In practical situations, it is natural to expect the input-output pairs of a machine learning model to satisfy some requirements.

This data-reliance may lead to low generalization of the learned alignment relationships. Inthis work, we propose theEye-gazeGuided Multi-modalAlignment (EGMA) frameworktoharness eye-gazedataforbetter alignment of medical visual and textual features.

The streaming model is applicable in many algorithmic and ML related tasks where the size ofthe data farexceeds the available storage.

Wevalidate ourmethod, RobustContrastiveLearning(RoCL),onmultiplebenchmarkdatasets, on which itobtains comparable robust accuracyover state-of-the-art supervised adversarial learning methods, and significantly improved robustness against the black boxand unseen types of attacks.

A.1 Motivation For what purpose was the dataset created? As an affiliated dataset, we created MIMIC-CXR-VQA to provide a benchmark for medical visual question answering systems. Who created the dataset (e.g., which team, research group) and on behalf of which Who funded the creation of the dataset? This work was (partially) supported by Microsoft Research Asia, Institute of Information & Communications Technology Planning & Evaluation (IITP) grant (No.2019-0-00075, RS-2022-00155958), National Research Foundation of Korea (NRF) grant (NRF-2020H1D3A2A03100945), and the Korea Health Industry Development Institute (KHIDI) What do the instances that comprise the dataset represent (e.g., documents, photos, EHRXQA contains natural questions and corresponding SQL/NeuralSQL queries (text). How many instances are there in total (of each type, if appropriate)? In EHRXQA, there are about 46.2K instances (16,366 image-related samples, 16,529 table-related samples, and 13,257 image+table-related samples).

A very useful method for devising online posted prices is viavanishing-regret online learning algorithms inanadversarial environment [12,11,23,9,10,28].

Then, wederivealocalMDI importance measure of variable relevance, which has a very natural connection withtheglobal MDImeasure andcanberelated toanewnotion oflocalfeature relevance. We further link local MDI importances with Shapley values and discuss them in the light of related measures from the literature.

Open Catalyst 2020 (OC20) Datasetand Community Challenges // ACSCatalysis.