At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners.
Although this requires the specification of bespoke task-dependent models, encouraging empirical results are beginning to emerge.
Cross-lingual text representations have gained popularity lately and act as the backbone of many tasks such as unsupervised machine translation and cross-lingual information retrieval, to name a few.
Ranking tasks are usually based on the text of the main body of the page and the actions (clicks) of users on the page.
Our results are presented in a language-oriented (as opposed to model-oriented) fashion to emphasise the language-based goals of this work.
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia.
We also use our method for comparing image and text encoders trained using different modern approaches, thus addressing the issues hindering the development of novel methods for cross-modal recipe retrieval.
This paper describes HyperStream, a large-scale, flexible and robust software package, written in the Python language, for processing streaming data with workflow creation capabilities.
This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels.
It was recently shown that neural ordinary differential equation models cannot solve fundamental and seemingly straightforward tasks even with high-capacity vector field representations.
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags.
There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home.