Home /
Research
Showing 397 - 402 / 904
Born Identity Network: Multi-way Counterfactual Map Generation to Explain a Classifier's Decision
KwanseokOh, JeeSeokYoon, Heung-IlSuk....
Published date-11/20/2020
There exists an apparent negative correlation between performance and interpretability of deep learning models. In an effort to reduce this negative correlation, we propose Born Identity Network (BIN), which is …
Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews
QuocThaiNguyen, ThoaiLinhNguyen, NgocHoangLuong....
Published date-11/20/2020
SentimentAnalysis
Sentiment analysis is an important task in the field ofNature Language Processing (NLP), in which users' feedbackdata on a specific issue are evaluated and analyzed. Manydeep learning models have been …
Learning Informative Representations of Biomedical Relations with Latent Variable Models
HarshilShah, JulienFauqueur....
Published date-11/20/2020
LatentVariableModels, RelationExtraction
Extracting biomedical relations from large corpora of scientific documents is a challenging natural language processing task. Existing approaches usually focus on identifying a relation either in a single sentence (mention-level) …
ATSal: An Attention Based Architecture for Saliency Prediction in 360 Videos
YasserDahou, MarouaneTliba, KevinMcGuinness....
Published date-11/20/2020
SaliencyPrediction
The spherical domain representation of 360 video/image presents many challenges related to the storage, processing, transmission and rendering of omnidirectional videos (ODV). Models of human visual attention can be used …
Action Duration Prediction for Segment-Level Alignment of Weakly-Labeled Videos
RezaGhoddoosian, SaifSayed, VassilisAthitsos....
Published date-11/20/2020
This paper focuses on weakly-supervised action alignment, where only the ordered sequence of video-level actions is available for training. We propose a novel Duration Network, which captures a short temporal …
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving
DiFeng, AliHarakeh, StevenWaslander....
Published date-11/20/2020
AutonomousDriving, ObjectDetection
Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have …