Zobrazeno 1 - 10
of 735
pro vyhledávání: '"Ma, Junwei"'
Autor:
Ma, Junwei, Thomas, Valentin, Hosseinzadeh, Rasa, Kamkari, Hamidreza, Labach, Alex, Cresswell, Jesse C., Golestan, Keyvan, Yu, Guangwei, Volkovs, Maksims, Caterini, Anthony L.
The challenges faced by neural networks on tabular data are well-documented and have hampered the progress of tabular foundation models. Techniques leveraging in-context learning (ICL) have shown promise here, allowing for dynamic adaptation to unsee
Externí odkaz:
http://arxiv.org/abs/2410.18164
Despite significant anecdotal evidence regarding the vulnerability of the U.S. power infrastructure, there is a dearth of longitudinal and nation-level characterization of the spatial and temporal patterns in the frequency and extent of power outages
Externí odkaz:
http://arxiv.org/abs/2408.15882
Advances in deep generative modelling have not translated well to tabular data. We argue that this is caused by a mismatch in structure between popular generative models and discriminative models of tabular data. We thus devise a technique to turn Ta
Externí odkaz:
http://arxiv.org/abs/2406.05216
Autor:
Thomas, Valentin, Ma, Junwei, Hosseinzadeh, Rasa, Golestan, Keyvan, Yu, Guangwei, Volkovs, Maksims, Caterini, Anthony
Tabular data is a pervasive modality spanning a wide range of domains, and the inherent diversity poses a considerable challenge for deep learning. Recent advancements using transformer-based in-context learning have shown promise on smaller and less
Externí odkaz:
http://arxiv.org/abs/2406.05207
Examining the relationship between vulnerability of the built environment and community recovery is crucial for understanding disaster resilience. Yet, this relationship is rather neglected in the existing literature due to previous limitations in th
Externí odkaz:
http://arxiv.org/abs/2405.03874
Contrastive learning is a model pre-training technique by first creating similar views of the original data, and then encouraging the data and its corresponding views to be close in the embedding space. Contrastive learning has witnessed success in i
Externí odkaz:
http://arxiv.org/abs/2404.17489
Autor:
Ma, Junwei, Mostafavi, Ali
Examining the impact of disasters on life activities of populations is critical for understanding community resilience dynamics, yet it remains insufficiently studied in the existing literature. In this study, we leveraged data from more than 1.2 mil
Externí odkaz:
http://arxiv.org/abs/2402.15434
Foundation models have revolutionized tasks in computer vision and natural language processing. However, in the realm of tabular data, tree-based models like XGBoost continue to dominate. TabPFN, a transformer model tailored for tabular data, mirrors
Externí odkaz:
http://arxiv.org/abs/2402.06971
Autor:
Lei, Yuchen, Ma, Junwei, Luo, Jiaming, Huang, Shenyang, Yu, Boyang, Song, Chaoyu, Xing, Qiaoxia, Wang, Fanjie, Xie, Yuangang, Zhang, Jiasheng, Mu, Lei, Ma, Yixuan, Wang, Chong, Yan, Hugen
The evolution of excitons from 2D to 3D is of great importance in photo-physics, yet the layer-dependent exciton polarizability has not been investigated in 2D semiconductors. Here, we determine the exciton polarizabilities for 3- to 11-layer black p
Externí odkaz:
http://arxiv.org/abs/2309.10327
Autor:
Wang, Chong, Xie, Yuangang, Ma, Junwei, Hu, Guangwei, Xing, Qiaoxia, Huang, Shenyang, Song, Chaoyu, Wang, Fanjie, Lei, Yuchen, Zhang, Jiasheng, Mu, Lei, Zhang, Tan, Huang, Yuan, Qiu, Cheng-Wei, Yao, Yugui, Yan, Hugen
Publikováno v:
Nano Letters, 2023
Stacking bilayer structures is an efficient way to tune the topology of polaritons in in-plane anisotropic films, e.g., by leveraging the twist angle (TA). However, the effect of another geometric parameter, film thickness ratio (TR), on manipulating
Externí odkaz:
http://arxiv.org/abs/2307.14586