Search Results for author:
Found 13 papers, 6 papers with code
Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance
This research not only contributes to the understanding of SLMs but also provides practical insights for researchers seeking efficient summarization solutions that balance performance and resource use.
Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference
Probabilistic graphical models (PGMs) serve as a powerful framework for modeling complex systems with uncertainty and extracting valuable insights from data.
Efficient Hyperparameter Optimization with Adaptive Fidelity Identification
Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method.
Interpretable Traffic Event Analysis with Bayesian Networks
With a concrete case study, our framework can derive a Bayesian Network from a dataset based on the causal relationships between weather and traffic events across the United States.
Fast Parallel Bayesian Network Structure Learning
The mainstream BN structure learning methods require performing a large number of conditional independence (CI) tests.
Fast Parallel Exact Inference on Bayesian Networks: Poster
Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models.
Efficient Second-Order Optimization for Deep Learning with Kernel Machines
However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training.
Entity Extraction with Knowledge from Web Scale Corpora
Entity extraction is an important task in text mining and natural language processing.
Practical Federated Gradient Boosting Decision Trees
There have been several recent studies on how to train GBDTs in the federated learning setting.
Privacy-Preserving Gradient Boosting Decision Trees
Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds.
Adaptive Kernel Value Caching for SVM Training
Our experimental results show that EFU often has 20\% higher hit ratio than LRU in the training with the Gaussian kernel.
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
By systematically summarizing the existing federated learning systems, we present the design factors, case studies, and future research opportunities.
Improving Efficiency of SVM k-fold Cross-validation by Alpha Seeding
The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters.
