This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods.

This paper tackles the modelling of large, complex and multivariate time series panels inaprobabilistic setting.

Probabilistic Circuits (PCs) are a promising avenue for probabilistic modeling.

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).