Search Results for author:
Found 16 papers, 5 papers with code
Using Game Engines and Machine Learning to Create Synthetic Satellite Imagery for a Tabletop Verification Exercise
Satellite imagery is regarded as a great opportunity for citizen-based monitoring of activities of interest.
Generating Synthetic Satellite Imagery With Deep-Learning Text-to-Image Models -- Technical Challenges and Implications for Monitoring and Verification
In this work, we use novel DL models to explore how synthetic satellite images can be created using conditioning mechanisms.
Interpretable Time Series Models for Wastewater Modeling in Combined Sewer Overflows
Climate change poses increasingly complex challenges to our society.
Changes in Policy Preferences in German Tweets during the COVID Pandemic
Quantifying political preferences in online social media remains challenging: The vast amount of content requires scalable automated extraction of political preferences -- however fine grained political preference extraction is difficult with current machine learning (ML) technology, due to the lack of data sets.
Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data
Extracting structured information from unstructured data is one of the key challenges in modern information retrieval applications, including e-commerce.
GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods
We present initial results demonstrating that ML models trained with our data can reliably (F1 score 96%) predict the sustainability label of products.
GreenDB: Toward a Product-by-Product Sustainability Database
We present our proof of concept implementation of a scraping system that creates the GreenDB dataset.
CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile Motion Sensors
In cities worldwide, cars cause health and traffic problems whichcould be partly mitigated through an increased modal share of bicycles.
More Than Words: Towards Better Quality Interpretations of Text Classifiers
The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of trust by the users.
Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated
Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans.
BIG-bench Machine Learning
Explainable artificial intelligence
+1
A Turing Test for Transparency
This effect challenges the very goal of XAI and implies that responsible usage of transparent AI methods has to consider the ability of humans to distinguish machine generated from human explanations.
Binary Classification
Explainable artificial intelligence
+3
A psychophysics approach for quantitative comparison of interpretable computer vision models
While there are clear advantages of evaluations with no humans in the loop, such as scalability, reproducibility and less algorithmic bias than with humans in the loop, these metrics are limited in their usefulness if we do not understand how they relate to other metrics that take human cognition into account.
Quantifying Interpretability and Trust in Machine Learning Systems
Our results complement existing qualitative work on trust and interpretability by quantifiable measures that can serve as objectives for further improving methods in this field of research.
Automating Political Bias Prediction
Standard text features extracted from speeches and manifestos of political parties are used to predict political bias in terms of political party affiliation and in terms of political views.
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance
From an information-theoretic perspective, a noisy transmission system such as a visual Brain-Computer Interface (BCI) speller could benefit from the use of error-correcting codes.
