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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 1Source ↗PDF ↗Scholar ↗
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 56Source ↗PDF ↗Scholar ↗