sampling method

Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees AAAI 2022 A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization ICML 2021 A Hybrid Stochastic Gradient Hamiltonian Monte Carlo Method AAAI 2021 Kernel Stein Discrepancy Descent ICML 2021 Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions NeurIPS 2021 On Sampling-Based Training Criteria for Neural Language Modeling Interspeech 2021 Optimal Underdamped Langevin MCMC Method NeurIPS 2021 Parallel tempering on optimized paths ICML 2021 SHPOS: A Theoretical Guaranteed Accelerated Particle Optimization Sampling Method IJCAI 2021 Speeding Up Transformer Training By Using Dataset Subsampling - An Exploratory Analysis EMNLP 2021 Tight sampling and discarding bounds for scenario programs with an arbitrary number of removed samples L4DC 2021 Validity-Based Sampling and Smoothing Methods for Multiple Reference Image Captioning NAACL 2021 Bandit Samplers for Training Graph Neural Networks NeurIPS 2020 GCN meets GPU: Decoupling “When to Sample” from “How to Sample” NeurIPS 2020 Grid-GCN for Fast and Scalable Point Cloud Learning CVPR 2020 Involutive MCMC: a Unifying Framework ICML 2020 Low-Variance and Zero-Variance Baselines for Extensive-Form Games ICML 2020 Multiplicative Gaussian Particle Filter AISTATS 2020 Reverse Engineering Configurations of Neural Text Generation Models ACL 2020 Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020 UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis SemEval 2020 Empirical Evaluation of Active Learning Techniques for Neural MT EMNLP 2019 On two ways to use determinantal point processes for Monte Carlo integration NeurIPS 2019 Probabilistic Planning with Sequential Monte Carlo methods ICLR 2019 Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo NeurIPS 2018