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arXiv research

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.

169,051 papers · 148 categories

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8 results for “Meta-Classification”

Meta-Meta Classification for One-Shot Learningcs.LG

A new meta-meta classification method tackles few-shot learning tasks.

problem Learning with limited data in small-data settings.

method Designing an ensemble of learners for a large set of problems, then learning how to combine them for a new problem.

result Meta-meta classification outperforms traditional meta-learning and ensembling approaches in one-shot learning tasks.

meta-learning few-shot learning ensemble meta-meta classification one-shot learning

2020-04-17abs ↗pdf ↗

Classification Uncertainty of Deep Neural Networks Based on Gradient Informationcs.LG

We study the quantification of uncertainty of Convolutional Neural Networks (CNNs) based on gradient metrics. Unlike the classical softmax entropy, such metrics gather information from all layers of the CNN. We show for the EMNIST digits data set that for several such metrics we achieve the same meta classification acc…

2018-05-22abs ↗pdf ↗

Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industrycs.LG

Proposes a deep learning churn prediction system for telecom using TL and meta-classification.

problem Churn prediction challenges in telecom due to large data, high dimensions, and imbalanced data.

method Transfer Learning (TL) and Ensemble-based Meta-Classification. Two stages: TL on Deep CNNs, then GP-AdaBoost meta-classifier.

result TL-DeepE system achieved 75.4% and 68.2% prediction accuracy on Orange and Cell2cell datasets, respectively.

Churn Prediction Telecom Industry Transfer Learning Deep Convolution Neural Networks Meta-Classification

2019-01-18abs ↗pdf ↗

Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Imagescs.CV

Method generates uncertainty measures for street scene segmentation.

problem Reliability and uncertainty measures in semantic segmentation of street scenes.

method Nested crops, neural network segmentation, post-processing, uncertainty heat maps.

result Significant improvements in classification and regression performance.

semantic segmentation street scenes nested crops uncertainty measures DeepLabv3+

2019-04-09abs ↗pdf ↗

Multi-class Classification without Multi-class Labelscs.LG

No multi-class labels needed for multi-class classification.

problem Multi-class classification without requiring class-specific labels.

method Meta classification learning using pairwise similarity prediction.

result The method learns a multi-class classifier from binary classifier for pairwise similarity.

multi-class classification pairwise similarity unsupervised learning binary classifier neural networks

2019-01-02abs ↗pdf ↗

Forward Amortized Inference for Likelihood-Free Variational Marginalizationstat.ML

In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient can be sampled without bias and without requiring any evaluation of eith…

2018-05-29abs ↗pdf ↗

Multi-Class Classification from Noisy-Similarity-Labeled Datacs.LG

Paper tackles noisy similarity labels for multi-class classification.

problem Learning multi-class classifiers from noisy similarity-labeled data.

method Proposes a method using a noise transition matrix to learn from noisy data.

result Demonstrates superior performance compared to state-of-the-art methods.

multi-class classification noisy labels similarity labeling deep learning noise transition matrix

2020-02-16abs ↗pdf ↗

Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilitiescs.CV

We present a method that "meta" classifies whether seg-ments predicted by a semantic segmentation neural networkintersect with the ground truth. For this purpose, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input sce…

2018-11-01abs ↗pdf ↗