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© Kirill Bykov
February 17, 2022

Shining a light into the Black Box of AI Systems

In the paper “NoiseGrad — Enhancing Explanations by Introducing Stochasticity to Model Weights,” to be presented at the 36th AAAI-22 Conference on Artificial Intelligence, a team of researchers, among them BIFOLD researchers Dr. Marina Höhne, Shinichi Nakajima, PhD, and Kirill Bykov, propose new methods to reduce visual diffusion of the different explanation methods, which have shown to make existing explanation methods more robust and reliable.

© Unsplash
The more features there are in the data, the more difficult machine learning tasks become.
December 21, 2021

Lifting the curse of dimensionality for statistics in ML

The paper “Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation” by BIFOLD researcher Dr. Robert A. Vandermeulen and his colleague Dr. Antoine Ledent, Technical University Kaiserslautern, was presented at the Conference on Neural Information Processing Systems (NeurIPS 2021). Their paper provides the first solid theoretical foundations for applying low-rank methods to nonparametric density estimation.

Being able to predict and model the individual steps of a chemical reaction at the molecular or even atomic level is a long-held dream of many material scientists.
December 15, 2021

Tracking spooky action at a distance

The use of AI in classical sciences such as chemistry, physics, or mathematics remains largely uncharted territory. Researchers from the Berlin Institute for the Foundation of Learning and Data (BIFOLD) at TU Berlin and Google Research have successfully developed an algorithm to precisely and efficiently predict the potential energy state of individual molecules using quantum mechanical data. Their findings, which offer entirely new opportunities for material scientists, have now been published in the paper “SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects” in Nature Communications.

© Wojciech Samek
December 15, 2021

Benchmarking Neural Network Explanations

Neural networks have found their way into many every day applications. During the past years they reached excellent performances on various largescale prediction tasks, ranging from computer vision, language processing or medical diagnosis. Even if in recent years AI research developed various techniques that uncover the decision-making process and detect so called “Clever Hans” predictors – there exists no ground truth-based evaluation framework for such explanation methods. BIFOLD researcher Dr. Wojciech Samek and his colleagues now established an Open Source ground truth framework, that provides a selective, controlled and realistic testbed for the evaluation of neural network explanations. The work will be published in Information Fusion.

December 12, 2021

Two BIFOLD papers ranked as ESI Highly Cited and Hot Papers

Two machine learning papers by BIFOLD researchers received the “Essential Science indicators” (ESI) “Highly Cited” and “Hot Papers” labels for their impact in the science community.

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Social media platforms like Twitter are hubs for many discussions, including health topics. The vast amount of health-related information published there can be processed to learn about population health.
December 02, 2021

Learning about population health from Twitter texts

Is it possible to learn about the health status of a population and potential side effect of medicationsby analyzing social media conversations? BIFOLD researchers tackled the challenge of making social media posts of medical laypersons concerning diseases and medications understandable for machines. At the BioCreative VII Challenge Evaluation Workshop 2021, they recently explored how a combination of background knowledge and a language transformer model can increase the precision of medical information extraction from Twitter texts.

December 02, 2021

BIFOLD researchers honored with BBBAW membership

At the “Einsteintag 2021” event on November 26, which honored Albert Einstein – prominent member of a predecessor institution of the Berlin-Brandenburg Academy of Sciences and Humanities (BBAW) – both BIFOLD Co-Director Volker Markl and BIFOLD Fellow Frank Noé were announced as new BBAW members.

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BIFOLD offers a weekly ML consultation hour: Every Wednesday from 11:00 am – 12:00.
December 01, 2021

Machine Learning consultation

Machine learning (ML) and artificial intelligence (AI) have permeated the sciences and large parts of working life. Today many people use machine learning techniques without being a proven expert. Consequently, many questions and problems arise while using these techniques. BIFOLD accommodates distinguished machine learning experts from different areas and offers a weekly consultation on machine learning for students, but also for companies and institutions.

November 30, 2021

BIFOLD colloquium "Scalable and fast cloud data management"

Event date: December 06, 2021

Norbert Ritter (University of Hamburg), Felix Gessert (Baqend), and Wolfram Wingerath (Baqend) will talk about their scalable and fast cloud data management research at University of Hamburg and Software-as-a-Service company Baqend.

November 23, 2021

Science & Startups launches AI initiative

Science & Startups is the association of the four startup services of Freie Universität Berlin, Humboldt-Universität zu Berlin, Technische Universität Berlin and Charité – Universitätsmedizin Berlin. Now they officially launched their new focus programme: K.I.E.Z. (Künstliche Intelligenz Entrepreneurship Zentrum). K.I.E.Z. will be carried out in close cooperation with the Berlin Institute for the Foundations of Learning and Data (BIFOLD).