Europe
Structural Causal Bandits: Where to Intervene?
Sanghack Lee, Elias Bareinboim
Neural Information Processing Systems http://nips.cc/
Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
This raises a challenging research question: How can we keep improving the systems when their capabilities have surpassed the levels of humans?
Training deep learning based denoisers without ground truth data
Shakarim Soltanayev, Se Young Chun
Neural Information Processing Systems http://nips.cc/
RecurrentKernelNetworks
However,whenlargeamounts ofannotated dataareavailable, models thatallow end-to-end training such as neural networks are often preferred.
dopanim: A Dataset of Doppelganger Animals with Noisy Annotations from Multiple Humans
Key attributes (cf. Figure 1) include the challenging task of classifying doppelganger animals,
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn
Neural Information Processing Systems http://nips.cc/
Neural Machine Translation with Soft Prototype
Yiren Wang, Yingce Xia, Fei Tian, Fei Gao, Tao Qin, Cheng Xiang Zhai, Tie-Yan Liu
This work is conducted at Microsoft Research Asia.
730ce0ae730f39e4d77b0f04a8afe4be-Supplemental-Conference.pdf
This paper studies the use of a machine learning-based estimator as a control variate for mitigating the variance of Monte Carlo sampling. Specifically, we seek to uncover the key factors that influence the efficiency of control variates in reducing variance.