Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects
Within the vast expanse of computerized language processing, a revolutionary entity known
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
as Large Language Models (LLMs) has emerged, wielding immense power in its capacity to …
Fast multi-resolution transformer fine-tuning for extreme multi-label text classification
Extreme multi-label text classification~(XMC) seeks to find relevant labels from an extreme
large label collection for a given text input. Many real-world applications can be formulated …
large label collection for a given text input. Many real-world applications can be formulated …
Node feature extraction by self-supervised multi-scale neighborhood prediction
Learning on graphs has attracted significant attention in the learning community due to
numerous real-world applications. In particular, graph neural networks (GNNs), which take …
numerous real-world applications. In particular, graph neural networks (GNNs), which take …
Multi-label feature selection via robust flexible sparse regularization
Multi-label feature selection is an efficient technique to deal with the high dimensional multi-
label data by selecting the optimal feature subset. Existing researches have demonstrated …
label data by selecting the optimal feature subset. Existing researches have demonstrated …
Deepxml: A deep extreme multi-label learning framework applied to short text documents
Scalability and accuracy are well recognized challenges in deep extreme multi-label
learning where the objective is to train architectures for automatically annotating a data point …
learning where the objective is to train architectures for automatically annotating a data point …
Multi-label legal document classification: A deep learning-based approach with label-attention and domain-specific pre-training
D Song, A Vold, K Madan, F Schilder - Information Systems, 2022 - Elsevier
Multi-label document classification has a broad range of applicability to various practical
problems, such as news article topic tagging, sentiment analysis, medical code …
problems, such as news article topic tagging, sentiment analysis, medical code …
Siamesexml: Siamese networks meet extreme classifiers with 100m labels
Deep extreme multi-label learning (XML) requires training deep architectures that can tag a
data point with its most relevant subset of labels from an extremely large label set. XML …
data point with its most relevant subset of labels from an extremely large label set. XML …
Cascadexml: Rethinking transformers for end-to-end multi-resolution training in extreme multi-label classification
S Kharbanda, A Banerjee… - Advances in neural …, 2022 - proceedings.neurips.cc
Abstract Extreme Multi-label Text Classification (XMC) involves learning a classifier that can
assign an input with a subset of most relevant labels from millions of label choices. Recent …
assign an input with a subset of most relevant labels from millions of label choices. Recent …
The effect of metadata on scientific literature tagging: A cross-field cross-model study
Due to the exponential growth of scientific publications on the Web, there is a pressing need
to tag each paper with fine-grained topics so that researchers can track their interested fields …
to tag each paper with fine-grained topics so that researchers can track their interested fields …
Ngame: Negative mining-aware mini-batching for extreme classification
Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels
from an extremely large label set. Performing deep XC with dense, learnt representations for …
from an extremely large label set. Performing deep XC with dense, learnt representations for …