Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift

Kavli Affiliate: Zhuo Li | First 5 Authors: Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li, Jianling Sun | Summary: Recent years have witnessed the success of introducing Transformers to time series forecasting. From a data generation perspective, we illustrate that existing Transformers are susceptible to distribution shifts driven by temporal contexts, whether observed or […]


Continue.. Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift

Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift

Kavli Affiliate: Zhuo Li | First 5 Authors: Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li, Jianling Sun | Summary: Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are susceptible to distribution shifts driven by temporal contexts, whether […]


Continue.. Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift

Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift

Kavli Affiliate: Zhuo Li | First 5 Authors: Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li, Jianling Sun | Summary: Recent years have witnessed the success of introducing deep learning models to time series forecasting. From a data generation perspective, we illustrate that existing models are susceptible to distribution shifts driven by temporal contexts, whether […]


Continue.. Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift

First Detection and Modeling of Spatially Resolved Ly$α$ in TW Hya

Kavli Affiliate: Gregory J. Herczeg | First 5 Authors: Seok-Jun Chang, Nicole Arulanantham, Max Gronke, Gregory J. Herczeg, Edwin A. Bergin | Summary: Lyman-$alpha$ (Ly$alpha$) is the strongest emission line in UV spectra from T-Tauri stars. Due to its resonant nature, Ly$alpha$ emission carries information about the physical properties of the H I medium via […]


Continue.. First Detection and Modeling of Spatially Resolved Ly$α$ in TW Hya

First Detection and Modeling of Spatially Resolved Ly$α$ in TW Hya

Kavli Affiliate: Gregory J. Herczeg | First 5 Authors: Seok-Jun Chang, Nicole Arulanantham, Max Gronke, Gregory J. Herczeg, Edwin A. Bergin | Summary: Lyman-$alpha$ (Ly$alpha$) is the strongest emission line in the accretion-generated UV spectra from T-Tauri stars and, as such, plays a critical role in regulating chemistry within the surrounding protoplanetary disks. Due to […]


Continue.. First Detection and Modeling of Spatially Resolved Ly$α$ in TW Hya

Water-Rich Disks around Late M-stars Unveiled: Exploring the Remarkable Case of Sz114

Kavli Affiliate: Gregory J. Herczeg | First 5 Authors: Chengyan Xie, Ilaria Pascucci, Feng Long, Klaus M. Pontoppidan, Andrea Banzatti | Summary: We present an analysis of the JWST/MIRI-MRS spectrum of Sz114, an accreting M5 star surrounded by a large dust disk with a gap at ~39au. The spectrum is molecular-rich: we report the detection […]


Continue.. Water-Rich Disks around Late M-stars Unveiled: Exploring the Remarkable Case of Sz114

Water-Rich Disks around Late M-stars Unveiled: Exploring the Remarkable Case of Sz114

Kavli Affiliate: Gregory J. Herczeg | First 5 Authors: Chengyan Xie, Ilaria Pascucci, Feng Long, Klaus M. Pontoppidan, Andrea Banzatti | Summary: We present an analysis of the JDISC JWST/MIRI-MRS spectrum of Sz~114, an accreting M5 star surrounded by a large dust disk with a shallow gap at $sim 39$ au. The spectrum is molecular-rich: […]


Continue.. Water-Rich Disks around Late M-stars Unveiled: Exploring the Remarkable Case of Sz114

Adaptive Fine-tuning based Transfer Learning for the Identification of MGMT Promoter Methylation Status

Kavli Affiliate: Jing Wang | First 5 Authors: Erich Schmitz, Yunhui Guo, Jing Wang, , | Summary: Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis. Treatment usually includes a mix of surgical resection, radiation therapy, and akylating chemotherapy but, even with these intensive treatments, the 2-year survival […]


Continue.. Adaptive Fine-tuning based Transfer Learning for the Identification of MGMT Promoter Methylation Status

Adaptive Fine-tuning based Transfer Learning for the Identification of MGMT Promoter Methylation Status

Kavli Affiliate: Jing Wang | First 5 Authors: Erich Schmitz, Yunhui Guo, Jing Wang, , | Summary: Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis. Treatment usually includes a mix of surgical resection, radiation therapy, and akylating chemotherapy but, even with these intensive treatments, the 2-year survival […]


Continue.. Adaptive Fine-tuning based Transfer Learning for the Identification of MGMT Promoter Methylation Status

Simple Mechanisms for Representing, Indexing and Manipulating Concepts

Kavli Affiliate: Li Xin Li| First 5 Authors: [#item_custom_name[1, [#item_custom_name[2, [#item_custom_name[3, [#item_custom_name[4, [#item_custom_name[5| Summary:Supervised and unsupervised learning using deep neural networks typically aims to exploit the underlying structure in the training data; this structure is often explained using a latent generative process that produces the data, and the generative process is often hierarchical, involving latent […]


Continue.. Simple Mechanisms for Representing, Indexing and Manipulating Concepts