Search Results for author: Ershad Banijamali

Found 12 papers, 0 papers with code

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

no code implementations CVPR 2021 Elmira Amirloo, Mohsen Rohani, Ershad Banijamali, Jun Luo, Pascal Poupart

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.

Autonomous Driving Motion Planning

Deep Variational Sufficient Dimensionality Reduction

no code implementations18 Dec 2018 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 of the observations regarding the label variable is preserved.

Dimensionality Reduction General Classification

Optimizing over a Restricted Policy Class in Markov Decision Processes

no code implementations26 Feb 2018 Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis

However, under a condition that is akin to the occupancy measures of the base policies having large overlap, we show that there exists an efficient algorithm that finds a policy that is almost as good as the best convex combination of the base policies.

Policy Gradient Methods

JADE: Joint Autoencoders for Dis-Entanglement

no code implementations24 Nov 2017 Ershad Banijamali, Amir-Hossein Karimi, Alexander Wong, Ali Ghodsi

The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis.

General Classification

Disentangling Dynamics and Content for Control and Planning

no code implementations24 Nov 2017 Ershad Banijamali, Ahmad Khajenezhad, Ali Ghodsi, Mohammad Ghavamzadeh

In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems.

Robust Locally-Linear Controllable Embedding

no code implementations15 Oct 2017 Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi

We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise.

Fast Spectral Clustering Using Autoencoders and Landmarks

no code implementations7 Apr 2017 Ershad Banijamali, Ali Ghodsi

Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition.

Generative Mixture of Networks

no code implementations10 Feb 2017 Ershad Banijamali, Ali Ghodsi, Pascal Poupart

The model consists of K networks that are trained together to learn the underlying distribution of a given data set.

Radon-Gabor Barcodes for Medical Image Retrieval

no code implementations16 Sep 2016 Mina Nouredanesh, H. R. Tizhoosh, Ershad Banijamali, James Tung

The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks.

Medical Image Retrieval

Gabor Barcodes for Medical Image Retrieval

no code implementations14 May 2016 Mina Nouredanesh, Hamid. R. Tizhoosh, Ershad Banijamali

This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side.

Content-Based Image Retrieval Medical Image Retrieval

Semi-Supervised Representation Learning based on Probabilistic Labeling

no code implementations10 May 2016 Ershad Banijamali, Ali Ghodsi

Then, we map the data to lower-dimensional space using a linear transformation such that the dependency between the transformed data and the assigned labels is maximized.

Representation Learning

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