5 citations · 8 across the 6 of their papers we have counts for
7 papers
Feed-Forward Panel Estimation for Discrete-time Survival Analysis of Recurrent Events with Frailty
Borna Bateni, Peyman Bateni, Bishwadeep Bhattacharyya +1
In recurrent survival analysis where the event of interest can occur multiple times for each subject, frailty models play a crucial role by capturing unobserved heterogeneity at th…
Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices
Peyman Bateni, Leonid Sigal
Stress is considered to be the epidemic of the 21st-century. Yet, mobile apps cannot directly evaluate the impact of their content and services on user stress. We introduce the Bea…
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
Peyman Bateni, Jarred Barber, Raghav Goyal +4
Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectively from few labelled examp…
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
Adam Scibior, Vasileios Lioutas, Daniele Reda +2
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction. Agents are modeled with conditional recurrent variational neural…
Neural RST-based Evaluation of Discourse Coherence
Grigorii Guz, Peyman Bateni, Darius Muglich +1
This paper evaluates the utility of Rhetorical Structure Theory (RST) trees and relations in discourse coherence evaluation. We show that incorporating silver-standard RST features…
Enhancing Few-Shot Image Classification with Unlabelled Examples
Peyman Bateni, Jarred Barber, Jan-Willem van de Meent +1
We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-…
Improved Few-Shot Visual Classification
Peyman Bateni, Raghav Goyal, Vaden Masrani +2
Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date…