25 citations · 64 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…
Robust Locally-Linear Controllable Embedding
Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh +2
Embed-to-control (E2C) is a model for solving high-dimensional optimal control problems by combining variational auto-encoders with locally-optimal controllers. However, the E2C mo…
Fast Spectral Clustering Using Autoencoders and Landmarks
Ershad Banijamali, Ali Ghodsi
In this paper, we introduce an algorithm for performing spectral clustering efficiently. Spectral clustering is a powerful clustering algorithm that suffers from high computational…
Generative Mixture of Networks
Ershad Banijamali, Ali Ghodsi, Pascal Poupart
A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data…