Search Results for author: Supawit Chockchowwat

Found 5 papers, 0 papers with code

Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility

no code implementations15 May 2023 Supawit Chockchowwat, Zhaoheng Li, Yongjoo Park

In machine learning (ML), Python serves as a convenient abstraction for working with key libraries such as PyTorch, scikit-learn, and others.

Probabilistic PolarGMM: Unsupervised Cluster Learning of Very Noisy Projection Images of Unknown Pose

no code implementations26 Jun 2022 Supawit Chockchowwat, Chandrajit L. Bajaj

A crucial step in single particle analysis (SPA) of cryogenic electron microscopy (Cryo-EM), 2D classification and alignment takes a collection of noisy particle images to infer orientations and group similar images together.

3D Reconstruction Cryogenic Electron Microscopy (cryo-EM) +1

Airphant: Cloud-oriented Document Indexing

no code implementations26 Dec 2021 Supawit Chockchowwat, Chaitanya Sood, Yongjoo Park

However, simply placing existing search engines (e. g., Apache Lucene) on top of cloud storage significantly increases their end-to-end query latencies (i. e., more than 6 seconds on average in one of our studies).

Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning

no code implementations27 May 2019 Caleb Chuck, Supawit Chockchowwat, Scott Niekum

Deep reinforcement learning (DRL) is capable of learning high-performing policies on a variety of complex high-dimensional tasks, ranging from video games to robotic manipulation.

reinforcement-learning Reinforcement Learning (RL)

Functional Generative Design: An Evolutionary Approach to 3D-Printing

no code implementations19 Apr 2018 Cem C. Tutum, Supawit Chockchowwat, Etienne Vouga, Risto Miikkulainen

The proposed methodology for discovering solutions to this problem consists of three components: First, an effective search space is learned through a variational autoencoder (VAE); second, a surrogate model for functional designs is built; and third, a genetic algorithm is used to simultaneously update the hyperparameters of the surrogate and to optimize the designs using the updated surrogate.

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