We conduct four experiments, each containing a different set of known and unknown classes, using the GTZAN and the FMA datasets to establish a baseline capacity for novel genre detection.
In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way.
1 code implementation • 31 Dec 2021 • Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, Roman Wang, Nikhil Singh, Taylor L. Patti, Jayson Lynch, Avi Shporer, Nakul Verma, Eugene Wu, Gilbert Strang
We automatically synthesize programs using few-shot learning and OpenAI's Codex transformer and execute them to solve course problems at 81% automatic accuracy.
This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction.
We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT).