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 …
Matryoshka representation learning
Learned representations are a central component in modern ML systems, serving a
multitude of downstream tasks. When training such representations, it is often the case that …
multitude of downstream tasks. When training such representations, it is often the case that …
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 …
Cascadexml: Rethinking transformers for end-to-end multi-resolution training in extreme multi-label classification
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 …
Extreme zero-shot learning for extreme text classification
The eXtreme Multi-label text Classification (XMC) problem concerns finding most relevant
labels for an input text instance from a large label set. However, the XMC setup faces two …
labels for an input text instance from a large label set. However, the XMC setup faces two …
Knowledge-aided momentum contrastive learning for remote-sensing image text retrieval
Remote-sensing image–text retrieval (RSITR) has attracted widespread attention due to its
great potential for rapid information mining ability on remote-sensing images. Although …
great potential for rapid information mining ability on remote-sensing images. Although …
Pina: Leveraging side information in extreme multi-label classification via predicted instance neighborhood aggregation
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an
exceptionally large label space. Most of the existing XMC learners focus on the extraction of …
exceptionally large label space. Most of the existing XMC learners focus on the extraction of …
Eliminate Before Align: A Remote Sensing Image-Text Retrieval Framework with Keyword Explicit Reasoning
Mountains of researches center around the Remote Sensing Image-Text Retrieval (RSITR),
aiming at retrieving the corresponding targets based on the given query. Among them, the …
aiming at retrieving the corresponding targets based on the given query. Among them, the …