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10 papers · 1 filter
"When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams
Bhaskar Gautam, Annappa Basava, Abhishek Singh +1
The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge…
FA-RPN: Floating Region Proposals for Face Detection
Mahyar Najibi, Bharat Singh, Larry S. Davis
We propose a novel approach for generating region proposals for performing face-detection. Instead of classifying anchor boxes using features from a pixel in the convolutional feat…
Learning to Segment Inputs for NMT Favors Character-Level Processing
Julia Kreutzer, Artem Sokolov
Most modern neural machine translation (NMT) systems rely on presegmented inputs. Segmentation granularity importantly determines the input and output sequence lengths, hence the m…
Generative Low-Shot Network Expansion
Adi Hayat, Mark Kliger, Shachar Fleishman +1
Conventional deep learning classifiers are static in the sense that they are trained on a predefined set of classes and learning to classify a novel class typically requires re-tra…
Subsampled Rényi Differential Privacy and Analytical Moments Accountant
Yu-Xiang Wang, Borja Balle, Shiva Kasiviswanathan
We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Spec…
Linear Bandits with Stochastic Delayed Feedback
Claire Vernade, Alexandra Carpentier, Tor Lattimore +3
Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and rec…