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Found 16 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.
All-in-One Tuning and Structural Pruning for Domain-Specific LLMs
Therefore, such a combination of the pruning decisions and the finetuned weights may be suboptimal, leading to non-negligible performance degradation.
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases
Our approach conceptualizes domain knowledge as natural language and introduces a specialized multimodal GNN capable of leveraging this uncurated knowledge to guide the learning process of the GNN, such that it can improve the model performance and strengthen the interpretability of the predictions.
Dynamic Uncertainty Ranking: Enhancing In-Context Learning for Long-Tail Knowledge in LLMs
Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training.
Transfer Learning with Clinical Concept Embeddings from Large Language Models
Large Language Models (LLMs) show significant potential of capturing the semantic meaning of clinical concepts and reducing heterogeneity.
Unlocking Memorization in Large Language Models with Dynamic Soft Prompting
Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation.
Pruning as a Domain-specific LLM Extractor
Moreover, by efficiently approximating weight importance with the refined training loss on a domain-specific calibration dataset, we obtain a pruned model emphasizing generality and specificity.
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
Large Language Models (LLMs) have achieved exceptional capabilities in open generation across various domains, yet they encounter difficulties with tasks that require intensive knowledge.
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 Recent Survey of Heterogeneous Transfer Learning
In this paper, we offer an extensive review of over 60 HTL methods, covering both data-based and model-based approaches.
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.
