sequence model
Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers
A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers.
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Recasting Continual Learning as Sequence Modeling
That is, we propose to formulate continual learning as a sequence modeling problem, allowing advanced sequence models to be utilized for continual learning.
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Partially-Supervised Image Captioning
Peter Anderson, Stephen Gould, Mark Johnson
Neural Information Processing Systems http://nips.cc/
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Fast Structured Decoding for Sequence Models
Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, Zhihong Deng
Neural Information Processing Systems http://nips.cc/
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Bootstrapped Transformer for Offline Reinforcement Learning Kerong Wang Shanghai Jiao Tong University Hanye Zhao Shanghai Jiao Tong University Xufang Luo Microsoft Research Asia Kan Ren
The work was conducted during the internship of Kerong Wang and Hanye Zhao at Microsoft Research.
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STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments.
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