Search Results for author:
Found 4 papers, 1 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
G-SemTMO: Tone Mapping with a Trainable Semantic Graph
In this work, we draw inspiration from an expert photographer's approach and present a Graph-based Semantic-aware Tone Mapping Operator, G-SemTMO.
Example-Based Named Entity Recognition
We present a novel approach to named entity recognition (NER) in the presence of scarce data that we call example-based NER.
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology available to every developer.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
