27 citations · 46 across the 8 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in training data is pivotal for sustaining robustness in neural networks, especially when removing specific biases or outdated informati…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…