1 citations · 1 across the 4 of their papers we have counts for
4 papers
Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift
Surbhi Goel, Jonathan Pei, James Wang
Behavior cloning provides strong imitation learning guarantees when training and test environments share the same dynamics. However, in many deployment settings the test environmen…
QIANets: Quantum-Integrated Adaptive Networks for Reduced Latency and Improved Inference Times in CNN Models
Zhumazhan Balapanov, Vanessa Matvei, Olivia Holmberg +3
Convolutional neural networks (CNNs) have made significant advances in computer vision tasks, yet their high inference times and latency often limit real-world applicability. While…
SWAG: Storytelling With Action Guidance
Zeeshan Patel, Karim El-Refai, Jonathan Pei +1
Automated long-form story generation typically employs long-context large language models (LLMs) for one-shot creation, which can produce cohesive but not necessarily engaging cont…
PREADD: Prefix-Adaptive Decoding for Controlled Text Generation
Jonathan Pei, Kevin Yang, Dan Klein
We propose Prefix-Adaptive Decoding (PREADD), a flexible method for controlled text generation. Unlike existing methods that use auxiliary expert models to control for attributes,…