Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. This is opposed to the traditional task of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.
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In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Ranked #1 on Fine-Grained Image Classification on Stanford Cars (using extra training data)
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.
IMAGE CAPTIONING IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-LABEL CLASSIFICATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING ONE-SHOT LEARNING SENTIMENT ANALYSIS SPEAKER VERIFICATION TEXT CLASSIFICATION TIME SERIES FORECASTING VISUAL QUESTION ANSWERING
Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
Ranked #3 on Malware Detection on Android Malware Dataset
Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently.
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
It provides native Python implementations of popular multi-label classification methods alongside a novel framework for label space partitioning and division.
The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.
These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks.
Ranked #1 on Multi-Task Learning on CelebA