Search Results for author:
Found 14 papers, 8 papers with code
Addressing distributional shifts in operations management: The case of order fulfillment in customized production
We empirically validate our proposed approach using real-world data from a job shop production that supplies large metal components to an oil platform construction yard.
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
Unlike existing approaches, we generate pseudo labels and propose to train the model via a novel attention-based contrastive adaptation method.
Contrastive Domain Adaptation for Question Answering using Limited Text Corpora
Here, we train a QA system on both source data and generated data from the target domain with a contrastive adaptation loss that is incorporated in the training objective.
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents
As this approach can incorporate any active learning agent into its ensemble, it allows to increase the performance of every active learning agent by learning how to combine it with others.
IntKB: A Verifiable Interactive Framework for Knowledge Base Completion
(ii) Our system is designed such that it continuously learns during the KB completion task and, therefore, significantly improves its performance upon initial zero- and few-shot relations over time.
Learning a Cost-Effective Annotation Policy for Question Answering
State-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive.
Practical Annotation Strategies for Question Answering Datasets
Here, remarkably, annotating a stratified subset with only 1. 2% of the original training set achieves 97. 7% of the performance as if the complete dataset was annotated.
RankQA: Neural Question Answering with Answer Re-Ranking
The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer.
Sample Complexity Bounds for Recurrent Neural Networks with Application to Combinatorial Graph Problems
As our primary contribution, this is the first work that upper bounds the sample complexity for learning real-valued RNNs.
Adaptive Document Retrieval for Deep Question Answering
State-of-the-art systems in deep question answering proceed as follows: (1) an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer.
Putting Question-Answering Systems into Practice: Transfer Learning for Efficient Domain Customization
Traditional information retrieval (such as that offered by web search engines) impedes users with information overload from extensive result pages and the need to manually locate the desired information therein.
Deep learning for affective computing: text-based emotion recognition in decision support
Emotions widely affect human decision-making.
Face Translation between Images and Videos using Identity-aware CycleGAN
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement.
Improving Video Generation for Multi-functional Applications
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
