Search Results for author:
Found 10 papers, 4 papers with code
Benchmarking Meaning Representations in Neural Semantic Parsing
Thus, the impact of meaning representation on semantic parsing is less understood.
Inferring latent neural sources via deep transcoding of simultaneously acquired EEG and fMRI
Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution.
TestAug: A Framework for Augmenting Capability-based NLP Tests
Our experiments show that TestAug has three advantages over the existing work on behavioral testing: (1) TestAug can find more bugs than existing work; (2) The test cases in TestAug are more diverse; and (3) TestAug largely saves the manual efforts in creating the test suites.
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models
In this work, for the first time, we propose to investigate this problem where only a small number of labeled training samples are available.
An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models
We find that using the same time budget, HPO often fails to outperform grid search due to two reasons: insufficient time budget and overfitting.
Latent neural source recovery via transcoding of simultaneous EEG-fMRI
Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution for inferring a latent source space of neural activity.
Deep Neural Networks with Koopman Operators for Modeling and Control of Autonomous Vehicles
Furthermore, a data-driven model predictive controller with the learned Koopman model is designed for path tracking control of autonomous vehicles.
Unsupervised Sparse-view Backprojection via Convolutional and Spatial Transformer Networks
Many imaging technologies rely on tomographic reconstruction, which requires solving a multidimensional inverse problem given a finite number of projections.
Substituting Gadolinium in Brain MRI Using DeepContrast
Together, these studies validate our hypothesis that a deep learning approach can potentially replace the need for GBCAs in brain MRI.
Scalable and Robust Construction of Topical Hierarchies
Automated generation of high-quality topical hierarchies for a text collection is a dream problem in knowledge engineering with many valuable applications.
