Asia
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Neural Information Processing Systems http://nips.cc/
Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge
Tingting Qiao, Jing Zhang, Duanqing Xu, Dacheng Tao
Given a text description, we immediately imagine an overall visual impression using this prior and, based on this, we draw a picture by progressively adding more andmore details.
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone
Neural Information Processing Systems http://nips.cc/
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
Neural Information Processing Systems http://nips.cc/
Norm-Ranging LSH for Maximum Inner Product Search
Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng
MIPS is a challenging problem as modern datasets often have high dimensionality and large cardinality. Initially, tree-based methods [Ram and Gray, 2012, Koenigstein et al., 2012] were proposed for MIPS, which use the idea of branch and bound similar to k-d tree [Friedman and Tukey, 1974].
Consistency-based Semi-supervised Learning for Object detection
Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak
While the object detection task requires a huge number of annotated samples to guarantee its performance, placing bounding boxes for every object in each sample is time-consuming and costs alot.
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis
Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin, Edward Chang
Neural Information Processing Systems http://nips.cc/