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5 papers · 2 filters
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
Nils Rethmeier, Vageesh Kumar Saxena, Isabelle Augenstein
While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify mo…
Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification
Matias Valdenegro-Toro
Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but…
Incremental Improvement of a Question Answering System by Re-ranking Answer Candidates using Machine Learning
Michael Barz, Daniel Sonntag
We implement a method for re-ranking top-10 results of a state-of-the-art question answering (QA) system. The goal of our re-ranking approach is to improve the answer selection giv…
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
Mohsin Munir, Shoaib Ahmed Siddiqui, Ferdinand Küsters +3
Neural networks (NN) are considered as black-boxes due to the lack of explainability and transparency of their decisions. This significantly hampers their deployment in environment…
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
Alireza Koochali, Peter Schichtel, Sheraz Ahmed +1
Time series forecasting is one of the challenging problems for humankind. Traditional forecasting methods using mean regression models have severe shortcomings in reflecting real-w…