Home /
Research
Showing 139 - 144 / 904
Backpropagating Linearly Improves Transferability of Adversarial Examples
YiwenGuo, QizhangLi, HaoChen....
Published date-12/01/2020
The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community. In this paper, we study the transferability of such examples, which lays the …
Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming
JoeyHuchette, HaihaoLu, HosseinEsfandiari....
Published date-12/01/2020
We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue from the seller side. …
Learning to Adapt to Evolving Domains
HongLiu, MingshengLong, JianminWang....
Published date-12/01/2020
DomainAdaptation, Meta-Learning, TransferLearning, UnsupervisedDomainAdaptation
Domain adaptation aims at knowledge transfer from a labeled source domain to an unlabeled target domain. Current domain adaptation methods have made substantial advances in adapting discrete domains. However, this …
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain
JinsungYoon, YaoZhang, JamesJordon....
Published date-12/01/2020
DataAugmentation, Imputation, Self-SupervisedLearning
Self- and semi-supervised learning frameworks have made significant progress in training machine learning models with limited labeled data in image and language domains. These methods heavily rely on the unique …
Make One-Shot Video Object Segmentation Efficient Again
TimMeinhardt, LauraLeal-Taixé....
Published date-12/01/2020
ObjectDetection, SemanticSegmentation, VideoObjectSegmentation, VideoSemanticSegmentation, Youtube-VOS
Video object segmentation (VOS) describes the task of segmenting a set of objects in each frame of a video. In the semi-supervised setting, the first mask of each object is …
SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology
MarkVeillette, SiddharthSamsi, ChrisMattioli....
Published date-12/01/2020
Modern deep learning approaches have shown promising results in meteorological applications like precipitation nowcasting, synthetic radar generation, front detection and several others. In order to effectively train and validate these …