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 …
Pecos: Prediction for enormous and correlated output spaces
Many large-scale applications amount to finding relevant results from an enormous output
space of potential candidates. For example, finding the best matching product from a large …
space of potential candidates. For example, finding the best matching product from a large …
Constructing tree-based index for efficient and effective dense retrieval
Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve
the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the …
the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the …
On tuning parameters guiding similarity computations in a data deduplication pipeline for customers records: Experience from a R&D project
Data stored in information systems are often erroneous. Duplicate data are one of the typical
error type. To discover and handle duplicates, the so-called deduplication methods are …
error type. To discover and handle duplicates, the so-called deduplication methods are …
A survey on extreme multi-label learning
Multi-label learning has attracted significant attention from both academic and industry field
in recent decades. Although existing multi-label learning algorithms achieved good …
in recent decades. Although existing multi-label learning algorithms achieved good …
Elias: End-to-end learning to index and search in large output spaces
Extreme multi-label classification (XMC) is a popular framework for solving many real-world
problems that require accurate prediction from a very large number of potential output …
problems that require accurate prediction from a very large number of potential output …
Zero-Shot Learning Over Large Output Spaces: Utilizing Indirect Knowledge Extraction from Large Language Models
Extreme Multi-label Learning (XMC) is a task that allocates the most relevant labels for an
instance from a predefined label set. Extreme Zero-shot XMC (EZ-XMC) is a special setting …
instance from a predefined label set. Extreme Zero-shot XMC (EZ-XMC) is a special setting …
MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
The eXtreme Multi-label text Classification (XMC) refers to training a classifier that assigns a
text sample with relevant labels from an extremely large-scale label set (eg, millions of …
text sample with relevant labels from an extremely large-scale label set (eg, millions of …
Uncertainty in extreme multi-label classification
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable
machine learning models for decision making. However, most research in this domain has …
machine learning models for decision making. However, most research in this domain has …
[PDF][PDF] End-to-end learning to index and search in large output spaces
Extreme multi-label classification (XMC) is a popular framework for solving many real-world
problems that require accurate prediction from a very large number of potential output …
problems that require accurate prediction from a very large number of potential output …