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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 551–600 of 796 papers

TitleStatusHype
Distilling Knowledge Learned in BERT for Text GenerationCode0
A comparison of end-to-end models for long-form speech recognition—0
Relative Maximum Likelihood Updating of Ambiguous Beliefs—0
Sample Complexity of Learning Mixtures of Sparse Linear Regressions—0
Hyperbolic Node Embedding for Signed Networks—0
TRB: A Novel Triplet Representation for Understanding 2D Human BodyCode0
Capacity, Bandwidth, and Compositionality in Emergent Language LearningCode0
gradSLAM: Automagically differentiable SLAMCode0
Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents—0
hv-Block Cross Validation is not a BIBD: a Note on the Paper by Jeff Racine (2000)—0
Nonrobustness of asymptotic stability of impulsive systems with inputs—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Thompson Sampling in Non-Episodic Restless Bandits—0
How does the Mind store Information?—0
On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps—0
Towards a Metric for Automated Conversational Dialogue System Evaluation and Improvement—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee—0
Feature-Robustness, Flatness and Generalization Error for Deep Neural Networks—0
Where is the Information in a Deep Network?—0
Coresets for Accelerating Incremental Gradient Methods—0
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained EnvironmentsCode0
Slices of Attention in Asynchronous Video Job Interviews—0
A Guaranteed Convergence Analysis for the Projected Fast Iterative Soft-Thresholding Algorithm in Parallel MRI—0
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking—0
Kernel absolute summability is only sufficient for RKHS stability—0
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear ControlCode0
An Open-Source Framework for Adaptive Traffic Signal ControlCode0
Ensemble-Based Deep Reinforcement Learning for Chatbots—0
Encoder-Agnostic Adaptation for Conditional Language GenerationCode0
Chromosomal rearrangements as a source of new gene formation in Drosophila yakuba—0
Neural Machine Translation with Noisy Lexical Constraints—0
Deep Video Precoding—0
Weakly deterministic transformations are subregular—0
Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering—0
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?Code0
A Neural Network Detector for Spectrum Sensing under Uncertainties—0
Computational Concentration of Measure: Optimal Bounds, Reductions, and More—0
Global Aggregations of Local Explanations for Black Box models—0
Benchmarking Model-Based Reinforcement LearningCode0
Distribution-Independent PAC Learning of Halfspaces with Massart Noise—0
Investigating BERT's Knowledge of Language: Five Analysis Methods with NPIs—0
Is the Policy Gradient a Gradient?—0
Augmenting Neural Networks with First-order LogicCode0
Scalable Syntax-Aware Language Models Using Knowledge Distillation—0
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations—0
Enhancing Gradient-based Attacks with Symbolic Intervals—0
Evaluating Explanation Methods for Deep Learning in SecurityCode0
Autonomous Reinforcement Learning of Multiple Interrelated Tasks—0
Average-case Analysis of the Assignment Problem with Independent Preferences—0
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