisy environments
Gordon Wichern, Joe Antognini, Michael Flynn, Licheng Richard Zhu, Emmett McQuinn, Dwight Crow, Ethan Manilow, and Jonathan Le Roux · 2019
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An optimistic perspective on offline reinforcement learning
Rishabh Agarwal, Dale Schuurmans, and Mohammad Norouzi · 2020
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Rethinking attention with performers
Krzysztof Marcin Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Quincy Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2020
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X3d: Expanding architectures for efficient video recognition
Christoph Feichtenhofer · 2020
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D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
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Hippo: Recurrent memory with optimal polynomial projections
Albert Gu, Tri Dao, Stefano Ermon, Atri Rudra, and Christopher Ré · 2020
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Conformer: Convolution-augmented Transformer for Speech Recognition
Anmol Gulati, Chung-Cheng Chiu, James Qin, Jiahui Yu, Niki Parmar, Ruoming Pang, Shibo Wang, Wei Han, Yonghui Wu, Yu Zhang, and Zhengdong Zhang, editors · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret · 2020
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Linear dynamical systems as a core computational primitive
Shiva Kaul · 2020
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Ct-net: Channel tensorization network for video classification
Kunchang Li, Xianhang Li, Yali Wang, Jun Wang, and Yu Qiao · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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Sparse sinkhorn attention
Yi Tay, Dara Bahri, Liu Yang, Donald Metzler, and Da-Cheng Juan · 2020
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Long range arena: A benchmark for efficient transformers
Yi Tay, Mostafa Dehghani, Samira Abnar, Yikang Shen, Dara Bahri, Philip Pham, Jinfeng Rao, Liu Yang, Sebastian Ruder, and Donald Metzler · 2020
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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Z Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Big bird: Transformers for longer sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al · 2020
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Vivit: A video vision transformer
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid · 2021
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Is space-time attention all you need for video understanding?
Gedas Bertasius, Heng Wang, and Lorenzo Torresani · 2021
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Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
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Combining generative artificial intelligence and on-chip synthesis for de novo drug design
Francesca Grisoni, Berend JH Huisman, Alexander L Button, Michael Moret, Kenneth Atz, Daniel Merk, and Gisbert Schneider · 2021
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Re · 2021
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Liquid time-constant networks
Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, and Radu Grosu · 2021
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Fnet: Mixing tokens with fourier transforms
James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, and Santiago Ontanon · 2021
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Temporal fusion transformers for interpretable multi-horizon time series forecasting
Bryan Lim, Sercan Ö Arık, Nicolas Loeff, and Tomas Pfister · 2021
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Pay attention to mlps
Hanxiao Liu, Zihang Dai, David So, and Quoc V Le · 2021
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Luna: Linear unified nested attention
Xuezhe Ma, Xiang Kong, Sinong Wang, Chunting Zhou, Jonathan May, Hao Ma, and Luke Zettlemoyer · 2021
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Keeping your eye on the ball: Trajectory attention in video transformers
Mandela Patrick, Dylan Campbell, Yuki Asano, Ishan Misra, Florian Metze, Christoph Feichtenhofer, Andrea Vedaldi, and Joao F Henriques · 2021
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cosformer: Rethinking softmax in attention
Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, and Yiran Zhong · 2021
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An image is worth 16x16 words, what is a video worth?
