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Found 8 papers, 7 papers with code
Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices
The user's pulse wave is then used to determine stress (according to the Baevsky Stress Index), heart rate, and heart rate variability.
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.
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers
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.
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction.
Neural RST-based Evaluation of Discourse Coherence
We evaluate our approach on the Grammarly Corpus for Discourse Coherence (GCDC) and show that when ensembled with the current state of the art, we can achieve the new state of the art accuracy on this benchmark.
Ranked #1 on
Coherence Evaluation
on GCDC + RST - F1
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.
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)
Improved Few-Shot Visual Classification
Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data.
Ranked #2 on
Few-Shot Image Classification
on Mini-Imagenet 10-way (5-shot)
(using extra training data)
