Country
HOUDINI: Lifelong Learning as Program Synthesis
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri
Neural Information Processing Systems http://nips.cc/
Park: An Open Platform for Learning-Augmented Computer Systems
Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, ravichandra addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Dr.Mohammad Alizadeh
Neural Information Processing Systems http://nips.cc/
Reduced Policy Optimization for Continuous Control with Hard Constraints Shutong Ding 1 Jingya Wang 1 Yali Du2Y e Shi 1 1
Recent advances in constrained reinforcement learning (RL) have endowed reinforcement learning with certain safety guarantees.
(Probably) Concave Graph Matching
From acomputational point of view,this problem is equivalent to the quadratic assignment problem, and as such is an NP-hard problem (Burkard et al., 1998).
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
Jiecao Chen, Qin Zhang, Yuan Zhou
Neural Information Processing Systems http://nips.cc/
FastSpeech: Fast, Robust and Controllable Text to Speech
Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, Tie-Yan Liu
Prominent methods (e.g., Tacotron 2)usuallyfirst generate mel-spectrogram from text, and then synthesize speech from themel-spectrogram using vocoder such as WaveNet. Compared with traditionalconcatenative and statistical parametric approaches, neural network based end-to-end models suffer from slow inference speed, and the synthesized speech isusually not robust (i.e., some words are skipped or repeated) and lack of con-trollability (voice speed or prosody control).
Ignorance is Bliss: Robust Control via Information Gating Manan Tomar
We propose information gating as a way to learn parsimonious representations that identify the minimal information required for a task.
