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Minimax Pareto Fairness: A Multi Objective Perspective
NataliaMartinez, MartinBertran, GuillermoSapiro....
Published date-11/03/2020
fairness
In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where …
Brain Predictability toolbox: a Python library for neuroimaging based machine learning
SageHahn, DekangYuan, WesleyThompson....
Published date-11/03/2020
Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (in particular brain, psychiatric, behavioral, and physiological variables) and …
Minimum Bayes Risk Training for End-to-End Speaker-Attributed ASR
NaoyukiKanda, ZhongMeng, LiangLu....
Published date-11/03/2020
SpeakerIdentification, SpeechRecognition
Recently, an end-to-end speaker-attributed automatic speech recognition (E2E SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech. In the …
DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection
JoshuaMelton, ArunkumarBagavathi, SiddharthKrishnan....
Published date-11/03/2020
HateSpeechDetection, TransferLearning
Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several main-stream (Twitter and Facebook) and fringe …
Shift If You Can: Counting and Visualising Correction Operations for Beat Tracking Evaluation
A.SáPinto, I.Domingues, M.E.P.Davies....
Published date-11/03/2020
In this late-breaking abstract we propose a modified approach for beat tracking evaluation which poses the problem in terms of the effort required to transform a sequence of beat detections …
The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets
VidKocijan, Oana-MariaCamburu, ThomasLukasiewicz....
Published date-11/03/2020
CoreferenceResolution
Diagnostic datasets that can detect biased models are an important prerequisite for bias reduction within natural language processing. However, undesired patterns in the collected data can make such tests incorrect. …