Search Results for author:
Found 3 papers, 1 papers with code
Neural network gradient-based learning of black-box function interfaces
We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions.
Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application.
Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks
The analysis sheds light on the relative strengths of different sentence embedding methods with respect to these low level prediction tasks, and on the effect of the encoded vector's dimensionality on the resulting representations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
