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Found 5 papers, 2 papers with code
Algorithmic progress in language models
We investigate the rate at which algorithms for pre-training language models have improved since the advent of deep learning.
The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
We provide a data-driven survey of the role of the compute divide in shaping machine learning research.
Large Language Model Routing with Benchmark Datasets
There is a rapidly growing number of open-source Large Language Models (LLMs) and benchmark datasets to compare them.
The Grand Illusion: The Myth of Software Portability and Implications for ML Progress
Collectively, our results reveal how costly straying from a narrow set of hardware-software combinations can be - and suggest that specialization of hardware impedes innovation in machine learning research.
Economic impacts of AI-augmented R&D
Since its emergence around 2010, deep learning has rapidly become the most important technique in Artificial Intelligence (AI), producing an array of scientific firsts in areas as diverse as protein folding, drug discovery, integrated chip design, and weather prediction.
