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.
Weak supervision challenges black-box models, suggesting fusion of modeling cultures.
Self-supervised learning excels in topic modeling by being less model-specific.
We describe a novel weakly labeled Audio Event Classification approach based on a self-supervised attention model. The weakly labeled framework is used to eliminate the need for expensive data labeling procedure and self-supervised attention is deployed to help a model distinguish between relevant and irrelevant parts …
Generative models enhance weak supervision for better image classification.
In this study, importance of user inputs is studied in the context of personalizing human activity recognition models using incremental learning. Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method. Three different approaches to update models are compared:…
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…
Optimal and safe semi-supervised learning estimator for high-dimensional data.
Generative model explains self-supervised learning across various tasks.
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 (…
The scarcity of data annotated at the desired level of granularity is a recurring issue in many applications. Significant amounts of effort have been devoted to developing weakly supervised methods tailored to each individual setting, which are often carefully designed to take advantage of the particular properties of …
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 …
Study compares semi-supervised learning methods for anomaly detection in hydraulic systems.
Study shows semi-supervised learning can be more robust with fewer labeled examples.
We introduce a new model for building conditional generative models in a semi-supervised setting to conditionally generate data given attributes by adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model uses a pair of stacked discriminators to learn the marginal distribution of the data, and the co…
PLRM synthesizes labels from mismatched sources for better training sets.
A new method for training generative models with sparse supervision.
Combines foundation models with weak supervision to improve NLP and video tasks.
Proposes supervised method for whole DAG causal structure learning.
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based estimation. In this paper, we present a general framework of max-margin supervised topic models for both continuous and categorical response var…
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chos…
Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we…
Self-supervision provides effective representations for downstream tasks without requiring labels. However, existing approaches lag behind fully supervised training and are often not thought beneficial beyond obviating or reducing the need for annotations. We find that self-supervision can benefit robustness in a varie…
ProbKT uses probabilistic logical reasoning to train object detection models with weak supervision.
DoubleMatch combines pseudo-labeling with self-supervision for SSL.
A method for collecting human supervision that combines rules and instance labels.
In many machine learning scenarios, supervision by gold labels is not available and consequently neural models cannot be trained directly by maximum likelihood estimation (MLE). In a weak supervision scenario, metric-augmented objectives can be employed to assign feedback to model outputs, which can be used to extract …
A new method for semi-supervised learning of sparse features using elastic-net.
Paper resolves the debate on process vs. outcome supervision in reinforcement learning.
Faster weak supervision framework using triplet methods.
Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in…
As machine learning models continue to increase in complexity, collecting large hand-labeled training sets has become one of the biggest roadblocks in practice. Instead, weaker forms of supervision that provide noisier but cheaper labels are often used. However, these weak supervision sources have diverse and unknown a…
Study calculates Bayes risk for semi-supervised learning with uncertain labels.
Better use of unlabelled data improves Bayesian active learning models.
New framework combines semi-supervised data programming with subset selection for improved text classification.
New method estimates model performance bounds without ground truth labels.
This work improves adaptive conformal prediction using self-supervised learning.
Improved ASR for English-isiZulu code-switched speech with semi-supervised training.
Survey on self-supervised pre-training for neural networks using unlabeled data.
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work has managed to learn semi-supervised models in a non-restrictive regime. However, so far such appro…
Efficiently builds diverse sub-model ensembles for robust self-supervised learning.
Proposes a supervised VAE to reveal model invariances for interpretability.
Improved self-supervised denoising for Poisson-Gaussian noise.
Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chos…
Big models pretrain and fine-tune for semi-supervised learning on ImageNet.
ETM models improve efficiency in semi-supervised logistic regression.
AugmentedPCA improves PCA with supervised or adversarial objectives.
In this paper, we study statistical properties of semi-supervised learning, which is considered as an important problem in the community of machine learning. In the standard supervised learning, only the labeled data is observed. The classification and regression problems are formalized as the supervised learning. In s…
Subset selection improves weak supervision performance.