Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions

Model-agnostic feature attributions can provide local insights in complex ML models. If the explanation is correct, a domain expert can validate and trust the model's decision. However, if it contradicts the expert's knowledge, related work only corrects irrelevant features to improve the model. To allow for unlimited interaction, in this paper we provide model-agnostic implementations for two popular explanation methods (Occlusion and Shapley values) to enforce entirely different attributions in the complex model. For a particular set of samples, we use the corrected feature attributions to generate extra local data, which is used to retrain the model to have the right explanation for the samples. Through simulated and real data experiments on a variety of models we show how our proposed approach can significantly improve the model's performance only by augmenting its training dataset based on corrected explanations. Adding our interactive explanations to active learning settings increases the sample efficiency significantly and outperforms existing explanatory interactive strategies. Additionally we explore how a domain expert can provide feature attributions which are sufficiently correct to improve the model

Peri-articular histiocytic sarcoma and synovial cell sarcoma in Bernese Mountain dogs : a retrospective investigation of the prevalence of these tumours in association with previously diseased joints

C

The implications of K-Ar glauconite dating of the Diest Formation on the paleogeography of the Upper Miocene in Belgium

The glauconite-rich Diest Formation in central and north Belgium contains sands in the Campine subsurface and the hilly Hageland area that can be distinguished from each other. The Hageland Diest Sands member contains no stratigraphically relevant fossils while in the Campine subsurface dinoflagellate cysts are common and show a stratigraphic range covering the entire Tortonian stage. K-Ar dates were determined for glauconite from 13 selected samples spread over both areas. A glauconite date corresponding to the earliest Tortonian indicates newly formed glauconite was incorporated into a greensand at the base of the Diest Formation in the central Campine area. All other dates point at reworked glauconite and can be organized in two groups, one reflecting a Burdigalian age and another reflecting a Langhian age. These data and the thickness and glauconite content of the Diest Formation imply massive reworking of older Miocene deposits. The paleogeographic implications of these data lead to the tentative recognition of two Tortonian sedimentary sequences. An older one corresponding to dinoflagellate biochron DN8 comprises the Deurne Member, part of the Dessel Member, the Hageland Diest member, the eastern Campine Diest member and some basal sands of the Diest Formation in the central Campine. A younger sequence corresponding to dinoilagellate biochrons DN9 and 10 was strongly influenced by the prograding proto-Rhine delta front in the Roer Valley Graben to the northeast. The subsiding Campine basin was filled from east to west during this second cycle