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Coda: An End-to-End Neural Program Decompiler
Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao
Neural Information Processing Systems http://nips.cc/
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
This work was supported in part by the National Science Foundation under grant CCF-2149588 and Cisco, Inc.
Self-Supervised Normalization for Non-autoregressive Speech-to-speech Translation
Non-autoregressive Transformers (NA Ts) are recently applied in direct speech-to-speech translation systems, which convert speech across different languages without intermediate text data.
Reward is Enough for Convex MDPs
Maximising a cumulative reward function that is Markov and stationary, i.e., defined over state-action pairs and independent of time, is sufficient to capture many
Reusable Slotwise Mechanisms
However, achieving this capability necessitates not only an effective scene representation but also an understanding of the mechanisms governing interactions among object subsets. Recent studies have made significant progress in representing scenes using object slots.
Reusable Slotwise Mechanisms
However, achieving this capability necessitates not only an effective scene representation but also an understanding of the mechanisms governing interactions among object subsets. Recent studies have made significant progress in representing scenes using object slots.