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Found 8 papers, 3 papers with code
Gemma 2: Improving Open Language Models at a Practical Size
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters.
Pushing the performances of ASR models on English and Spanish accents
Speech to text models tend to be trained and evaluated against a single target accent.
Learned Force Fields Are Ready For Ground State Catalyst Discovery
We present evidence that learned density functional theory (``DFT'') force fields are ready for ground state catalyst discovery.
ASR4REAL: An extended benchmark for speech models
Popular ASR benchmarks such as Librispeech and Switchboard are limited in the diversity of settings and speakers they represent.
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production.
An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage
As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand.
Inspirational Adversarial Image Generation
We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image.
GDPP: Learning Diverse Generations Using Determinantal Point Process
Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic-looking images.
