196 citations · 243 across the 10 of their papers we have counts for
11 papers
Unlearning Information Bottleneck: Machine Unlearning of Systematic Patterns and Biases
Ling Han, Hao Huang, Dustin Scheinost +2
Effective adaptation to distribution shifts in training data is pivotal for sustaining robustness in neural networks, especially when removing specific biases or outdated informati…
T cell receptor binding prediction: A machine learning revolution
Anna Weber, Aurélien Pélissier, María Rodríguez Martínez
Recent advancements in immune sequencing and experimental techniques are generating extensive T cell receptor (TCR) repertoire data, enabling the development of models to predict T…
Conformal Autoregressive Generation: Beam Search with Coverage Guarantees
Nicolas Deutschmann, Marvin Alberts, María Rodríguez Martínez
We introduce two new extensions to the beam search algorithm based on conformal predictions (CP) to produce sets of sequences with theoretical coverage guarantees. The first method…
Attention-based Interpretable Regression of Gene Expression in Histology
Mara Graziani, Niccolò Marini, Nicolas Deutschmann +3
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations. For models exceed…
Is Attention Interpretation? A Quantitative Assessment On Sets
Jonathan Haab, Nicolas Deutschmann, Maria Rodríguez Martínez
The debate around the interpretability of attention mechanisms is centered on whether attention scores can be used as a proxy for the relative amounts of signal carried by sub-comp…
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…
Learning Invariances for Interpretability using Supervised VAE
An-phi Nguyen, María Rodríguez Martínez
We propose to learn model invariances as a means of interpreting a model. This is motivated by a reverse engineering principle. If we understand a problem, we may introduce inducti…
On quantitative aspects of model interpretability
An-phi Nguyen, María Rodríguez Martínez
Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment…
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…
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data
Ali Oskooei, Sophie Mai Chau, Jonas Weiss +3
In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR inte…
Inferring clonal composition from multiple tumor biopsies
Matteo Manica, Hyunjae Ryan Kim, Roland Mathis +14
Explicit accounting for copy number alterations can dramatically improve mutation frequency estimates, leading to more accurate phylogeny reconstructions and subclone characterizat…