no code implementations • 4 Oct 2022 • Peyman Bateni, Leonid Sigal
The user's pulse wave is then used to determine stress (according to the Baevsky Stress Index), heart rate, and heart rate variability.
2 code implementations • 13 Jan 2022 • Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Jan-Willem van de Meent, Leonid Sigal, Frank Wood
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.
2 code implementations • University of British Columbia Theses and Dissertations 2021 • Peyman Bateni
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.
no code implementations • 22 Apr 2021 • Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, Frank Wood
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction.
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Grigorii Guz, Peyman Bateni, Darius Muglich, Giuseppe Carenini
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
2 code implementations • 28 Sep 2020 • Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood
We propose a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance.
2 code implementations • 17 Jun 2020 • Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood
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)
2 code implementations • CVPR 2020 • Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal
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)