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Found 16 papers, 7 papers with code
Explainable Publication Year Prediction of Eighteenth Century Texts with the BERT Model
In this paper, we describe a BERT model trained on the Eighteenth Century Collections Online (ECCO) dataset of digitized documents.
Optical Character Recognition
Optical Character Recognition (OCR)
Consistent algorithms for multi-label classification with macro-at-$k$ metrics
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework.
Generalized test utilities for long-tail performance in extreme multi-label classification
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
Towards Memory-Efficient Training for Extremely Large Output Spaces -- Learning with 500k Labels on a Single Commodity GPU
In classification problems with large output spaces (up to millions of labels), the last layer can require an enormous amount of memory.
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
We thus propose CascadeXML, an end-to-end multi-resolution learning pipeline, which can harness the multi-layered architecture of a transformer model for attending to different label resolutions with separate feature representations.
Extreme Multi-Label Classification
Multi Label Text Classification
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On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC).
Adversarial Examples for Extreme Multilabel Text Classification
Extreme Multilabel Text Classification (XMTC) is a text classification problem in which, (i) the output space is extremely large, (ii) each data point may have multiple positive labels, and (iii) the data follows a strongly imbalanced distribution.
Propensity-scored Probabilistic Label Trees
Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels.
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization
We want to start in a region of weight space a) with low loss value, b) that is favourable for second-order optimization, and c) where the conjugate-gradient (CG) calculations can be performed quickly.
Unbiased Loss Functions for Multilabel Classification with Missing Labels
This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate.
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification
Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction of related searches and product recommendation tasks.
Unbiased Loss Functions for Extreme Classification With Missing Labels
In addition to the computational burden arising from large number of training instances, features and labels, problems in XMC are faced with two statistical challenges, (i) large number of 'tail-labels' -- those which occur very infrequently, and (ii) missing labels as it is virtually impossible to manually assign every relevant label to an instance.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
Adversarial Extreme Multi-label Classification
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
On Flat versus Hierarchical Classification in Large-Scale Taxonomies
We study in this paper flat and hierarchical classification strategies in the context of large-scale taxonomies.
