Results 111 to 120 of about 124,133 (290)
Fine-tuning protein language models boosts predictions across diverse tasks
Nature CommunicationsPrediction methods inputting embeddings from protein language models have reached or even surpassed state-of-the-art performance on many protein prediction tasks.
Robert Schmirler +2 more
doaj +1 more source
Exploring a New Architecture for Efficient Parameter Fine-Tuning in SLoRA Multitasking Scenarios
Applied SciencesPropose an enhanced LoRA (Low-Rank Adaptation) MoE (mixed expert) architecture, SLoRA (Enhanced LoRA MoE Architecture), aimed at addressing the key problem of efficient parameter fine-tuning in multitasking scenarios.
Ce Shi, Jin-Woo Jung
doaj +1 more source
Advanced Functional Materials, EarlyView.Liquid‐phase transmission electron microscopy enables direct observation of nucleation and growth processes in solution. This review is dedicated to the remembrance of Helmut Cölfen and highlights recent studies on complex materials—oxides, biominerals, organic–inorganic crystals—which were central to his research activity. It summarizes key milestones,
Charles Sidhoum +5 more
wiley +1 more source
PVP: Pre-trained Visual Parameter-Efficient Tuning
, 2023 Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. However, it is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs ...
Hu, Qingyong +5 more
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ANALYSIS OF WHISPER AUTOMATIC SPEECH RECOGNITION PERFORMANCE ON LOW RESOURCE LANGUAGE
Pilar Nusa MandiriImplementing Automatic Speech Recognition Technology in daily life could give convenience to its users. However, speeches that can be recognized accurately by the ASR model right now are in languages considered high resources, like English.
Riefkyanov Surya Adia Pratama +1 more
doaj +1 more source
PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark
Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)Despite the state-of-the-art performance of Large Language Models (LLMs) achieved on many tasks, their massive scale often leads to high computational and environmental costs, limiting their accessibility. Parameter-Efficient Fine-Tuning (PEFT) methods address this challenge by reducing the number of trainable parameters while maintaining strong ...
Róbert Belanec +3 more
openaire +3 more sources
Advanced Functional Materials, EarlyView.Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner +14 more
wiley +1 more source
Advanced Functional Materials, EarlyView.A swelling‐programmed micropatterned hydrogel guides adherent cells through a controlled transition from cell–matrix anchoring to cadherin‐mediated cell–cell compaction, enabling rapid assembly of high‐viability spheroids with defined size and morphology.
Han Gyeol Nam +8 more
wiley +1 more source
UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation
The Segment Anything Model 2 (SAM2) demonstrates remarkable universal segmentation capabilities on natural images. However, its performance on ultrasound images is significantly degraded due to domain disparities.
Chen, Xin +9 more
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Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT
, 2023 Large pre-trained language models have recently gained significant traction due to their improved performance on various down-stream tasks like text classification and question answering, requiring only few epochs of fine-tuning.
Kundu, Souvik +3 more
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