20 Новостных Групп

Advances in transfer learning have let go the limitations of traditional supervised machine learning algorithms for being de- pendent on annotated training data for training new models for every new …...

AbstractMachine Learning has been the quintessential solution for many AI problems, but learning models are heavily dependent on specific training data. Some learning models can be incorporated with prior knowledge …...

AbstractThis study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic …...

AbstractThere exist few text-specific methods for unsupervised anomaly detection, and for those that do exist, none utilize pre-trained models for distributed vector representations of words. In this paper we introduce …...

AbstractLeveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods. Recent deep methods for anomalies in images learn …...

Meta-learning has emerged as a trending tech- nique to tackle few-shot text classification and achieved state-of-the-art performance. How- ever, existing solutions heavily rely on the ex- ploitation of lexical features …...

AbstractTopic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or …...