Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning

no code implementations • 25 Nov 2023 • Melrose Roderick, Gaurav Manek, Felix Berkenkamp, J. Zico Kolter

A key problem in off-policy Reinforcement Learning (RL) is the mismatch, or distribution shift, between the dataset and the distribution over states and actions visited by the learned policy.

Q-Learning Reinforcement Learning (RL)

Model-based Reinforcement Learning with Ensembled Model-value Expansion

no code implementations • 29 Sep 2021 • Gaurav Manek, J Zico Kolter

Model-based reinforcement learning (MBRL) methods are often more data-efficient and quicker to converge than their model-free counterparts, but typically rely crucially on accurate modeling of the environment dynamics and associated uncertainty in order to perform well.

Model-based Reinforcement Learning reinforcement-learning +1

Learning Stable Deep Dynamics Models

1 code implementation • NeurIPS 2019 • Gaurav Manek, J. Zico Kolter

Deep networks are commonly used to model dynamical systems, predicting how the state of a system will evolve over time (either autonomously or in response to control inputs).

Efficient GAN-Based Anomaly Detection

7 code implementations • 17 Feb 2018 • Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, Vijay Ramaseshan Chandrasekhar

However, few works have explored the use of GANs for the anomaly detection task.

Anomaly Detection

Pruning Convolutional Neural Networks for Image Instance Retrieval

no code implementations • 18 Jul 2017 • Gaurav Manek, Jie Lin, Vijay Chandrasekhar, Ling-Yu Duan, Sateesh Giduthuri, Xiao-Li Li, Tomaso Poggio

In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN).

Image Instance Retrieval Retrieval

Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text

1 code implementation • 17 Jun 2017 • Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar

Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.

Classification General Classification +1

Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge

no code implementations • 26 May 2017 • Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Huiling Chen, Jie Lin, Babar Nazir, Cen Chen, Tse Chiang Howe, Zeng Zeng, Vijay Chandrasekhar

We present a deep learning framework for computer-aided lung cancer diagnosis.

Lung Cancer Diagnosis