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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 601–650 of 796 papers

TitleStatusHype
SemEval-2019 Task 4: Hyperpartisan News Detection—0
Where is the Information in a Deep Neural Network?—0
Geolocating Political Events in TextCode0
A Neuro-AI Interface: Learning DNNs from the Human Brain—0
Path Planning Problems with Side Observations-When Colonels Play Hide-and-SeekCode0
End-to-End Pore Extraction and Matching in Latent Fingerprints: Going Beyond Minutiae—0
Private Learning Implies Online Learning: An Efficient Reduction—0
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis—0
Shaping the learning landscape in neural networks around wide flat minima—0
What Do Adversarially Robust Models Look At?Code0
Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel k-means Clustering—0
Autonomous Open-Ended Learning of Interdependent Tasks—0
OPIEC: An Open Information Extraction CorpusCode0
Quantification of Nematic Cell Polarity in Three-dimensional Tissues—0
Synthetic Neural Vision System Design for Motion Pattern Recognition in Dynamic Robot Scenes—0
Deep Learning for Large-Scale Traffic-Sign Detection and RecognitionCode0
An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression—0
Is Deeper Better only when Shallow is Good?Code0
Convolutional Analysis Operator Learning: Dependence on Training DataCode0
Learning Linear-Quadratic Regulators Efficiently with only T Regret—0
Exploring Frame Segmentation Networks for Temporal Action Localization—0
Deep Divergence-Based Approach to Clustering—0
A Theory of Selective Prediction—0
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis—0
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players—0
Deep learning-based electroencephalography analysis: a systematic reviewCode0
The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students—0
Using Scene Graph Context to Improve Image Generation—0
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning—0
Mapper Comparison with Wasserstein MetricsCode0
On the Dimensionality of Word EmbeddingCode0
SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction—0
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation—0
PAC-learning in the presence of adversaries—0
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity—0
Practical methods for graph two-sample testingCode0
Multi-scale variability in neuronal competition—0
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions—0
Importance of Search and Evaluation Strategies in Neural Dialogue ModelingCode0
Depth with Nonlinearity Creates No Bad Local Minima in ResNets—0
Effective Parallelisation for Machine Learning—0
Agnostic Sample Compression Schemes for Regression—0
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images—0
Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching—0
Fast Binary Functional Search on Graph—0
Discovering Features in Sr_14Cu_24O_41 Neutron Single Crystal Diffraction Data by Cluster Analysis—0
Searching for Efficient Multi-Scale Architectures for Dense Image Prediction—0
Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification—0
A Tree-based Decoder for Neural Machine TranslationCode0
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study—0
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