18 citations · 27 across the 5 of their papers we have counts for
5 papers
Training a Vision Transformer from scratch in less than 24 hours with 1 GPU
Saghar Irandoust, Thibaut Durand, Yunduz Rakhmangulova +2
Transformers have become central to recent advances in computer vision. However, training a vision Transformer (ViT) model from scratch can be resource intensive and time consuming…
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Yu Gong, Hossein Hajimirsadeghi, Jiawei He +2
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missin…
Point Process Flows
Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed +5
Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly model…
A Variational Auto-Encoder Model for Stochastic Point Processes
Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand +3
We propose a novel probabilistic generative model for action sequences. The model is termed the Action Point Process VAE (APP-VAE), a variational auto-encoder that can capture the…
Learning a Deep ConvNet for Multi-label Classification with Partial Labels
Thibaut Durand, Nazanin Mehrasa, Greg Mori
Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pi…