14 citations · 17 across the 8 of their papers we have counts for
9 papers
Looking through the mind's eye via multimodal encoder-decoder networks
Arman Afrasiyabi, Erica Busch, Rahul Singh +4
In this work, we explore the decoding of mental imagery from subjects using their fMRI measurements. In order to achieve this decoding, we first created a mapping between a subject…
Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi, Dhananjay Bhaskar, Erica L. Busch +5
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high tempora…
Density-based Isometric Mapping
Bardia Yousefi, Mélina Khansari, Ryan Trask +8
The isometric mapping method employs the shortest path algorithm to estimate the Euclidean distance between points on High dimensional (HD) manifolds. This may not be sufficient fo…
Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning
Mohamed Abid, Arman Afrasiyabi, Ihsen Hedhli +2
Generally, image-to-image translation (i2i) methods aim at learning mappings across domains with the assumption that the images used for translation share content (e.g., pose) but…
DarSwin: Distortion Aware Radial Swin Transformer
Akshaya Athwale, Arman Afrasiyabi, Justin Lagüe +3
Wide-angle lenses are commonly used in perception tasks requiring a large field of view. Unfortunately, these lenses produce significant distortions, making conventional models tha…
Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde +1
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this tr…
Mixture-based Feature Space Learning for Few-shot Image Classification
Arman Afrasiyabi, Jean-François Lalonde, Christian Gagné
We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich and robust feature representation in the context of few-shot image classification. Previous works h…
Associative Alignment for Few-shot Image Classification
Arman Afrasiyabi, Jean-François Lalonde, Christian Gagné
Few-shot image classification aims at training a model from only a few examples for each of the "novel" classes. This paper proposes the idea of associative alignment for leveragin…
Energy Saving Additive Neural Network
Arman Afrasiyabi, Ozan Yildiz, Baris Nasir +2
In recent years, machine learning techniques based on neural networks for mobile computing become increasingly popular. Classical multi-layer neural networks require matrix multipl…