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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 501–550 of 796 papers

TitleStatusHype
Would You Like to Hear the News? Investigating Voice-BasedSuggestions for Conversational News Recommendation—0
Domain Adaptive Image-to-Image Translation—0
Neural Networks with Small Weights and Depth-Separation Barriers—0
Fast Learning in Reproducing Kernel Krein Spaces via Signed Measures—0
Syntactic Structure Distillation Pretraining For Bidirectional Encoders—0
On the Impossibility of Global Convergence in Multi-Loss OptimizationCode0
Mathematical analysis and potential therapeutic implications of a novel HIV-1 model of basal and activated transcription in T-cells and macrophages—0
Continuous LWE—0
Implicit Regularization in Deep Learning May Not Be Explainable by NormsCode0
On the Global Convergence Rates of Softmax Policy Gradient Methods—0
A Re-Examination of the Evidence used by Hooge et al (2018) "Is human classification by experienced untrained observers a gold standard in fixation detection?"—0
A Comparison of Explicit and Implicit Proactive Dialogue Strategies for Conversational Recommendation—0
Rotation-invariant clustering of neuronal responses in primary visual cortex—0
Learning and Testing Junta Distributions with Subcube Conditioning—0
Low-rank matrix completion theory via Plucker coordinates—0
Characterizing the memory capacity of transmon qubit reservoirs—0
Scalable Autonomous Vehicle Safety Validation through Dynamic Programming and Scene Decomposition—0
Stopping Criteria for, and Strong Convergence of, Stochastic Gradient Descent on Bottou-Curtis-Nocedal Functions—0
Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints—0
Distinguishing Cell Phenotype Using Cell EpigenotypeCode0
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs—0
An Equivalence Between Private Classification and Online Prediction—0
The Spectral Underpinning of word2vec—0
Coherent Gradients: An Approach to Understanding Generalization in Gradient Descent-based Optimization—0
Network-Density-Controlled Decentralized Parallel Stochastic Gradient Descent in Wireless Systems—0
Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic—0
Optimal Signal-Adaptive Trading with Temporary and Transient Price Impact—0
Langevin DQNCode0
Metric-Free Individual Fairness in Online Learning—0
Learning Halfspaces with Massart Noise Under Structured Distributions—0
Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm—0
Think Global, Act Local: Relating DNN generalisation and node-level SNRCode0
Deepfakes for Medical Video De-Identification: Privacy Protection and Diagnostic Information Preservation—0
Near-Optimal Algorithms for Minimax Optimization—0
Learning functions varying along a central subspace—0
Memory capacity of neural networks with threshold and ReLU activations—0
A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy—0
Questioning the AI: Informing Design Practices for Explainable AI User Experiences—0
On the (In)tractability of Computing Normalizing Constants for the Product of Determinantal Point Processes—0
A general recurrent state space framework for modeling neural dynamics during decision-making—0
Exploiting the Sensitivity of L_2 Adversarial Examples to Erase-and-Restore—0
Learning from Learning Machines: Optimisation, Rules, and Social Norms—0
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem—0
Noisy, Greedy and Not So Greedy k-means++—0
Learning Representations for Time Series ClusteringCode0
Sample Complexity of Learning Mixture of Sparse Linear Regressions—0
A Reparameterization-Invariant Flatness Measure for Deep Neural Networks—0
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?—0
All-Pay Bidding Games on Graphs—0
Information-Theoretic Perspective of Federated Learning—0
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