Visit the "Research in Germany" virtual booth for an overview of the German research landscape in artificial intelligence and machine learning. Come and talk to representatives from various funding organizations, as well as representatives from the AI-research landscape in Germany.
Website: "Research in Germany“ at the NeurIPS2020 online conference.
The virtual NeurIPS schedule
During the “LatinX in AI Research Workshop” of the organisation “LatinX in AI” on 7 December 2020, which was hosted as part of the NeurIPS online conference, the Bremen-based DFKI and university scientists Dr. Matias Valdenegro (Robotics Innovation Center) and Octavio Arriaga (Department of Computer Science, University of Bremen) received the Best Paper Award for their oral presentation on the topic “Unsupervised Difficulty Estimation with Action Scores”. The paper presents a simple method for the calculation of an “Action Score”, which can be used as an estimate of the difficulty of samples in a dataset. This is useful to find biases and problems in machine learning models and datasets, aiding scientists and engineers to produce better and fairer models.
The organisation “LatinX in AI” (LXAI) supports Latin-American AI researchers and organises workshops as part of international conferences such as NeurIPS (International Conference on Neural Information Processing Systems). The paper can be found on the LatinX in AI website.
"Generative View Synthesis: From Single-view Semantics to Novel-view Images“
Tewodros A. Habtegebrial, TU Kaiserslautern; Varun Jampani, Google Research; Orazio Gallo, NVIDIA; Didier Stricker, DFKI Kaiserslautern / TU Kaiserslautern
Time Series Visualization Framework – TSViz Deep neural networks are being deployed in a range of different applications, which poses a great question on their reliability and robustness. In order to tackle the goal of interpretability of DNNs, DFKI developed a framework that acts as a bridge between the researches, developers, and users to provide suited information about the decisions of neural networks for time-series analysis.
exAID enables medical professionals to understand and use AI's diagnosis as an aid in clinical practice. Additionally, the extensive model & data analysis helps future dermatologists & medical researchers to better understand patterns in data & developers to identify biases in the model.
Shortly after the advent of Deep Learning, DFKI launched its Deep Learning Competence Center (DLCC) with the cross-sectional functionality to connect and bundle Deep Learning competences throughout the DFKI. Across all research departments, DFKI's DLCC brings together hundreds of experts focusing on the development and application of Deep Learning approaches in their respective fields. Apart from pushing the limits in each domain, the DLCC especially focuses on current DL specific research areas such as Efficiency, XAI, Robustness, Trustworthy AI, Small Data, Unsupervised / Self-supervised Learning, Generative Approaches.
The DLCC also functions as a central contact point for external researchers and experts interested in collaboration, and bundles the DFKI's efforts to teach novel Deep Learning approaches to a new generation of students, scientists and employees embracing machine learning.
The goal of the TreeSatAI project is the development of artificial intelligence methods for the monitoring of forests and tree populations at local, regional and global level. The project is funded by the German Federal Ministry of Education and Research (BMBF).
Using freely accessible geodata from different sources (remote sensing data, administrative information, social media, mobile apps, monitoring libraries, open image databases) prototypes for deep learning based extraction and classification of tree and stand features for four different use cases in the fields of forest, nature conservation and infrastructure monitoring are developed.
Interactive Machine Learning (IML) is the design and implementation of algorithms and intelligent user interface (IUI) frameworks that facilitate machine learning (ML) with the help of human interaction.
With the convergence of Artificial intelligence and Machine Learning, IML is where the Human-Computer Interaction (HCI) and IUI community meets the ML community. We also combine contributions from related fields such as data science, cognitive science, computer graphics, design or the arts, and natural language processing, data mining/data analytics, and knowledge representation and reasoning. Our focus is to improve the interaction between humans and machines to update ML models, by leveraging both state-of-the-art HCI and IUI approaches, as well as solutions that involve state-of-the-art ML techniques.
“Research in Germany” is an initiative of the German Federal Ministry of Education and Research. It presents Germany as a country of research and innovation and provides a forum for international exchange and cooperation.
At NeurIPS, the initiative presents the AI research landscape in Germany. Learn more about research at the Cluster of Excellence “Machine Learning,” Cyber Valley, the German Research Center for Artificial Intelligence (DFKI), and the Max Planck Society as well as about funding programmes for AI researchers with the Alexander von Humboldt Foundation, the German Academic Exchange Service, and the German Research Foundation.
https://neurips.cc/ExpoConferences/2020/Sponsorpage?id=655 (registered users only)
Social event of the German Network of National Centers of Excellence for AI Research in collaboration with international partners (please register for NeurIPS to participate)
A Decemberfest on Trustworthy AI Research - An overview and panel discussion with virtual drinks and Bretzels
Date: Tue, Dec 8th, 2020, 21:00 – 23:00 CET
What do we mean by Trust in AI? Why does it matter? What influence can technology have in building trust? These and further questions will be addressed during the two-part gathering for interested attendees. The event will start with a block of elevator pitches on the topic of Trustworthy AI held by researchers of the German Network of National Centers of Excellence for AI Research, in collaboration with international partners. This will then lead to the second part, a panel and audience discussion focused on central questions regarding Trustworthy AI. In addition to this semi-formal program, a social gathering space with topical corners and a hang-out space will allow a convivial get-together.
NeurIPS 2020 official webpage:
The virtual NeurIPS schedule:
The purpose of the Neural Information Processing Systems annual meeting is to foster the exchange of research on neural information processing systems in their biological, technological, mathematical, and theoretical aspects. The core focus is peer-reviewed novel research which is presented and discussed in the general session, along with invited talks by leaders in their field. On Sunday is an Expo, where our top industry sponsors give talks, panels, demos, and workshops on topics that are of academic interest. On Monday are tutorials, which cover a broad background on current lines of inquiry, affinity group meetings, and the opening talk & reception. The general sessions are held Tuesday - Thursday, and include talks, posters, and demonstrations. Friday - Saturday are the workshops, which are smaller meetings focused on current topics, and provide an informal, cutting edge venue for discussion.