Summary of Scaling Tractable Probabilistic Circuits: a Systems Perspective, by Anji Liu et al.
Scaling Tractable Probabilistic Circuits: A Systems Perspective
by Anji Liu, Kareem Ahmed, Guy Van den Broeck
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes PyJuice, a novel GPU implementation design for Probabilistic Circuits (PCs), which are deep generative models supporting exact and efficient probabilistic inference. PCs have recently been applied to complex tasks, but existing implementations are time- and memory-inefficient, hindering further scaling up. PyJuice addresses this challenge by introducing a compilation process that converts PCs into compact representations amenable to efficient block-based parallelization, reducing IO and leveraging Tensor Cores in modern GPUs. This leads to significant speedups (1-2 orders of magnitude) and memory reductions (2-5x). PyJuice enables the training of larger models and achieves state-of-the-art results on image (e.g., ImageNet32) and language (e.g., WikiText, CommonGen) datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a special tool that helps computers learn from data by making smart guesses. This tool is called Probabilistic Circuits (PCs). PCs are really good at helping computers make predictions about things like pictures and words. But making these tools work efficiently on big computers has been a challenge. A team of researchers created a new way to make PCs work faster and use less memory, called PyJuice. This allows them to train even bigger and better PCs that can help computers learn from huge amounts of data. |
Keywords
» Artificial intelligence » Inference
