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Found 16 papers, 2 papers with code
Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood
This different view allows for wider applicability of the tests by offering users access to a richer model class.
Inherently Interpretable Time Series Classification via Multiple Instance Learning
Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes.
Low-count Time Series Anomaly Detection
Low-count time series describe sparse or intermittent events, which are prevalent in large-scale online platforms that capture and monitor diverse data types.
Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models
At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners.
Evaluation of Field-Aware Neural Ranking Models for Recipe Search
Although this requires the specification of bespoke task-dependent models, encouraging empirical results are beginning to emerge.
Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora
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.
Hypothesis Testing for Class-Conditional Label Noise
Differently, in this paper we assume we have access to a set of anchor points whose true posterior is approximately 1/2.
Non-Linear Multiple Field Interactions Neural Document Ranking
Ranking tasks are usually based on the text of the main body of the page and the actions (clicks) of users on the page.
Towards Multi-Language Recipe Personalisation and Recommendation
Our results are presented in a language-oriented (as opposed to model-oriented) fashion to emphasise the language-based goals of this work.
Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia.
Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA
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.
HyperStream: a Workflow Engine for Streaming Data
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.
Ordinal Regression as Structured Classification
This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels.
Neural ODEs with stochastic vector field mixtures
It was recently shown that neural ordinary differential equation models cannot solve fundamental and seemingly straightforward tasks even with high-capacity vector field representations.
Label Propagation for Learning with Label Proportions
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.
Probabilistic Sensor Fusion for Ambient Assisted Living
There is a widely-accepted need to revise current forms of health-care provision, with particular interest in sensing systems in the home.
