196 citations · 1.1k across the 20 of their papers we have counts for
21 papers
COVID-BLUeS -- A Prospective Study on the Value of AI in Lung Ultrasound Analysis
Nina Wiedemann, Dianne de Korte-de Boer, Matthias Richter +10
As a lightweight and non-invasive imaging technique, lung ultrasound (LUS) has gained importance for assessing lung pathologies. The use of Artificial intelligence (AI) in medical…
Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap
Alice Driessen, Benedek Harsanyi, Marianna Rapsomaniki +1
Learning the response of single-cells to various treatments offers great potential to enable targeted therapies. In this context, neural optimal transport (OT) has emerged as a pri…
We Need Improved Data Curation and Attribution in AI for Scientific Discovery
Mara Graziani, Antonio Foncubierta, Dimitrios Christofidellis +5
As the interplay between human-generated and synthetic data evolves, new challenges arise in scientific discovery concerning the integrity of the data and the stability of the mode…
Quantum Doubly Stochastic Transformers
Jannis Born, Filip Skogh, Kahn Rhrissorrakrai +3
At the core of the Transformer, the softmax normalizes the attention matrix to be right stochastic. Previous research has shown that this often de-stabilizes training and that enfo…
Multiscale Byte Language Models -- A Hierarchical Architecture for Causal Million-Length Sequence Modeling
Eric Egli, Matteo Manica, Jannis Born
Bytes form the basis of the digital world and thus are a promising building block for multimodal foundation models. Recently, Byte Language Models (BLMs) have emerged to overcome t…
Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Models
Jonas Zausinger, Lars Pennig, Anamarija Kozina +13
While language models have exceptional capabilities at text generation, they lack a natural inductive bias for emitting numbers and thus struggle in tasks involving quantitative re…
Quantum Theory and Application of Contextual Optimal Transport
Nicola Mariella, Albert Akhriev, Francesco Tacchino +9
Optimal Transport (OT) has fueled machine learning (ML) across many domains. When paired data measurements are coupled to covariates, a challenging c…
Language models in molecular discovery
Nikita Janakarajan, Tim Erdmann, Sarath Swaminathan +2
The success of language models, especially transformer-based architectures, has trickled into other domains giving rise to "scientific language models" that operate on small molecu…
Towards quantum-enabled cell-centric therapeutics
Saugata Basu, Jannis Born, Aritra Bose +30
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to the expectation that in the near future qua…
Domain-agnostic and Multi-level Evaluation of Generative Models
Girmaw Abebe Tadesse, Jannis Born, Celia Cintas +4
While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplifi…
Unifying Molecular and Textual Representations via Multi-task Language Modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born +3
The recent advances in neural language models have also been successfully applied to the field of chemistry, offering generative solutions for classical problems in molecular desig…
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
Matteo Manica, Jannis Born, Joris Cadow +21
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful represe…
Regression Transformer: Concurrent sequence regression and generation for molecular language modeling
Jannis Born, Matteo Manica
Despite significant progress of generative models in the natural sciences, their controllability remains challenging. One fundamentally missing aspect of molecular or protein gener…
TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks
Anna Weber, Jannis Born, María Rodríguez Martínez
Motivation: The activity of the adaptive immune system is governed by T-cells and their specific T-cell receptors (TCR), which selectively recognize foreign antigens. Recent advanc…
Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis
Jannis Born, Nina Wiedemann, Gabriel Brändle +3
Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical…
Computer Vision For COVID-19 Control: A Survey
Anwaar Ulhaq, Asim Khan, Douglas Gomes +1
The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intell…
PaccMann on SARS-CoV-2: Designing antiviral candidates with conditional generative models
Jannis Born, Matteo Manica, Joris Cadow +4
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents. Bridging systems bio…
POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
Jannis Born, Gabriel Brändle, Manuel Cossio +4
With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-1…
PaccMann: Designing anticancer drugs from transcriptomic data via reinforcement learning
Jannis Born, Matteo Manica, Ali Oskooei +3
With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep le…
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
Matteo Manica, Ali Oskooei, Jannis Born +3
In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using…
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Ali Oskooei, Jannis Born, Matteo Manica +3
We present a novel approach for the prediction of anticancer compound sensitivity by means of multi-modal attention-based neural networks (PaccMann). In our approach, we integrate…