Adversarial Logit Pairing

4 code implementations • NeurIPS 2018 • Harini Kannan, Alexey Kurakin, Ian Goodfellow

In this paper, we develop improved techniques for defending against adversarial examples at scale.

Eye Tracking for Everyone

2 code implementations • CVPR 2016 • Kyle Krafka, Aditya Khosla, Petr Kellnhofer, Harini Kannan, Suchendra Bhandarkar, Wojciech Matusik, Antonio Torralba

We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices.

Gaze Estimation

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

1 code implementation • 8 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 (RL)

High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

no code implementations • NeurIPS 2019 • Ruben Villegas, Arkanath Pathak, Harini Kannan, Dumitru Erhan, Quoc V. Le, Honglak Lee

Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time.

Inductive Bias Optical Flow Estimation +2

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

no code implementations • 1 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 +1