Traditionally, there are three species of classification: unsupervised, supervised, and semi-supervised. Supervised and semi-supervised classification differ by whether or not weight is given to unlabelled observations in the classification procedure. In unsupervised classification, or clustering, all observations are …
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,657 papers · 148 categories
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48 results for “fractionally supervised classification”
Develops FSC for maxima nominated samples, improving classification in rare-event data.
problem Combining labeled and unlabeled data in rare-event scenarios.
method Introduces a latent representation to account for maxima nomination sampling.
result Improves classification performance in rare-event contamination mixtures.
SidAE combines autoencoders and Siamese networks for self-supervised feature extraction.
problem Efficiently extract meaningful features from unlabeled data.
method Proposes SidAE, a combination of autoencoders and Siamese networks for self-supervised image classification.
result SidAE outperforms self-supervised baselines across multiple datasets and scenarios, especially with limited labeled data.
Proposes a probabilistic approach to semi-supervised learning using normalizing flows.
problem Leveraging unlabelled data for semi-supervised learning with limited labelled data.
method Uses a normalizing flow to learn the posterior distribution over predictions for labelled data, serving as a prior for unlabelled data.
result Demonstrates improved performance on various tasks with varying output complexity.
SSNAS finds neural architectures without labeled data.
problem Limited labeled data for NAS.
method Self-supervised learning for NAS.
result Comparable results to supervised NAS with labeled data.
Proposes a method to classify binary data from multiple unlabeled datasets.
problem High annotation costs in training classifiers from weakly supervised data.
method Introduces surrogate set classification (SSC) to predict data origin from multiple unlabeled datasets, then uses this to train a binary classifier.
result Demonstrates superior performance compared to existing methods.
We describe a graph-based semi-supervised learning framework in the context of deep neural networks that uses a graph-based entropic regularizer to favor smooth solutions over a graph induced by the data. The main contribution of this work is a computationally efficient, stochastic graph-regularization technique that u…
The paper establishes risk bounds for PU learning with label noise.
problem Finding a classifier in PU learning with label noise.
method Establishes risk bounds under the assumption of label selection randomness.
result Proves that the upper bound on minimax risk is almost optimal.
Study how noisy labels affect semi-supervised learning.
problem Effect of noisy labels on semi-supervised learning performance.
method Proposed an algorithm derived from a continuous relaxation of the Maximum A Posteriori (MAP) estimator for a Degree Corrected Stochastic Block Model (DC-SBM).
result Our approach achieves promising performance even with very noisy labeled data.
Complex classification performance metrics such as the F-measure and Jaccard index are often used, in order to handle class-imbalanced cases such as information retrieval and image segmentation. These performance metrics are not decomposable, that is, they cannot be expressed in a per-example manner, which hinder…
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphsstat.ML
We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or distributed setting. We utilize a technique, first described in [13] for the construction of mini-batches for stochastic gradient descent (SGD…
Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previous algorithms based on consistency regularization can harness the abundance of unlabeled data to pr…
Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage. This paper studies the problem of classifying frames of footage according to the activity of the camera-wearer with an emphasis on application to real-world police body-wo…
Paper analyzes and improves graph convolutional networks for node classification.
problem Over-smoothing in GCNs causes poor performance in node classification tasks.
method Interpreted GCNs from an optimization perspective, introduced metrics to measure over-smoothing, derived a new kernel GCN+.
result GCN+ reduces over-smoothing and improves node classification performance.
Binary classification improves with a small fraction of corrupted labels.
problem Binary classification with corrupted labels.
method Established corruption as a form of regularization and computed upper bounds on estimation error.
result Corruption is beneficial only up to a small fraction of the total sample, scaling with the square root of the sample size.
Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists w…
Study confirms fractional norms and quasinorms do not help overcome curse of dimensionality.
problem Overcoming the curse of dimensionality in machine learning.
method Systematic testing of fractional norms and quasinorms (p<1) on classification problems.
result Distance concentration behavior is qualitatively the same for all norms and quasinorms as dimensionality increases.
Paper proposes an active learning method to improve remote sensing object detection with less labeled data.
problem High labor and time costs in annotating remote sensing images for CNN object detectors.
method Uncertainty-based active learning that selects images with more information for annotation.
result Detector achieves high performance with a fraction of the training images.
Improved anomaly detection for launch vehicle propulsion systems using LSTM and statistical relabeling.
problem Detecting anomalies in real-time telemetry data for launch vehicles.
method Utilized LSTM networks for anomaly classification, introduced a statistical detector based on Mahalanobis distance and forward-backward detection fractions to adjust training labels.
result Precision and recall of the LSTM classifier improved by 7% and 22% respectively after statistical relabeling.
Enhanced financial forecasting using supervised autoencoders with noise augmentation and triple labeling.
problem Improving investment strategy performance on noisy financial data.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced noise augmentation and bottleneck size significantly boost strategy effectiveness.
GraphXCOVID uses deep semi-supervised learning to identify COVID-19 from chest X-rays with minimal labels.
problem Identifying COVID-19 on chest X-rays with limited labelled data.
method Graph-based deep semi-supervised framework with pseudo-labeling and attention maps.
result Outperforms current supervised models with a tiny fraction of labelled examples.
S4 learns new self-supervision automatically, improving accuracy with less human effort.
problem Lack of direct supervision in machine learning.
method Combines deep learning and probabilistic logic to automatically generate and verify new self-supervision.
result S4 can automatically propose accurate self-supervision, matching supervised methods with less human effort.
