Search Results for author:
Found 6 papers, 4 papers with code
MemoryPrompt: A Light Wrapper to Improve Context Tracking in Pre-trained Language Models
Transformer-based language models (LMs) track contextual information through large, hard-coded input windows.
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili
But in an in-the-wild test on Swahili radio broadcasts with actual hate speech keywords, the AWE model (using one minute of template data) is more robust, giving similar performance to an ASR system trained on 30 hours of labelled data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Can discrete information extraction prompts generalize across language models?
We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information.
Benchmarking Algorithms from Machine Learning for Low-Budget Black-Box Optimization
Machine learning has invaded various domains of computer science, including black-box optimization.
Self-Attention for Audio Super-Resolution
Convolutions operate only locally, thus failing to model global interactions.
Ranked #1 on
Audio Super-Resolution
on Voice Bank corpus (VCTK)
(using extra training data)
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super resolution that is able to produce photorealistic images.
