Zobrazeno 1 - 10
of 68 011
pro vyhledávání: '"Model and optimization"'
Expressive large-scale neural networks enable training powerful models for prediction tasks. However, in many engineering and science domains, such models are intended to be used not just for prediction, but for design -- e.g., creating new proteins
Externí odkaz:
http://arxiv.org/abs/2410.13106
Autor:
Tan, Rong-Xi, Xue, Ke, Lyu, Shen-Huan, Shang, Haopu, Wang, Yao, Wang, Yaoyuan, Fu, Sheng, Qian, Chao
Offline model-based optimization (MBO) aims to identify a design that maximizes a black-box function using only a fixed, pre-collected dataset of designs and their corresponding scores. A common approach in offline MBO is to train a regression-based
Externí odkaz:
http://arxiv.org/abs/2410.11502
Autor:
Yin, Xiaoyu, Peri, Elisabetta, Pelssers, Eduard, Toonder, Jaap den, Klous, Lisa, Daanen, Hein, Mischi, Massimo
Background and objective: Diabetes is one of the four leading causes of death worldwide, necessitating daily blood glucose monitoring. While sweat offers a promising non-invasive alternative for glucose monitoring, its application remains limited due
Externí odkaz:
http://arxiv.org/abs/2412.02870
Balancing mutually diverging performance metrics, such as, processing latency, outcome accuracy, and end device energy consumption is a challenging undertaking for deep learning model inference in ad-hoc edge environments. In this paper, we propose E
Externí odkaz:
http://arxiv.org/abs/2410.12221
Autor:
Yuan, Ye, Zhang, Youyuan, Chen, Can, Wu, Haolun, Li, Zixuan, Li, Jianmo, Clark, James J., Liu, Xue
Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores. These tasks span various domains, such as robotics, material design, and protein and molecular engineering. A
Externí odkaz:
http://arxiv.org/abs/2405.13964
Optimizing complex and high-dimensional black-box functions is ubiquitous in science and engineering fields. Unfortunately, the online evaluation of these functions is restricted due to time and safety constraints in most cases. In offline model-base
Externí odkaz:
http://arxiv.org/abs/2407.01624
Autor:
Uehara, Masatoshi, Zhao, Yulai, Hajiramezanali, Ehsan, Scalia, Gabriele, Eraslan, Gökcen, Lal, Avantika, Levine, Sergey, Biancalani, Tommaso
AI-driven design problems, such as DNA/protein sequence design, are commonly tackled from two angles: generative modeling, which efficiently captures the feasible design space (e.g., natural images or biological sequences), and model-based optimizati
Externí odkaz:
http://arxiv.org/abs/2405.19673
Autor:
Maionchi, Daniela de Oliveira, Coimbra, Neil Diogo Silva, da Silva, Junior Gonçalves, Santos, Fabio Pereira dos
Microfluidic devices are gaining attention for their small size and ability to handle tiny fluid volumes. Mixing fluids efficiently at this scale, known as micromixing, is crucial. This article builds upon previous research by introducing a novel opt
Externí odkaz:
http://arxiv.org/abs/2406.01728
In this paper, it was proposed a new concept of the inexact higher degree $(\delta, L, q)$-model of a function that is a generalization of the inexact $(\delta, L)$-model, $(\delta, L)$-oracle and $(\delta, L)$-oracle of degree $q \in [0,2)$. Some ex
Externí odkaz:
http://arxiv.org/abs/2405.16140
Optimization algorithms and large language models (LLMs) enhance decision-making in dynamic environments by integrating artificial intelligence with traditional techniques. LLMs, with extensive domain knowledge, facilitate intelligent modeling and st
Externí odkaz:
http://arxiv.org/abs/2405.10098