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Learning-AugmentedAlgorithmsforOnlineLinear andSemidefiniteProgramming
We show that if the predictor is accurate, we can efficiently bypass these impossibility results and achieve a constant-factor approximation to the optimal solution, i.e., consistency.
MetaAnchor: Learning to Detect Objects with Customized Anchors
Tong Yang, Xiangyu Zhang, Zeming Li, Wenqiang Zhang, Jian Sun
Neural Information Processing Systems http://nips.cc/
Object landmark discovery through unsupervised adaptation
Enrique Sanchez, Georgios Tzimiropoulos
Similarly to previous methods, our approach is fully unsupervised in a sense that it does not require or make any use of annotated landmarks for the target object category.
ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, Boris Katz
ObjectNet is the same size as the ImageNet test set (50,000 images), and by design does not comepaired withatraining setinordertoencourage generalization.
Neural Ordinary Differential Equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David K. Duvenaud
Neural Information Processing Systems http://nips.cc/
cell
However,theanalysis ofsuchdataposes challenges due to the high levels of noise, sparsity, and data scale encountered.
Private Hypothesis Selection
Mark Bun, Gautam Kamath, Thomas Steinke, Steven Z. Wu
We provide a differentially private algorithm for hypothesis selection.