Search Results for author:
Found 13 papers, 8 papers with code
TACTiS: Transformer-Attentional Copulas for Time Series
The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance.
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class.
byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings
This article introduces byteSteady -- a fast model for classification using byte-level n-gram embeddings.
In Search of Robust Measures of Generalization
A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
Differentiable Causal Discovery from Interventional Data
This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16\% points.
Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning
Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.
Maximum Margin Interval Trees
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine.
Large scale modeling of antimicrobial resistance with interpretable classifiers
Antimicrobial resistance is an important public health concern that has implications in the practice of medicine worldwide.
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
The Set Covering Machine (SCM) is a greedy learning algorithm that produces sparse classifiers.
Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine
The increased affordability of whole genome sequencing has motivated its use for phenotypic studies.
