Shreshth A Malik

Shreshth A Malik
  • University of Oxford

About

6
Publications
365
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15
Citations
Current institution
University of Oxford

Publications

Publications (6)
Conference Paper
Full-text available
Accurate estimation of thermospheric density is critical for precise modeling of satellite drag forces in low Earth orbit (LEO). Improving this estimation is crucial to tasks such as state estimation, collision avoidance, and re-entry calculations. The largest source of uncertainty in determining thermospheric density is modeling the effects of spa...
Preprint
Full-text available
We introduce BatchGFN -- a novel approach for pool-based active learning that uses generative flow networks to sample sets of data points proportional to a batch reward. With an appropriate reward function to quantify the utility of acquiring a batch, such as the joint mutual information between the batch and the model parameters, BatchGFN is able...
Preprint
Full-text available
Automated planetary transit detection has become vital to prioritize candidates for expert analysis given the scale of modern telescopic surveys. While current methods for short-period exoplanet detection work effectively due to periodicity in the light curves, there lacks a robust approach for detecting single-transit events. However, volunteer-la...
Preprint
Full-text available
When experience is scarce, models may have insufficient information to adapt to a new task. In this case, auxiliary information - such as a textual description of the task - can enable improved task inference and adaptation. In this work, we propose an extension to the Model-Agnostic Meta-Learning algorithm (MAML), which allows the model to adapt u...
Article
A key bottleneck for material discovery is synthesis. While significant advances have been made in computational material design, synthesis pathways are still often determined through trial and error. In this work, we develop a method that predicts the major product of solid-state reactions. The main advance presented here is the construction of fi...
Preprint
A common bottleneck for materials discovery is synthesis. While recent methodological advances have resulted in major improvements in the ability to predicatively design novel materials, researchers often still rely on trial-and-error approaches for determining synthesis procedures. In this work, we develop a model that predicts the major product o...

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