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Open-Ended Question Answering

Open-ended questions are defined as those that simply pose the question, without imposing any constraints on the format of the response. This distinguishes them from questions with a predetermined answer format.

Papers

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Showing 651–675 of 796 papers

TitleStatusHype
The Impact of Automatic Pre-annotation in Clinical Note Data Element Extraction - the CLEAN Tool—0
Efficient human-like semantic representations via the Information Bottleneck principle—0
On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization—0
Hierarchical Clustering better than Average-Linkage—0
Finite State Reasoning for Presupposition Satisfaction—0
An Empirical Study on Fine-Grained Named Entity Recognition—0
Towards Good Practices on Building Effective CNN Baseline Model for Person Re-identificationCode0
Optimal Bounds on the VC-dimension—0
Optimal occlusion uniformly partitions red blood cells fluxes within a microvascular network—0
Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization—0
Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks—0
End-to-End Learning on 3D Protein Structure for Interface PredictionCode0
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory—0
Quantity beats quality for semantic segmentation of corrosion in images—0
Adversarial and Perceptual Refinement for Compressed Sensing MRI ReconstructionCode0
On the Complexity of Detecting Convexity over a Box—0
Structured Variational Learning of Bayesian Neural Networks with Horseshoe PriorsCode0
How much income inequality is fair? Nash bargaining solution and its connection to entropy—0
Conditional Noise-Contrastive Estimation of Unnormalised Models—0
PAC-learning in the presence of evasion adversaries—0
Private PAC learning implies finite Littlestone dimension—0
ALMN: Deep Embedding Learning with Geometrical Virtual Point Generating—0
Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation—0
LSTMs Exploit Linguistic Attributes of Data—0
Universality of Deep Convolutional Neural Networks—0
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