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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 201–225 of 796 papers

TitleStatusHype
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks—0
Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification—0
Distribution-Independent PAC Learning of Halfspaces with Massart Noise—0
DNF-Net: A Neural Architecture for Tabular Data—0
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks—0
2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments—0
Evaluation of Object Trackers in Distorted Surveillance Videos—0
Domain Adaptive Image-to-Image Translation—0
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation—0
Do pretrained transformers infer telicity like humans?—0
A Reparameterization-Invariant Flatness Measure for Deep Neural Networks—0
Autonomous Open-Ended Learning of Interdependent Tasks—0
Doubly Adversarial Federated Bandits—0
Drift anticipation with forgetting to improve evolving fuzzy system—0
Most Important Person-guided Dual-branch Cross-Patch Attention for Group Affect Recognition—0
Duff: A Dataset-Distance-Based Utility Function Family for the Exponential Mechanism—0
Dynamic Algorithms for Matroid Submodular Maximization—0
Dynamic Network Model from Partial Observations—0
Evaluating Machine Common Sense via Cloze Testing—0
Effective Parallelisation for Machine Learning—0
Efficient Bi-level Optimization for Non-smooth Optimization—0
Efficient human-like semantic representations via the Information Bottleneck principle—0
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking—0
Efficient Online Learning via Randomized Rounding—0
A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation—0
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