We present a new flavor of Variational Autoencoder (VAE) that interpolates seamlessly between unsupervised, semi-supervised and fully supervised learning domains. We show that unlabeled datapoints not only boost unsupervised tasks, but also the classification performance. Vice versa, every label not only improves class…
A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health status of a patient. These sequences of clinical measurements are naturally represented as time series, characterized by multiple variables a…
Improved self-supervised learning for document images.
problem Performance of self-supervised pre-training on document images is poor.
method Proposed context-aware alternatives and a novel multi-modal method.
result Novel method outperforms other self-supervised methods on document image classification.
This paper gives two new combinatorial topological proofs of the classification of rational tangles. Each proof rests on an elegant lemma showing that rational tangles are isotopic to canonical alternating rational tangles. The first proof defines the tangle fraction from the canonical form and uses flyping to prove in…
ModSSC unifies semi-supervised classification for various data types.
problem Fragmented support for semi-supervised classification across different methods, settings, and data types.
method ModSSC is a modular Python framework that supports reproducible and controlled experimentation for semi-supervised classification on heterogeneous data.
result ModSSC enables systematic comparison of semi-supervised learning across various datasets and model backbones.
Semi-supervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Trainingcs.LG
Magnetic Resonance Imaging (MRI) of the brain can come in the form of different modalities such as T1-weighted and Fluid Attenuated Inversion Recovery (FLAIR) which has been used to investigate a wide range of neurological disorders. Current state-of-the-art models for brain tissue segmentation and disease classificati…
This work tackles continual learning with semi-supervised data, showing that even with minimal labeled data, performance can match full-supervised methods.
problem Training deep networks on a stream of tasks without forgetting, especially when labeled data is scarce.
method Designing a novel CSSL method that leverages metric learning and consistency regularization to learn from both labeled and unlabeled data.
result Our method outperforms state-of-the-art methods trained with full supervision, achieving comparable performance with only 25% labeled data.
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using on…
New framework for fair classification in adversarial settings with provable guarantees.
problem Fairness in classification with adversarial perturbations of protected attributes.
method Optimization framework for learning fair classifiers with provable guarantees.
result Near-tightness of accuracy and fairness guarantees for multiple protected attributes and various hypothesis classes.
We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral Image Classification in a semi-supervised setting. The Ladder Network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and unsupervised cost. In many settings this has proven to be more successful than o…
Covariance and histogram image descriptors provide an effective way to capture information about images. Both excel when used in combination with special purpose distance metrics. For covariance descriptors these metrics measure the distance along the non-Euclidean Riemannian manifold of symmetric positive definite mat…
Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Althou…
Directly compute classification by learning features with class scores.
problem Classification efficiency and accuracy on various datasets.
method PCA for feature encoding, supervised learning model with encoder-decoder structure.
result Effective classification performance on multiple datasets.
Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classificationcs.LG
Hierarchical text classification has many real-world applications. However, labeling a large number of documents is costly. In practice, we can use semi-supervised learning or weakly supervised learning (e.g., dataless classification) to reduce the labeling cost. In this paper, we propose a path cost-sensitive learning…
In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints. In contrast to existing approaches, we decompose SSC into two simpler classification tasks/stages: the first stage uses a pair of Siamese neural networks to label the unlabel…
Paper develops MRCs for supervised classification using generalized maximum entropy.
problem Developing robust classifiers for decision problems.
method Generalized maximum entropy principle applied to minimax risk classifiers.
result Learning techniques for determining MRCs with performance guarantees.
GNNs improve semi-supervised node regression, but why? We explain.
problem Understanding when and why GNNs succeed in semi-supervised node regression.
method Aggregate-and-readout model encompassing message passing architectures, least-squares estimation over GNNs with linear graph convolutions and a deep ReLU readout.
result Sharp non-asymptotic risk bound separating approximation, stochastic, and optimization errors.
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Using a projection of the supervised estimate onto a set of constraints imposed by the u…
This paper gives new and elementary combinatorial topological proofs of the classification of unoriented and oriented rational knots and links. These proofs are based on the known classification of alternating knots through flyping, and the calculus of continued fractions. We characterize the class of strongly invertib…
Weakly Supervised-Based Oversampling for High Imbalance and High Dimensionality Data Classificationcs.LG
This paper tackles imbalanced classification with weakly supervised oversampling.
problem Imbalanced classification in high-dimensional datasets.
method Weakly supervised SMOTE, cost-sensitive NCA, bootstrap ensemble.
result Improved classification performance on synthetic and real-world datasets.
We combine supervised learning with unsupervised learning in deep neural networks. The proposed model is trained to simultaneously minimize the sum of supervised and unsupervised cost functions by backpropagation, avoiding the need for layer-wise pre-training. Our work builds on the Ladder network proposed by Valpola (…
There has been an increasing interest in semi-supervised learning in the recent years because of the great number of datasets with a large number of unlabeled data but only a few labeled samples. Semi-supervised learning algorithms can work with both types of data, combining them to obtain better performance for both c…
LC-GNN improves GNNs for node classification by incorporating label consistency.
problem Limited performance of GNNs due to label consistency assumption not always holding.
method LC-GNN uses node pairs with the same label but unconnected to expand GNN's receptive field.
result LC-GNN outperforms traditional GNNs in semi-supervised node classification.
DM improves self-supervised transfer learning by matching target distributions.
problem Improving self-supervised transfer learning performance.
method Distribution Matching (DM) method that drives representation distribution towards a predefined reference distribution.
result DM outperforms existing methods on target classification tasks.
The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects. Classifiers are the tools that implement the actual functional mapping from these measurements---also called features or …
Proposes a graph learning framework for clustering and semi-supervised classification.
problem Graph-based clustering and semi-supervised classification techniques have shown impressive performance but lack explicit cluster structure.
method Uses self-expressiveness of samples for global structure and adaptive neighbor approach for local structure. Incorporates rank constraint to ensure optimal performance.
result The proposed method achieves better performance than state-of-the-art methods in clustering and semi-supervised classification.