Zobrazeno 1 - 10
of 492
pro vyhledávání: '"Nguyen, Viet anh"'
Two-stage risk-averse distributionally robust optimization (DRO) problems are ubiquitous across many engineering and business applications. Despite their promising resilience, two-stage DRO problems are generally computationally intractable. To addre
Externí odkaz:
http://arxiv.org/abs/2412.17257
Autor:
Nguyen, Viet Anh, Nguyen, Linh Thi Dieu, Do, Thi Thu Ha, Wu, Ye, Sergeev, Aleksandr A., Zhu, Ding, Valuckas, Vytautas, Pham, Duong, Bui, Hai Xuan Son, Hoang, Duy Mai, Bui, Son Tung, Bui, Xuan Khuyen, Nguyen, Binh Thanh, Nguyen, Hai Son, Vu, Lam Dinh, Rogach, Andrey, Ha, Son Tung, Le-Van, Quynh
Publikováno v:
J Phys Chem Lett J Phys Chem Lett . 2024 Nov 14;15(45):11291-11299
Enhancing light emission from perovskite nanocrystal (NC) films is essential in light-emitting devices, as their conventional stacks often restrict the escape of emitted light. This work addresses this challenge by employing a TiO$_2$ grating to enha
Externí odkaz:
http://arxiv.org/abs/2411.12463
Latent space optimization (LSO) is a powerful method for designing discrete, high-dimensional biological sequences that maximize expensive black-box functions, such as wet lab experiments. This is accomplished by learning a latent space from availabl
Externí odkaz:
http://arxiv.org/abs/2411.11265
Unsupervised pre-training on vast amounts of graph data is critical in real-world applications wherein labeled data is limited, such as molecule properties prediction or materials science. Existing approaches pre-train models for specific graph domai
Externí odkaz:
http://arxiv.org/abs/2409.19117
Autor:
Gehlawat, Sahil, Nguyên, Viêt-Anh
Let $\mathcal{F}_d(\mathbb{P}^n)$ be the space of all singular holomorphic foliations by curves on $\mathbb{P}^n$ ($n \geq 2$) with degree $d \geq 1.$ We show that there is subset $\mathcal{S}_d(\mathbb{P}^n)$ of $\mathcal{F}_d(\mathbb{P}^n)$ with fu
Externí odkaz:
http://arxiv.org/abs/2409.06052
The empirical Wasserstein projection (WP) distance quantifies the Wasserstein distance from the empirical distribution to a set of probability measures satisfying given expectation constraints. The WP is a powerful tool because it mitigates the curse
Externí odkaz:
http://arxiv.org/abs/2408.11753
The versatility of self-attention mechanism earned transformers great success in almost all data modalities, with limitations on the quadratic complexity and difficulty of training. To apply transformers across different data modalities, practitioner
Externí odkaz:
http://arxiv.org/abs/2408.05822
The versatility of self-attention mechanism earned transformers great success in almost all data modalities, with limitations on the quadratic complexity and difficulty of training. Efficient transformers, on the other hand, often rely on clever data
Externí odkaz:
http://arxiv.org/abs/2408.05391
Intrinsic capability to continuously learn a changing data stream is a desideratum of deep neural networks (DNNs). However, current DNNs suffer from catastrophic forgetting, which hinders remembering past knowledge. To mitigate this issue, existing C
Externí odkaz:
http://arxiv.org/abs/2406.17381
Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to multiple instances of the covariates. We introduce a novel neural entropic optimal transport meth
Externí odkaz:
http://arxiv.org/abs/2406.02317