Search Results for author:
Found 10 papers, 5 papers with code
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels
Gaussian processes are powerful models for probabilistic machine learning, but are limited in application by their $O(N^3)$ inference complexity.
Enhancing Few-Shot Image Classification with Unlabelled Examples
We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance.
Ranked #1 on
Few-Shot Image Classification
on Tiered ImageNet 10-way (1-shot)
(using extra training data)
Improving Few-Shot Visual Classification with Unlabelled Examples
We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance.
End-to-end Alexa Device Arbitration
We introduce a variant of the speaker localization problem, which we call device arbitration.
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks.
Challenges and Opportunities in Multi-device Speech Processing
We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and machine learning for multiple smart devices".
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Muse: Text-To-Image Generation via Masked Generative Transformers
Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations; compared to autoregressive models, such as Parti, Muse is more efficient due to the use of parallel decoding.
Ranked #1 on
Text-to-Image Generation
on MS-COCO
(FID metric)
SPADE: Self-supervised Pretraining for Acoustic DisEntanglement
Self-supervised representation learning approaches have grown in popularity due to the ability to train models on large amounts of unlabeled data and have demonstrated success in diverse fields such as natural language processing, computer vision, and speech.
StyleDrop: Text-to-Image Generation in Any Style
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency
We demonstrate image generation and captioning performance on par with state-of-the-art text-to-image and image-to-text models with orders of magnitude fewer (only 3M) paired image-text data.
