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Found 13 papers, 4 papers with code
What is Hiding in Medicine's Dark Matter? Learning with Missing Data in Medical Practices
Electronic patient records (EPRs) produce a wealth of data but contain significant missing information.
Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
A key technical focus of the work is in providing performance guarantees for these new AI correctors through bounds on the probabilities of incorrect decisions.
An Informational Space Based Semantic Analysis for Scientific Texts
This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts.
Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation
Recent work by Mellor et al (2021) showed that there may exist correlations between the accuracies of trained networks and the values of some easily computable measures defined on randomly initialised networks which may enable to search tens of thousands of neural architectures without training.
Scikit-dimension: a Python package for intrinsic dimension estimation
Dealing with uncertainty in applications of machine learning to real-life data critically depends on the knowledge of intrinsic dimensionality (ID).
Learning from scarce information: using synthetic data to classify Roman fine ware pottery
To address the challenge we propose to use a transfer learning approach whereby the model is first trained on a synthetic dataset replicating features of the original objects.
High-dimensional separability for one- and few-shot learning
New stochastic separation theorems for data with fine-grained structure are formulated and proved.
Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data
Large observational clinical datasets become increasingly available for mining associations between various disease traits and administered therapy.
Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI
This principle is expected to work both for artificial NN and for selection and modification of important synaptic contacts in brain.
Explainable Artificial Intelligence (XAI)
Face Recognition
+1
Fractional norms and quasinorms do not help to overcome the curse of dimensionality
The curse of dimensionality causes the well-known and widely discussed problems for machine learning methods.
Informational Space of Meaning for Scientific Texts
The LSC is a scientific corpus of 1, 673, 350 abstracts and the LScDC is a scientific dictionary which words are extracted from the LSC.
LScDC-new large scientific dictionary
In this paper, we present a scientific corpus of abstracts of academic papers in English -- Leicester Scientific Corpus (LSC).
Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph
Large datasets represented by multidimensional data point clouds often possess non-trivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples.
