Zobrazeno 1 - 10
of 1 699
pro vyhledávání: '"Yerim An"'
Publikováno v:
IEEE Access, Vol 12, Pp 162882-162893 (2024)
SRAM-based Compute-In-Memory (CIM) has two main paradigms: Digital domain and Analog domain, where both have been extensively explored to overcome the von-Neumann bottleneck and enhance energy efficiency. Digital CIM offers robustness and dynamic bit
Externí odkaz:
https://doaj.org/article/a35eaf25c46b4e73827797d14c733ab4
Autor:
Keun-Yong Chung, Honggu Kim, Yerim An, Kiho Seong, Dong-Hyun Shin, Kwang-Hyun Baek, Yong Shim
Publikováno v:
IEEE Access, Vol 12, Pp 24254-24261 (2024)
Process-in-memory (PIM) is an emerging computing paradigm to overcome the energy bottleneck inherent in conventional computing platform. While PIM utilizes several types of memory elements, SRAM based PIM has been researched extensively for its high
Externí odkaz:
https://doaj.org/article/c64f90fed6c24aaabfd8c354ae0f93e2
We introduce a novel continued pre-training method, MELT (MatEriaLs-aware continued pre-Training), specifically designed to efficiently adapt the pre-trained language models (PLMs) for materials science. Unlike previous adaptation strategies that sol
Externí odkaz:
http://arxiv.org/abs/2410.15126
Few-shot object counting has garnered significant attention for its practicality as it aims to count target objects in a query image based on given exemplars without the need for additional training. However, there is a shortcoming in the prevailing
Externí odkaz:
http://arxiv.org/abs/2408.09734
Temporal action detection (TAD) is challenging, yet fundamental for real-world video applications. Large temporal scale variation of actions is one of the most primary difficulties in TAD. Naturally, multi-scale features have potential in localizing
Externí odkaz:
http://arxiv.org/abs/2408.09354
Autor:
Yim, Jinkyu, Song, Jaeyong, Choi, Yerim, Lee, Jaebeen, Jung, Jaewon, Jang, Hongsun, Lee, Jinho
Training large language models (LLMs) is known to be challenging because of the huge computational and memory capacity requirements. To address these issues, it is common to use a cluster of GPUs with 3D parallelism, which splits a model along the da
Externí odkaz:
http://arxiv.org/abs/2405.18093
Autor:
Alabdulkareem, Abdulrahman, Arnold, Christian M, Lee, Yerim, Feenstra, Pieter M, Katz, Boris, Barbu, Andrei
Traditional security mechanisms isolate resources from users who should not access them. We reflect the compositional nature of such security mechanisms back into the structure of LLMs to build a provably secure LLM; that we term SecureLLM. Other app
Externí odkaz:
http://arxiv.org/abs/2405.09805
Autor:
Nam, Ju-Hyeon, Park, Seo-Hyeong, Syazwany, Nur Suriza, Jung, Yerim, Im, Yu-Han, Lee, Sang-Chul
Polyp segmentation is crucial for preventing colorectal cancer a common type of cancer. Deep learning has been used to segment polyps automatically, which reduces the risk of misdiagnosis. Localizing small polyps in colonoscopy images is challenging
Externí odkaz:
http://arxiv.org/abs/2310.05538
Publikováno v:
Frontiers in Zoology, Vol 21, Iss 1, Pp 1-17 (2024)
Abstract Background Lachninae (Insecta: Aphididae) represent a fascinating group of aphids that are traditionally divided into five tribes. Among these, members of the tribe Tuberolachnini exhibit remarkable morphological and biological diversity. On
Externí odkaz:
https://doaj.org/article/76f045e6d1724cfdaf2e41b131f2fd18
Local feature matching is essential for many applications, such as localization and 3D reconstruction. However, it is challenging to match feature points accurately in various camera viewpoints and illumination conditions. In this paper, we propose a
Externí odkaz:
http://arxiv.org/abs/2301.10413