Search Results for author:
Found 12 papers, 4 papers with code
Variance Reduction and Quasi-Newton for Particle-Based Variational Inference
Particle-based Variational Inference methods (ParVIs), like Stein Variational Gradient Descent, are nonparametric variational inference methods that optimize a set of particles to best approximate a target distribution.
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation
Additionally, based on the same intuition, we propose a 2-Step method, which takes both conversation and summary as input to simulate human annotation process.
Recurrent Neural Networks with Mixed Hierarchical Structures and EM Algorithm for Natural Language Processing
In this paper, we propose a novel approach called the latent indicator layer to identify and learn implicit hierarchical information (e. g., phrases), and further develop an EM algorithm to handle the latent indicator layer in training.
2020 U.S. presidential election in swing states: Gender differences in Twitter conversations
Social media is commonly used by the public during election campaigns to express their opinions regarding different issues.
COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twitter
Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021.
Recurrent Neural Networks with Mixed Hierarchical Structures for Natural Language Processing
Hierarchical structures exist in both linguistics and Natural Language Processing (NLP) tasks.
Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization
Hyperparameters play a critical role in the performances of many machine learning methods.
Towards Differentiable Resampling
Resampling is a key component of sample-based recursive state estimation in particle filters.
Sample Adaptive MCMC
For MCMC methods like Metropolis-Hastings, tuning the proposal distribution is important in practice for effective sampling from the target distribution \pi.
Group Additive Structure Identification for Kernel Nonparametric Regression
The additive model is one of the most popularly used models for high dimensional nonparametric regression analysis.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model.
A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora.
