Search Results for author: Sergio Guadarrama

Found 18 papers, 11 papers with code

Multi-Game Decision Transformers

no code implementations30 May 2022 Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch

Specifically, we show that a single transformer-based model - with a single set of weights - trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance.

Atari Games Offline RL

Compressive Visual Representations

1 code implementation NeurIPS 2021 Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, Ian Fischer

We verify this by developing SimCLR and BYOL formulations compatible with the Conditional Entropy Bottleneck (CEB) objective, allowing us to both measure and control the amount of compression in the learned representation, and observe their impact on downstream tasks.

Contrastive Learning Self-Supervised Image Classification

Measuring the Reliability of Reinforcement Learning Algorithms

1 code implementation ICLR 2020 Stephanie C. Y. Chan, Samuel Fishman, John Canny, Anoop Korattikara, Sergio Guadarrama

To aid RL researchers and production users with the evaluation and improvement of reliability, we propose a set of metrics that quantitatively measure different aspects of reliability.


From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

no code implementations ICLR 2019 Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama

In this work, we investigate the problem of grounding language commands as reward functions using inverse reinforcement learning, and argue that language-conditioned rewards are more transferable than language-conditioned policies to new environments.


The Devil is in the Decoder: Classification, Regression and GANs

1 code implementation18 Jul 2017 Zbigniew Wojna, Vittorio Ferrari, Sergio Guadarrama, Nathan Silberman, Liang-Chieh Chen, Alireza Fathi, Jasper Uijlings

Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.

Boundary Detection Depth Estimation +2

PixColor: Pixel Recursive Colorization

no code implementations19 May 2017 Sergio Guadarrama, Ryan Dahl, David Bieber, Mohammad Norouzi, Jonathon Shlens, Kevin Murphy

Then, given the generated low-resolution color image and the original grayscale image as inputs, we train a second CNN to generate a high-resolution colorization of an image.


Semantic Instance Segmentation via Deep Metric Learning

1 code implementation30 Mar 2017 Alireza Fathi, Zbigniew Wojna, Vivek Rathod, Peng Wang, Hyun Oh Song, Sergio Guadarrama, Kevin P. Murphy

We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.

Instance Segmentation Metric Learning +2

Improved Image Captioning via Policy Gradient optimization of SPIDEr

2 code implementations ICCV 2017 Si-Qi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, Kevin Murphy

Finally, we show that using our PG method we can optimize any of the metrics, including the proposed SPIDEr metric which results in image captions that are strongly preferred by human raters compared to captions generated by the same model but trained to optimize MLE or the COCO metrics.

Image Captioning

Im2Calories: Towards an Automated Mobile Vision Food Diary

no code implementations ICCV 2015 Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

7 code implementations CVPR 2015 Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, Trevor Darrell

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise.

Video Recognition

Caffe: Convolutional Architecture for Fast Feature Embedding

2 code implementations20 Jun 2014 Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell

The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.

Dimensionality Reduction General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.