Awesome Energy Based Models/Learning (Awesome-EBM)
The Awesome-EBM project is a curated collection that focuses on energy-based models (EBMs) and energy-based learning techniques. It serves as a comprehensive resource for researchers, students, and practitioners interested in understanding and advancing the field of EBMs. This project doesn't merely list papers, but organizes a trove of knowledge to help guide users through the diverse applications and developments in the area.
Table of Contents
The repository includes several sections, each focusing on different aspects of energy-based learning. Here are the main components:
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Workshops & Symposiums: This section highlights events and gatherings such as workshops that focus on energy-based models, providing opportunities for learning and collaboration.
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Representative Applications: This part outlines various real-world uses of EBMs. These include data generation for images, graphs, and sequences, discriminative learning tasks like classification and regression, density estimation, anomaly and fraud detection, model calibration, and robustness improvements against adversarial inputs. Additionally, it covers advanced applications in image processing, language and speech modeling, and robotics planning.
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Papers (Reverse Chronological Order): The repository includes a chronological list of publications on EBMs, grouped by year. This makes it easier for users to navigate through recent advancements and historical development in the field. Each paper listed comes with a link to further study its content in depth.
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Tutorials & Talks & Blogs: This section provides resources like tutorials, talks, and blog posts, helping newcomers get acquainted with energy-based models and their implementation.
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Open Source Libraries: Many practitioners benefit from open-source libraries that are listed here. These libraries can support individuals in implementing and experimenting with energy-based models in various projects.
In-Depth Topics
Workshops & Symposiums
Workshops like the EBM Workshop at ICLR 2021 create opportunities for academics and professionals to discuss the latest research and emerging topics in energy-based models. These venues often foster collaborations, offering deep insights and pushing the boundaries of the field.
Representative Applications
EBMs can be used in versatile ways:
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Data Generation: EBMs are effective in generating new data instances in different domains such as images, sequences, and graphs.
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Discriminative Learning: They are utilized for tasks that require distinguishing between different classes or predicting continuous outcomes efficiently.
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Density Estimation and Maximum Entropy: These are used for understanding data distributions and reinforcement learning tasks that necessitate entropy maximization.
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Out-of-Distribution Detection: Identifying anomalies or instances that don't fit a known data distribution is crucial for applications like fraud detection.
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Robustness Improvements: EBMs enhance the robustness of models against adversaries, making them more reliable in challenging environments.
Papers (Reverse Chronological Order)
This section is a library of research papers organized by publication year. It provides an easy way to track the progression of research in energy-based models from the earliest work before 2007 to the latest advancements in 2023. Each entry includes the title, authors, and a link to the full paper, serving as a valuable resource for those who wish to delve deep into specific topics.
Tutorials & Talks & Blogs
These resources form a bridge between theoretical knowledge and practical implementation, offering insights from experts in the field. They are particularly useful for beginners who want to learn more about energy-based models and how to apply them in real-world scenarios.
Open Source Libraries
Open-source libraries are essential tools for developers and researchers. They provide pre-built functions and algorithms, allowing users to implement energy-based models quickly and focus on experimentation and customization.
Overall, the Awesome-EBM project is an essential stop for anyone interested in the field of energy-based models. Whether you're looking to understand the fundamental concepts, apply EBMs to solve complex problems, or keep up-to-date with cutting-edge research, this compilation has something to offer.