Search Results for author: Mohammad Babaeizadeh

Found 16 papers, 9 papers with code

INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

no code implementations ICLR 2022 Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine

We propose a modified objective for model-based RL that, in combination with mutual information maximization, allows us to learn representations and dynamics for visual model-based RL without reconstruction in a way that explicitly prioritizes functionally relevant factors.

Model-based Reinforcement Learning

FitVid: High-Capacity Pixel-Level Video Prediction

no code implementations29 Sep 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan

Furthermore, such an agent can internally represent the complex dynamics of the real-world and therefore can acquire a representation useful for a variety of visual perception tasks.

Image Augmentation Video Prediction

FitVid: Overfitting in Pixel-Level Video Prediction

1 code implementation24 Jun 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, Dumitru Erhan

There is a growing body of evidence that underfitting on the training data is one of the primary causes for the low quality predictions.

Image Augmentation Video Generation +1

On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning

no code implementations1 Jan 2021 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Dumitru Erhan, Harini Kannan, Chelsea Finn, Sergey Levine

In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.

Model-based Reinforcement Learning reinforcement-learning

Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning

1 code implementation8 Dec 2020 Mohammad Babaeizadeh, Mohammad Taghi Saffar, Danijar Hafner, Harini Kannan, Chelsea Finn, Sergey Levine, Dumitru Erhan

In this paper, we study a number of design decisions for the predictive model in visual MBRL algorithms, focusing specifically on methods that use a predictive model for planning.

Model-based Reinforcement Learning reinforcement-learning

Model Based Reinforcement Learning for Atari

no code implementations ICLR 2020 Łukasz Kaiser, Mohammad Babaeizadeh, Piotr Miłos, Błażej Osiński, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Model-based Reinforcement Learning +2

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

1 code implementation ICLR 2020 Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions.

Frame Predict Future Video Frames +1

Model-Based Reinforcement Learning for Atari

4 code implementations1 Mar 2019 Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski

We describe Simulated Policy Learning (SimPLe), a complete model-based deep RL algorithm based on video prediction models and present a comparison of several model architectures, including a novel architecture that yields the best results in our setting.

Atari Games Atari Games 100k +3

Adjustable Real-time Style Transfer

1 code implementation ICLR 2020 Mohammad Babaeizadeh, Golnaz Ghiasi

Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another.

Style Transfer

Time Reversal as Self-Supervision

no code implementations2 Oct 2018 Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar

We test our method on the domain of assembly, specifically the mating of tetris-style block pairs.

Stochastic Variational Video Prediction

3 code implementations ICLR 2018 Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, Sergey Levine

We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods.

Frame Video Generation +1

Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case

no code implementations29 Sep 2017 Faraz Faghri, Sayed Hadi Hashemi, Mohammad Babaeizadeh, Mike A. Nalls, Saurabh Sinha, Roy H. Campbell

In an effort to overcome the data deluge in computational biology and bioinformatics and to facilitate bioinformatics research in the era of big data, we identify some of the most influential algorithms that have been widely used in the bioinformatics community.

Dimensionality Reduction General Classification

Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

3 code implementations18 Nov 2016 Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks.

reinforcement-learning

NoiseOut: A Simple Way to Prune Neural Networks

no code implementations18 Nov 2016 Mohammad Babaeizadeh, Paris Smaragdis, Roy H. Campbell

In this paper, we propose NoiseOut, a fully automated pruning algorithm based on the correlation between activations of neurons in the hidden layers.

Seq-NMS for Video Object Detection

1 code implementation26 Feb 2016 Wei Han, Pooya Khorrami, Tom Le Paine, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang

Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip.

Frame General Classification +3

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