Search Results for author:
Found 9 papers, 1 papers with code
Generalized Multiple Intent Conditioned Slot Filling
Natural language understanding includes the tasks of intent detection (identifying a user's objectives) and slot filling (extracting the entities relevant to those objectives).
NURBS-Diff: A Differentiable Programming Module for NURBS
These derivatives are used to define an approximate Jacobian used for performing the "backward" evaluation to train the deep learning models.
Locally-Contextual Nonlinear CRFs for Sequence Labeling
Linear chain conditional random fields (CRFs) combined with contextual word embeddings have achieved state of the art performance on sequence labeling tasks.
Efficiently labelling sequences using semi-supervised active learning
However, active learning methods usually use supervised training and ignore the data points which have not yet been labelled.
Learning Informative Representations of Biomedical Relations with Latent Variable Models
In both cases, recent methods have achieved strong results by learning a point estimate to represent the relation; this is then used as the input to a relation classifier.
MPP: Model Performance Predictor
We argue that an ensemble of such metrics can be used to create a score representing the prediction quality in production.
ML Health: Fitness Tracking for Production Models
Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels.
Generative Neural Machine Translation
We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences.
Generating Sentences Using a Dynamic Canvas
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language.
