Related papers: Proceedings of NIPS 2017 Symposium on Interpretabl…

Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Data Sources: Papers published in the Neural Information…

This is the Proceedings of the First Conference on Uncertainty in Artificial Intelligence, which was held in Los Angeles, CA, July 10-12, 1985

We present a novel approach to modeling the ground state mass of atomic nuclei based directly on a probabilistic neural network constrained by relevant physics. Our Physically Interpretable Machine Learning (PIML) approach incorporates…

This is the Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021. This index is not complete, as some accepted abstracts chose to opt-out of inclusion.

This book contains the accepted papers at ProSocrates 2017 Symposium: Problem-solving,Creativity and Spatial Reasoning in Cognitive Systems. ProSocrates 2017 symposium was held at the Hansewissenschaftkolleg (HWK) of Advanced Studies in…

This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011.

An important feature of successful supervised machine learning applications is to be able to explain the predictions given by the regression or classification model being used. However, most state-of-the-art models that have good predictive…

This volume contains the proceedings of the Fifth International Workshop on Verification and Program Transformation (VPT 2017). The workshop took place in Uppsala, Sweden, on April 29th, 2017, affiliated with the European Joint Conferences…

We present MIPS, a novel method for program synthesis based on automated mechanistic interpretability of neural networks trained to perform the desired task, auto-distilling the learned algorithm into Python code. We test MIPS on a…

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 exceeding human performance, e.g. predicting RNA structure from…

This volume constitutes the pre-proceedings of the 31st International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2021), held on 7-8th September 2021 as a hybrid (blended) meeting, both in-person (at the Teachers'…

These are the revised accepted papers from the 26th International Symposium on Graph Drawing and Network Visualization (GD 2018), Barcelona, Spain, September 26 - September 28, 2018. Proceedings are also to be published by Springer in the…

This EPTCS volume collects the post-proceedings of the 10th International Workshop On User Interfaces for Theorem Provers (UITP 2012), held as part of the Conferences on Intelligent Computer Mathematics (CICM 2012) in Bremen on July 11th…

This volume contains the proceedings of the Sixth Workshop on Intersection Types and Related Systems (ITRS 2012). The workshop was held in Dubrovnik (Croatia) on June 29th, 2012, affiliated to Twenty-Seventh Annual ACM/IEEE Symposium on…

The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant…

High-stakes applications require AI-generated models to be interpretable. Current algorithms for the synthesis of potentially interpretable models rely on objectives or regularization terms that represent interpretability only coarsely…

Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in…

Recent efforts in Machine Learning (ML) interpretability have focused on creating methods for explaining black-box ML models. However, these methods rely on the assumption that simple approximations, such as linear models or decision-trees,…

This volume contains the proceedings of the First Workshop on Synthesis (SYNT 2012). The workshop is held is held in Berkeley, California, on June 6th and 7th, as a satellite event to the 24th International Conference on Computer Aided…