Abstract: Large Language Models (LLMs) have become effective zero-shot classifiers, but their high computational requirements and environmental costs limit their practicality for large-scale ...

In a typical multi-label setting, a picture contains on average few positive labels, and many negative ones. This positive-negative imbalance dominates the optimization process, and can lead to ...

Native Python implementation. A native Python implementation for a variety of multi-label classification algorithms. To see the list of all supported classifiers, check this link. Interface to Meka. A ...

We describe OHBA Software Library for the analysis of electrophysiology data (osl-ephys). This toolbox builds on top of the widely used MNE-Python package and provides unique analysis tools for ...

The ECO-SAM utilizes a pre-trained BERT encoder to obtain semantic embedding of input texts and then leverages a self-attention mechanism to model the semantic correlation between emotions.

Johnson & Johnson JNJ announced that the FDA granted label expansion to its multiple myeloma (MM) therapy Carvykti (ciltacabtagene autoleucel) for earlier lines of treatment. The therapy has been ...

Abstract: In this study, we present a hierarchical multi-modal multi-label attribute classification model for anime illustrations using graph convolutional networks (GCNs). The focus of this study is ...

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater ...