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Found 5 papers, 1 papers with code
Conformal Autoregressive Generation: Beam Search with Coverage Guarantees
We introduce two new extensions to the beam search algorithm based on conformal predictions (CP) to produce sets of sequences with theoretical coverage guarantees.
Adaptive Conformal Regression with Jackknife+ Rescaled Scores
We address this with a new adaptive method based on rescaling conformal scores with an estimate of local score distribution, inspired by the Jackknife+ method, which enables the use of calibration data in conformal scores without breaking calibration-test exchangeability.
Attention-based Interpretable Regression of Gene Expression in Histology
Interpretability of deep learning is widely used to evaluate the reliability of medical imaging models and reduce the risks of inaccurate patient recommendations.
Is Attention Interpretation? A Quantitative Assessment On Sets
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-components of data.
Accelerating HEP simulations with Neural Importance Sampling
Virtually all high-energy-physics (HEP) simulations for the LHC rely on Monte Carlo using importance sampling by means of the VEGAS algorithm.
