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pro vyhledávání: '"Jung, A"'
Autor:
Research, LG AI, An, Soyoung, Bae, Kyunghoon, Choi, Eunbi, Choi, Stanley Jungkyu, Choi, Yemuk, Hong, Seokhee, Hong, Yeonjung, Hwang, Junwon, Jeon, Hyojin, Jo, Gerrard Jeongwon, Jo, Hyunjik, Jung, Jiyeon, Jung, Yountae, Kim, Euisoon, Kim, Hyosang, Kim, Joonkee, Kim, Seonghwan, Kim, Soyeon, Kim, Sunkyoung, Kim, Yireun, Kim, Youchul, Lee, Edward Hwayoung, Lee, Haeju, Lee, Honglak, Lee, Jinsik, Lee, Kyungmin, Lee, Moontae, Lee, Seungjun, Lim, Woohyung, Park, Sangha, Park, Sooyoun, Park, Yongmin, Seo, Boseong, Yang, Sihoon, Yeen, Heuiyeen, Yoo, Kyungjae, Yun, Hyeongu
We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model sizes, we publicly release the 7.8B instruction-tuned model to promote ope
Externí odkaz:
http://arxiv.org/abs/2408.03541
While the abundance of rich and vast datasets across numerous fields has facilitated the advancement of natural language processing, sectors in need of specialized data types continue to struggle with the challenge of finding quality data. Our study
Externí odkaz:
http://arxiv.org/abs/2407.18442
Recent advancements in large language models have heavily relied on the large reward model from reinforcement learning from human feedback for fine-tuning. However, the use of a single reward model across various domains may not always be optimal, of
Externí odkaz:
http://arxiv.org/abs/2407.17546
Autor:
Lee, Jung Hyun, Kim, Jeonghoon, Yang, June Yong, Kwon, Se Jung, Yang, Eunho, Yoo, Kang Min, Lee, Dongsoo
With the commercialization of large language models (LLMs), weight-activation quantization has emerged to compress and accelerate LLMs, achieving high throughput while reducing inference costs. However, existing post-training quantization (PTQ) techn
Externí odkaz:
http://arxiv.org/abs/2407.11534
We introduce ConTEXTure, a generative network designed to create a texture map/atlas for a given 3D mesh using images from multiple viewpoints. The process begins with generating a front-view image from a text prompt, such as 'Napoleon, front view',
Externí odkaz:
http://arxiv.org/abs/2407.10558
Autor:
Hwang, Seo-Bin, Kim, Han-Young, Heo, Chae-Yeon, Jung, Hie-Yong, Jung, Sung-Ju, Cho, Yeong-Jun
The flatfish is a major farmed species consumed globally in large quantities. However, due to the densely populated farming environment, flatfish are susceptible to injuries and diseases, making early disease detection crucial. Traditionally, disease
Externí odkaz:
http://arxiv.org/abs/2407.11348
Autor:
Oh, Changdae, Seo, Gyeongdeok, Jung, Geunyoung, Cheng, Zhi-Qi, Choi, Hosik, Jung, Jiyoung, Song, Kyungwoo
With the surge of large-scale pre-trained models (PTMs), adapting these models to numerous downstream tasks becomes a crucial problem. Consequently, parameter-efficient transfer learning (PETL) of large models has grasped huge attention. While PETL m
Externí odkaz:
http://arxiv.org/abs/2407.17491
Autor:
Kim, Sanghyun, Lee, Min Jung, Kim, Woohyeok, Jung, Deunsol, Rim, Jaesung, Cho, Sunghyun, Cho, Minsu
Burst image super-resolution has been a topic of active research in recent years due to its ability to obtain a high-resolution image by using complementary information between multiple frames in the burst. In this work, we explore using burst shots
Externí odkaz:
http://arxiv.org/abs/2406.17869
Autor:
Lim, Sungjun, Yeom, Jeyoon, Kim, Sooyon, Byun, Hoyoon, Kang, Jinho, Jung, Yohan, Jung, Jiyoung, Song, Kyungwoo
The large-scale pre-trained neural network has achieved notable success in enhancing performance for downstream tasks. Another promising approach for generalization is Bayesian Neural Network (BNN), which integrates Bayesian methods into neural netwo
Externí odkaz:
http://arxiv.org/abs/2406.15664
Autor:
Li, Chao, Pokorný, Vladislav, Žonda, Martin, Liu, Jung-Ching, Zhou, Ping, Chahib, Outhmane, Glatzel, Thilo, Häner, Robert, Decurtins, Silvio, Liu, Shi-Xia, Pawlak, Rémy, Meyer, Ernst
Magnetic impurities on superconductors present a viable platform for building advanced applications in quantum technologies. However, a controlled manipulation of their quantum states continues to pose a significant challenge, hindering the progress
Externí odkaz:
http://arxiv.org/abs/2408.05115