19 citations · 43 across the 9 of their papers we have counts for
9 papers
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
Teddy Koker, Abhijeet Gangan, Mit Kotak +2
Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with a standard loss on ene…
Training a Foundation Model for Materials on a Budget
Teddy Koker, Mit Kotak, Tess Smidt
Foundation models for materials modeling are advancing quickly, but their training remains expensive, often placing state-of-the-art methods out of reach for many research groups.…
UniTS: A Unified Multi-Task Time Series Model
Shanghua Gao, Teddy Koker, Owen Queen +3
Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, w…
Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials
Teddy Koker, Keegan Quigley, Eric Taw +2
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 propertie…
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Owen Queen, Thomas Hartvigsen, Teddy Koker +3
Interpreting time series models is uniquely challenging because it requires identifying both the location of time series signals that drive model predictions and their matching to…
Domain Adaptation for Time Series Under Feature and Label Shifts
Huan He, Owen Queen, Teddy Koker +3
Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents…
Graph Contrastive Learning for Materials
Teddy Koker, Keegan Quigley, Will Spaeth +2
Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, how…
AASAE: Augmentation-Augmented Stochastic Autoencoders
William Falcon, Ananya Harsh Jha, Teddy Koker +1
Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentat…
U-Noise: Learnable Noise Masks for Interpretable Image Segmentation
Teddy Koker, Fatemehsadat Mireshghallah, Tom Titcombe +1
Deep Neural Networks (DNNs) are widely used for decision making in a myriad of critical applications, ranging from medical to societal and even judicial. Given the importance of th…