activity
20162021
most citedJADE: Joint Autoencoders for Dis-Entanglement

16 citations · 27 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2021

Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map

Elmira Amirloo, Mohsen Rohani, Ershad Banijamali +2

While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed. In…

cs.LG2020

Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning

Ershad Banijamali, Mohsen Rohani, Elmira Amirloo +2

In autonomous driving (AD), accurately predicting changes in the environment can effectively improve safety and comfort. Due to complex interactions among traffic participants, how…

cs.LG20186 cited

Deep Variational Sufficient Dimensionality Reduction

Ershad Banijamali, Amir-Hossein Karimi, Ali Ghodsi

We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information…

cs.LG2018

Optimizing over a Restricted Policy Class in Markov Decision Processes

Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh +1

We address the problem of finding an optimal policy in a Markov decision process under a restricted policy class defined by the convex hull of a set of base policies. This problem…

cs.LG20172 cited

Disentangling Dynamics and Content for Control and Planning

Ershad Banijamali, Ahmad Khajenezhad, Ali Ghodsi +1

In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems. Specifically, we consider a situation where th…

cs.LG201716 cited

JADE: Joint Autoencoders for Dis-Entanglement

Ershad Banijamali, Amir-Hossein Karimi, Alexander Wong +1

The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis. State-of-the-art methods for disentangl…