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Found 5 papers, 4 papers with code
SparQ Attention: Bandwidth-Efficient LLM Inference
The computational difficulties of large language model (LLM) inference remain a significant obstacle to their widespread deployment.
Training and inference of large language models using 8-bit floating point
FP8 formats are gaining popularity to boost the computational efficiency for training and inference of large deep learning models.
Unit Scaling: Out-of-the-Box Low-Precision Training
We present unit scaling, a paradigm for designing deep learning models that simplifies the use of low-precision number formats.
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
Recent research has shown that graph neural networks (GNNs) can learn policies for locomotion control that are as effective as a typical multi-layer perceptron (MLP), with superior transfer and multi-task performance (Wang et al., 2018; Huang et al., 2020).
The Winnability of Klondike Solitaire and Many Other Patience Games
Our ignorance of the winnability percentage of the game in the Windows Solitaire program, more properly called 'Klondike', has been described as "one of the embarrassments of applied mathematics".
