2 code implementations • 29 Feb 2024 • ShangHua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen, Theodoros Tsiligkaridis, Marinka Zitnik
However, current foundation models apply to sequence data but not to time series, which present unique challenges due to the inherent diverse and multidomain time series datasets, diverging task specifications across forecasting, classification and other types of tasks, and the apparent need for task-specialized models.
1 code implementation • 8 Dec 2023 • Teddy Koker, Keegan Quigley, Eric Taw, Kevin Tibbetts, Lin Li
The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge.
1 code implementation • 6 Feb 2023 • Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, Marinka Zitnik
Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain.
no code implementations • 24 Nov 2022 • Teddy Koker, Keegan Quigley, Will Spaeth, Nathan C. Frey, Lin Li
By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks.
no code implementations • 29 Sep 2021 • William Alejandro Falcon, Ananya Harsh Jha, Teddy Koker, Kyunghyun Cho
We empirically evaluate the proposed AAVAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
1 code implementation • 26 Jul 2021 • William Falcon, Ananya Harsh Jha, Teddy Koker, Kyunghyun Cho
We empirically evaluate the proposed AASAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated.
1 code implementation • 14 Jan 2021 • Teddy Koker, FatemehSadat Mireshghallah, Tom Titcombe, Georgios Kaissis
Deep Neural Networks (DNNs) are widely used for decision making in a myriad of critical applications, ranging from medical to societal and even judicial.