Gilad Sharir, Asaf Noy, and Lihi Zelnik-Manor · 2021
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Synthesizer: Rethinking self-attention for transformer models
Yi Tay, Dara Bahri, Donald Metzler, Da-Cheng Juan, Zhe Zhao, and Che Zheng · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou · 2021
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Tdn: Temporal difference networks for efficient action recognition
Limin Wang, Zhan Tong, Bin Ji, and Gangshan Wu · 2021
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Towards long-form video understanding
Chao-Yuan Wu and Philipp Krahenbuhl · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Haixu Wu, Jiehui Xu, Jianmin Wang, and Mingsheng Long · 2021
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Nyströmformer: A nyström-based algorithm for approximating self-attention
Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, and Vikas Singh · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang · 2021
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Graph-based high-order relation modeling for long-term action recognition
Jiaming Zhou, Kun-Yu Lin, Haoxin Li, and Wei-Shi Zheng · 2021
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Long-short transformer: Efficient transformers for language and vision
Chen Zhu, Wei Ping, Chaowei Xiao, Mohammad Shoeybi, Tom Goldstein, Anima Anandkumar, and Bryan Catanzaro · 2021
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H-transformer-1d: Fast one-dimensional hierarchical attention for sequences
Zhenhai Zhu and Radu Soricut · 2021
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Masked autoencoders as spatiotemporal learners
Christoph Feichtenhofer, Yanghao Li, Kaiming He, et al · 2022
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Hungry hungry hippos: Towards language modeling with state space models
Daniel Y Fu, Tri Dao, Khaled K Saab, Armin W Thomas, Atri Rudra, and Christopher Ré · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
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It’s raw! audio generation with state-space models
Karan Goel, Albert Gu, Chris Donahue, and Christopher Ré · 2022
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On the parameterization and initialization of diagonal state space models
Albert Gu, Karan Goel, Ankit Gupta, and Christopher Ré · 2022
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Mm-vet: Evaluating large multimodal models for integrated capabilities
Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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Mambatab: A simple yet effective approach for handling tabular data
Md Atik Ahamed and Qiang Cheng · 2024
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Timemachine: A time series is worth 4 mambas for long-term forecasting
Md Atik Ahamed and Qiang Cheng · 2024
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Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, and Chang Xu · 2022
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Diagonal state spaces are as effective as structured state spaces
Ankit Gupta, Albert Gu, and Jonathan Berant · 2022
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Turbo training with token dropout
Tengda Han, Weidi Xie, and Andrew Zisserman · 2022
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Liquid structural state-space models
Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang, Makram Chahine, Alexander Amini, and Daniela Rus · 2022
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Transformer quality in linear time
Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc Le · 2022
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Block-recurrent transformers
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, and Behnam Neyshabur · 2022
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Long movie clip classification with state-space video models
Md Mohaiminul Islam and Gedas Bertasius · 2022
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Quentin Anthony, Yury Tokpanov, Paolo Glorioso, and Beren Millidge · 2024
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Graph mamba: Towards learning on graphs with state space models
Ali Behrouz and Farnoosh Hashemi · 2024
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Mambamixer: Efficient selective state space models with dual token and channel selection
Ali Behrouz, Michele Santacatterina, and Ramin Zabih · 2024
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Chi-Sheng Chen, Guan-Ying Chen, Dong Zhou, Di Jiang, and Dai-Shi Chen · 2024
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Rsmamba: Remote sensing image classification with state space model
Keyan Chen, Bowen Chen, Chenyang Liu, Wenyuan Li, Zhengxia Zou, and Zhenwei Shi · 2024
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Instructblip: Towards general-purpose vision-language models with instruction tuning
Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi · 2024
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Griffin: Mixing gated linear recurrences with local attention for efficient language models
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Monarch mixer: A simple sub-quadratic gemm-based architecture
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Is mamba capable of in-context learning?
Riccardo Grazzi, Julien Siems, Simon Schrodi, Thomas Brox, and Frank Hutter · 2024
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Mambair: A simple baseline for image restoration with state-space model
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Tao Guo, Yinuo Wang, and Cai Meng · 2024
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T-mamba: Frequency-enhanced gated long-range dependency for tooth 3d cbct segmentation
Jing Hao, Lei He, and Kuo Feng Hung · 2024
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Densemamba: State space models with dense hidden connection for efficient large language models
Wei He, Kai Han, Yehui Tang, Chengcheng Wang, Yujie Yang, Tianyu Guo, and Yunhe Wang · 2024
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Localmamba: Visual state space model with windowed selective scan
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Repeat after me: Transformers are better than state space models at copying
Samy Jelassi, David Brandfonbrener, Sham M Kakade, and Eran Malach · 2024
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Xilin Jiang, Cong Han, and Nima Mesgarani · 2024
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Obelics: An open web-scale filtered dataset of interleaved image-text documents
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Spmamba: State-space model is all you need in speech separation
Kai Li and Guo Chen · 2024
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Videomamba: State space model for efficient video understanding
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Stg-mamba: Spatial-temporal graph learning via selective state space model
Lincan Li, Hanchen Wang, Wenjie Zhang, and Adelle Coster · 2024
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Mamba-nd: Selective state space modeling for multi-dimensional data
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Spikemba: Multi-modal spiking saliency mamba for temporal video grounding
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Jamba: A hybrid transformer-mamba language model
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Chemical language modeling with structured state spaces
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Fd-vision mamba for endoscopic exposure correction
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