15 papers · ranked by Valyu relevance
Emir Öztürk, Altan Mesut, Stefano Cirillo
Learning-based data compression methods have gained significant attention in recent years. Although these methods achieve higher compression ratios compared to traditional techniques, their slow processing times make them less suitable for compressing large datasets, and they are generally more effective for short…
Samir Brahim Belhaouari, Insaf Kraidia
Large Language Models (LLMs) have revolutionized artificial intelligence by enabling multitasking across diverse fields. However, their high computational demands result in significant environmental impacts, particularly in terms of energy and water consumption. This paper addresses these issues by proposing an…
Guoliang Yang, Shuaiying Yu, Hao Yang, Ziling Nie + 2 more
'Longxiu Huang'] Previous studies have shown that deep models are often over-parameterized, and this parameter redundancy makes deep compression possible. The redundancy of model weight is often manifested as low rank and sparsity. Ignoring any part of the two or the different distributions of these two characteristics…
Jeff Armstrong, Adam Jackson, Alin Elena
Validation of Machine-Learned Interatomic Potential Energy Landscapes Authors: Jeff Armstrong, Adam Jackson, Alin Elena Machine-learned interatomic potentials (MLIPs) promise near density-functional theory accuracy at a fraction of the computational cost, offering a route toward predictive atomistic modeling of…
Yong-Yeon Jo, Young Sang Choi, Hyun Woo Park, Jae Hyeok Lee + 6 more
'Hyojung Jung' 'Hyo-Eun Kim' 'Kyounglan Ko' 'Chan Wha Lee' 'Hyo Soung Cha' 'Yul Hwangbo'] Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to…
Huabin Diao, Yuexing Hao, Shaoyun Xu, Gongyan Li + 1 more
Convolutional neural networks (CNNs) have achieved significant breakthroughs in various domains, such as natural language processing (NLP), and computer vision. However, performance improvement is often accompanied by large model size and computation costs, which make it not suitable for resource-constrained devices.…
David Podgorelec, Damjan Strnad, Ivana Kolingerová, Borut Žalik + 1 more
'Jun Chen'] After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others…
Yuhang Dong, W. David Pan, Dongsheng Wu
Malaria is a severe public health problem worldwide, with some developing countries being most affected. Reliable remote diagnosis of malaria infection will benefit from efficient compression of high-resolution microscopic images. This paper addresses a lossless compression of malaria-infected red blood cell images…
Itzel Nunez, Afshin Marani, Moncef L. Nehdi
Recycled aggregate concrete (RAC) contributes to mitigating the depletion of natural aggregates, alleviating the carbon footprint of concrete construction, and averting the landfilling of colossal amounts of construction and demolition waste. However, complexities in the mixture optimization of RAC due to the…
Abdul Khader Jilani Saudagar
The overall goal of the research is to improve the quality of biomedical image for telemedicine with minimum percentages of noise in the retrieved image and to take less computation time. The novelty of this technique lies in the implementation of spectral coding for biomedical images using neural networks in order to…
Shuo Han, Bo Mo, Jie Zhao, Junwei Xu + 3 more
'Nikolaos Doulamis'] Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information…
Hengrui Liao, Yue Li, Rowayda Sadek
In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless…
Hagi Costa, Marianne Silva, Ignacio Sánchez-Gendriz, Carlos M. D. Viegas + 4 more
'Carlos M. D. Viegas' 'Ivanovitch Silva' 'Adnan Shahid' 'Eli De Poorter' 'Jaron Fontaine'] The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial…
Yuhang Dong, W. David Pan, Jonathan Wu, Thangarajah Akilan + 2 more
'Jitendra Kumar' 'Chengsheng Yuan'] Digital images are usually stored in compressed format. However, image classification typically takes decompressed images as inputs rather than compressed images. Therefore, performing image classification directly in the compression domain will eliminate the need for decompression…
Chen-Hsiu Huang, Ja-Ling Wu, Jun Chen
End-to-end learned image compression codecs have notably emerged in recent years. These codecs have demonstrated superiority over conventional methods, showcasing remarkable flexibility and adaptability across diverse data domains while supporting new distortion losses. Despite challenges such as computational…