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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

Showing 301325 of 796 papers

TitleStatusHype
Width is Less Important than Depth in ReLU Neural Networks0
Near-Optimal Learning of Extensive-Form Games with Imperfect Information0
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-ColoringsCode0
On the Convergence of Heterogeneous Federated Learning with Arbitrary Adaptive Online Model Pruning0
Evaluating Machine Common Sense via Cloze Testing0
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks0
Testing the Ability of Language Models to Interpret Figurative Language0
FogAdapt: Self-Supervised Domain Adaptation for Semantic Segmentation of Foggy Images0
PWM2Vec: An Efficient Embedding Approach for Viral Host Specification from Coronavirus Spike SequencesCode0
Universal Online Learning with Bounded Loss: Reduction to Binary Classification0
Exploring the Impact of Virtualization on the Usability of the Deep Learning Applications0
Explanation as Question Answering based on Design Knowledge0
Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge0
A Dynamical Model for the Origin of Anisogamy0
Finding Structure in Silence: The Role of Pauses in Aligning Speaker Expectations0
Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization0
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning0
On the Practical Consistency of Meta-Reinforcement Learning Algorithms0
On the Value of Infinite Gradients in Variational Autoencoder Models0
Think Big, Teach Small: Do Language Models Distil Occam’s Razor?Code0
Computational Complexity of Normalizing Constants for the Product of Determinantal Point Processes0
Simple Stochastic and Online Gradient DescentAlgorithms for Pairwise LearningCode0
Dynamic Regret for Strongly Adaptive Methods and Optimality of Online KRR0
Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach0
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals0
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