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Emily Pitler

2 accepted papers

2025

Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions

ICLR 2025poster

Neural Stochastic Differential Equations (Neural SDEs) have emerged as powerful mesh-free generative models for continuous stochastic processes, with critical applications in fields such as finance, physics, and biology. Previous state-of-the-art methods have relied on adversarial training, such as…

Cited by 1SourcePDFScholar
2022

Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing

EMNLP 2022main

Despite their strong performance on many tasks, pre-trained language models have been shown to struggle on out-of-distribution compositional generalization. Meanwhile, recent work has shown considerable improvements on many NLP tasks from model scaling. Can scaling up model size also improve composi…

Cited by 56SourcePDFScholar