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Found 23 papers, 13 papers with code
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems.
Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data
To investigate this question we use population-based optimisation algorithms to generate artificial surrogate training data for naive Bayes for regression.
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks with respect to their inputs.
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes
Effective regularisation of neural networks is essential to combat overfitting due to the large number of parameters involved.
XGBoost: Scalable GPU Accelerated Learning
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github. com/dmlc/xgboost).
Probability Calibration Trees
Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications.
On the Calibration of Nested Dichotomies for Large Multiclass Tasks
Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems.
Ensembles of Nested Dichotomies with Multiple Subset Evaluation
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems.
Stochastic Gradient Trees
We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision.
Classifier Chains: A Review and Perspectives
This performance led to further studies of how exactly it works, and how it could be improved, and in the recent decade numerous studies have explored classifier chains mechanisms on a theoretical level, and many improvements have been made to the training and inference procedures, such that this method remains among the state-of-the-art options for multi-label learning.
Embedding Java Classes with code2vec: Improvements from Variable Obfuscation
code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors.
DFUC2020: Analysis Towards Diabetic Foot Ulcer Detection
Every 20 seconds, a limb is amputated somewhere in the world due to diabetes.
Adaptive XGBoost for Evolving Data Streams
The proposed method creates new members of the ensemble from mini-batches of data as new data becomes available.
Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation
DFUC2020 provided participants with a comprehensive dataset consisting of 2, 000 images for training and 2, 000 images for testing.
GPUTreeShap: Massively Parallel Exact Calculation of SHAP Scores for Tree Ensembles
SHAP (SHapley Additive exPlanation) values provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values.
Sampling Permutations for Shapley Value Estimation
Game-theoretic attribution techniques based on Shapley values are used to interpret black-box machine learning models, but their exact calculation is generally NP-hard, requiring approximation methods for non-trivial models.
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training.
Better Self-training for Image Classification through Self-supervision
Self-training is a simple semi-supervised learning approach: Unlabelled examples that attract high-confidence predictions are labelled with their predictions and added to the training set, with this process being repeated multiple times.
Semi-Supervised Learning using Siamese Networks
This work explores a new training method for semi-supervised learning that is based on similarity function learning using a Siamese network to obtain a suitable embedding.
Closed-loop Control for Online Continual Learning
To address the non-stationarity in the continual learning environment, we employ a Q function with task-specific and task-shared components to support fast adaptation.
Hitting the Target: Stopping Active Learning at the Cost-Based Optimum
This work enables practitioners to employ active learning by providing actionable recommendations for which stopping criteria are best for a given real-world scenario.
Feature Extractor Stacking for Cross-domain Few-shot Learning
Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor.
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Despite its strong empirical performance, rehearsal methods still suffer from a poor approximation of the loss landscape of past data with memory samples.
