Zobrazeno 1 - 10
of 1 673
pro vyhledávání: '"Kim, MinSu"'
Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure pr
Externí odkaz:
http://arxiv.org/abs/2410.17270
Autor:
Seo, Seonghwan, Kim, Minsu, Shen, Tony, Ester, Martin, Park, Jinkyoo, Ahn, Sungsoo, Kim, Woo Youn
Generative models in drug discovery have recently gained attention as efficient alternatives to brute-force virtual screening. However, most existing models do not account for synthesizability, limiting their practical use in real-world scenarios. In
Externí odkaz:
http://arxiv.org/abs/2410.04542
Autor:
Kim, Hyeonah, Kim, Minsu, Yun, Taeyoung, Choi, Sanghyeok, Bengio, Emmanuel, Hernández-García, Alex, Park, Jinkyoo
Designing biological sequences with desired properties is a significant challenge due to the combinatorially vast search space and the high cost of evaluating each candidate sequence. To address these challenges, reinforcement learning (RL) methods,
Externí odkaz:
http://arxiv.org/abs/2410.04461
The variational quantum eigensolver algorithm has gained attentions due to its capability of locating the ground state and ground energy of a Hamiltonian, which is a fundamental task in many physical and chemical problems. Although it has demonstrate
Externí odkaz:
http://arxiv.org/abs/2410.03130
Autor:
Kim, Minsu, Choi, Sanghyeok, Yun, Taeyoung, Bengio, Emmanuel, Feng, Leo, Rector-Brooks, Jarrid, Ahn, Sungsoo, Park, Jinkyoo, Malkin, Nikolay, Bengio, Yoshua
Amortized inference is the task of training a parametric model, such as a neural network, to approximate a distribution with a given unnormalized density where exact sampling is intractable. When sampling is implemented as a sequential decision-makin
Externí odkaz:
http://arxiv.org/abs/2410.01432
Model fairness is becoming important in class-incremental learning for Trustworthy AI. While accuracy has been a central focus in class-incremental learning, fairness has been relatively understudied. However, naively using all the samples of the cur
Externí odkaz:
http://arxiv.org/abs/2410.01324
Autor:
Cappellazzo, Umberto, Kim, Minsu, Chen, Honglie, Ma, Pingchuan, Petridis, Stavros, Falavigna, Daniele, Brutti, Alessio, Pantic, Maja
Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. For example, in the audio and speech domains, an LLM can be equipped with (automatic) speech recogn
Externí odkaz:
http://arxiv.org/abs/2409.12319
Autor:
Moon, Seungjae, Kim, Jung-Hoon, Kim, Junsoo, Hong, Seongmin, Cha, Junseo, Kim, Minsu, Lim, Sukbin, Choi, Gyubin, Seo, Dongjin, Kim, Jongho, Lee, Hunjong, Park, Hyunjun, Ko, Ryeowook, Choi, Soongyu, Park, Jongse, Lee, Jinwon, Kim, Joo-Young
The explosive arrival of OpenAI's ChatGPT has fueled the globalization of large language model (LLM), which consists of billions of pretrained parameters that embodies the aspects of syntax and semantics. HyperAccel introduces latency processing unit
Externí odkaz:
http://arxiv.org/abs/2408.07326
Recently, there has been an extensive research effort in building efficient large language model (LLM) inference serving systems. These efforts not only include innovations in the algorithm and software domains but also constitute developments of var
Externí odkaz:
http://arxiv.org/abs/2408.05499
We propose a new framework for creating and easily manipulating 3D models of arbitrary objects using casually captured videos. Our core ingredient is a novel hierarchy deformation model, which captures motions of objects with a tree-structured bones.
Externí odkaz:
http://arxiv.org/abs/2408.00351