1,557 research outputs found
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people to
understand and act on algorithmic predictions. An intriguing class of
explanations is through counterfactuals, hypothetical examples that show people
how to obtain a different prediction. We posit that effective counterfactual
explanations should satisfy two properties: feasibility of the counterfactual
actions given user context and constraints, and diversity among the
counterfactuals presented. To this end, we propose a framework for generating
and evaluating a diverse set of counterfactual explanations based on
determinantal point processes. To evaluate the actionability of
counterfactuals, we provide metrics that enable comparison of
counterfactual-based methods to other local explanation methods. We further
address necessary tradeoffs and point to causal implications in optimizing for
counterfactuals. Our experiments on four real-world datasets show that our
framework can generate a set of counterfactuals that are diverse and well
approximate local decision boundaries, outperforming prior approaches to
generating diverse counterfactuals. We provide an implementation of the
framework at https://github.com/microsoft/DiCE.Comment: 13 page
Adaptive Testing of Computer Vision Models
Vision models often fail systematically on groups of data that share common
semantic characteristics (e.g., rare objects or unusual scenes), but
identifying these failure modes is a challenge. We introduce AdaVision, an
interactive process for testing vision models which helps users identify and
fix coherent failure modes. Given a natural language description of a coherent
group, AdaVision retrieves relevant images from LAION-5B with CLIP. The user
then labels a small amount of data for model correctness, which is used in
successive retrieval rounds to hill-climb towards high-error regions, refining
the group definition. Once a group is saturated, AdaVision uses GPT-3 to
suggest new group descriptions for the user to explore. We demonstrate the
usefulness and generality of AdaVision in user studies, where users find major
bugs in state-of-the-art classification, object detection, and image captioning
models. These user-discovered groups have failure rates 2-3x higher than those
surfaced by automatic error clustering methods. Finally, finetuning on examples
found with AdaVision fixes the discovered bugs when evaluated on unseen
examples, without degrading in-distribution accuracy, and while also improving
performance on out-of-distribution datasets.Comment: ICCV camera-read
Quantum dark solitons in Bose gas confined in a hard wall box
Schr\"odinger equation for Bose gas with repulsive contact interactions in
one-dimensional space may be solved analytically with the help of the Bethe
ansatz if we impose periodic boundary conditions. It was shown that in such a
system there exist many-body eigenstates directly corresponding to dark soliton
solutions of the mean-field equation. The system is still integrable if one
switches from the periodic boundary conditions to an infinite square well
potential. The corresponding eigenstates were constructed by M. Gaudin. We
analyze weak interaction limit of Gaudin's solutions and identify
parametrization of eigenstates strictly connected with single and multiple dark
solitons. Numerical simulations of detection of particle's positions reveal
dark solitons in the weak interaction regime and their quantum nature in the
presence of strong interactions.Comment: 7 pages, 4 figures, version accepted for publication in Phys. Rev.
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