Results 91 to 100 of about 1,688,693 (190)
The impact of fine-tuning paradigms on unknown plant diseases recognition
Scientific ReportsPlant diseases pose significant threats to agriculture, impacting both food safety and public health. Traditional plant disease detection systems are typically limited to recognizing disease categories included in the training dataset, rendering them ...
Jiuqing Dong +5 more
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Assessing the Effectiveness of Transfer Learning in Chest X-Ray Analysis With the BRAX Dataset
IEEE AccessThe integration of machine learning techniques in medicine has significantly enhanced medical diagnostics, making them more efficient, agile, and accurate.
Elton Douglas Silva +4 more
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A structure-based coarse-fine approach for diversity tuning in cellular GAs
, 2012 This article empirically assesses a coarse-fine approach for diversity tuning in cellular Genetic Algorithms (cGAs). The coarse tuning is performed through the constant reconfiguration of the grid while the fine tuning is locally achieved through dynamic
ALICIA MORALES REYES, Ahmet Erdogan
core
Fine-tuning of pre-trained ASR models for transcription of Italian fiscal codes
reservedThis thesis presents an automatic speech recognition (ASR) system fine-tuned for the accurate extraction of Italian tax codes (codici fiscali) from spoken input.
BORASO, FRANCESCO
core
LLMs on a Budget: System-Level Approaches to Power-Efficient and Scalable Fine-Tuning
IEEE Open Journal of the Computer SocietyLarge Language Models (LLMs) have shown remarkable capabilities in various applications, including robotics, telecommunications, and scientific discovery.
Kailash Gogineni +2 more
doaj +1 more source
CoRRLarge language model fine-tuning techniques typically depend on extensive labeled data, external guidance, and feedback, such as human alignment, scalar rewards, and demonstration. However, in practical application, the scarcity of specific knowledge poses unprecedented challenges to existing fine-tuning techniques.
Jia Liu 0009 +5 more
openaire +2 more sources
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape.
Wan, W +9 more
core +1 more source
FedPFT: Federated Proxy Fine-Tuning of Foundation Models
Adapting Foundation Models (FMs) for down- stream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs.
Peng, Z +7 more
core +1 more source
An enhanced low-rank fine-tuning framework for federated large language models
Federated Learning (FL) enables collaborative training of Large Language Models (LLMs) across distributed devices without centralizing private data. However, two critical barriers impede practical deployment.
Zhu, Jiachen +3 more
core +1 more source