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Thinning for Accelerating the Learning of Point Processes
This paper discusses one of the most fundamental issues about point processes that what is the best sampling method for point processes. We propose thinning as a downsampling method for accelerating the learning of point processes.
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.
Doubly-Robust Lasso Bandit
While therewardcompensation mechanism isunknown,the learner can adapt his (her) decision to the past reward feedback so as to maximize the sum of rewards.
Self-Erasing Network for Integral Object Attention
Qibin Hou, PengTao Jiang, Yunchao Wei, Ming-Ming Cheng
To tackle such an issue as well as promote the quality of object attention, we introduce asimple yet effectiveSelfErasing Network (SeeNet) to prohibit attentions from spreading to unexpected background regions.