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Found 10 papers, 3 papers with code
Fast OSCAR and OWL with Safe Screening Rules
Ordered Weight $L_{1}$-Norms (OWL) is a new family of regularizers for high-dimensional sparse regression.
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that require domain-specific expertise, making their widespread adoption challenging.
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration
Though Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains, they struggle with knowledge-intensive tasks.
Online Transfer Learning for RSV Case Detection
Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications.
A Survey of Heterogeneous Transfer Learning
These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption.
Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning
Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge.
Sampling Through the Lens of Sequential Decision Making
Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a representation.
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification
To address this challenge, we propose a novel accelerated doubly stochastic gradient descent (ADSGD) method for sparsity regularized loss minimization problems, which can reduce the number of block iterations by eliminating inactive coefficients during the optimization process and eventually achieve faster explicit model identification and improve the algorithm efficiency.
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization
To the best of our knowledge, this is the first work of distributed safe dynamic screening method.
Fast OSCAR and OWL Regression via Safe Screening Rules
Moreover, we prove that the algorithms with our screening rule are guaranteed to have identical results with the original algorithms.
