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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 226–250 of 796 papers

TitleStatusHype
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity RecognitionCode1
Data-Centric Green AI: An Exploratory Empirical StudyCode0
Model Predictive Control for Fluid Human-to-Robot Handovers—0
Compositional optimization of quantum circuits for quantum kernels of support vector machines—0
Stability and Risk Bounds of Iterative Hard Thresholding—0
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation—0
Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction—0
The Structured Abstain Problem and the Lovász Hinge—0
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Regret Bounds for Expected Improvement Algorithms in Gaussian Process Bandit Optimization—0
Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive BiasesCode1
HEAR: Holistic Evaluation of Audio RepresentationsCode1
A Characterization of Multiclass Learnability—0
Fault-Tolerant Neural Networks from Biological Error Correction Codes—0
M2I: From Factored Marginal Trajectory Prediction to Interactive PredictionCode2
Parameterized Intractability for Multi-Winner Election under the Chamberlin-Courant Rule and the Monroe Rule—0
Overcoming a Theoretical Limitation of Self-AttentionCode0
Dissecting graph measure performance for node clustering in LFR parameter spaceCode1
Partially Fake Audio Detection by Self-attention-based Fake Span Discovery—0
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam—0
On characterizations of learnability with computable learners—0
Complexity of Arithmetic in Warded Datalog+-—0
A hypothesis-driven method based on machine learning for neuroimaging data analysis—0
Machine Explanations and Human UnderstandingCode1
Width is Less Important than Depth in ReLU Neural Networks—0
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