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7 results for "Probabilistic AI"
7 results for "Probabilistic AI"
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Conference Paper
The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of these models, usually through applying transformations such as weight...
Flexgibbs: Reconfigurable parallel gibbs sampling accelerator for structured graphs
Conference Paper
Many consider one of the key components to the success of deep learning as its compatibility with existing accelerators, mainly GPU. While GPUs are great at handling linear algebra kernels commonly found in deep learning, they are not the optimal...
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Accelerating Bayesian Inference on Structured Graphs Using Parallel Gibbs Sampling
Conference Proceedings
Bayesian models and inference is a class of machine learning that is useful for solving problems where the amount of data is scarce and prior knowledge about the application allows you to draw better conclusions. However, Bayesian models often requires...
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