medarc/AlgonautsDS-features
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MedARC
non-profit
AI & ML interests
MedARC aims to build a public community for contributions and develop foundation models for medical AI research.
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Whisper large 3 features, sequence average
#5 opened 13 days ago
by
ckadirt
Basic baseline models with scikit-learn LinearRegression or RidgeCV
#3 opened 22 days ago
by
cindyhfls
mihirneal
updated
a
dataset
about 1 month ago
stimuli_features_developer_kit
1
#1 opened about 2 months ago
by
ckadirt
mihirneal
published
a
dataset
about 2 months ago
griffin
authored
a
paper
3 months ago
jph00
authored
2
papers
3 months ago
IAMJB
authored
2
papers
4 months ago
conceptofmind
authored
a
paper
8 months ago
IAMJB
authored
5
papers
8 months ago
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
Paper
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2211.12737
•
Published
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2
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
Paper
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2305.01146
•
Published
•
1
ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data
Paper
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2308.11194
•
Published
Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts
Paper
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2309.07430
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Published
•
27
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
Paper
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2401.12208
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Published
•
22
conceptofmind
posted
an
update
12 months ago
Post
3331
Teraflop AI is excited to help support the Caselaw Access Project and Harvard Library Innovation Lab, in the release of over 6.6 million state and federal court decisions published throughout U.S. history. It is important to democratize fair access to data to the public, legal community, and researchers. This is a processed and cleaned version of the original CAP data.
During the digitization of these texts, there were erroneous OCR errors that occurred. We worked to post-process each of the texts for model training to fix encoding, normalization, repetition, redundancy, parsing, and formatting.
Teraflop AI’s data engine allows for the massively parallel processing of web-scale datasets into cleaned text form.
Link to the processed dataset: https://huggingface.co./datasets/TeraflopAI/Caselaw_Access_Project
The Caselaw Access Project dataset is licensed under the CC0 License.
We plan to release trillions of commercially licensed text tokens, images, audio, videos, and other datasets spanning numerous domains and modalities over the next months. If you are interested in contributing commercially licensed data be sure to reach out: https://twitter.com/EnricoShippole
Follow us for the next collaborative dataset releases: https://twitter.com/TeraflopAI
During the digitization of these texts, there were erroneous OCR errors that occurred. We worked to post-process each of the texts for model training to fix encoding, normalization, repetition, redundancy, parsing, and formatting.
Teraflop AI’s data engine allows for the massively parallel processing of web-scale datasets into cleaned text form.
Link to the processed dataset: https://huggingface.co./datasets/TeraflopAI/Caselaw_Access_Project
The Caselaw Access Project dataset is licensed under the CC0 License.
We plan to release trillions of commercially licensed text tokens, images, audio, videos, and other datasets spanning numerous domains and modalities over the next months. If you are interested in contributing commercially licensed data be sure to reach out: https://twitter.com/EnricoShippole
Follow us for the next collaborative dataset releases: https://twitter.com/TeraflopAI
tmabraham
authored
a
paper
12 months ago
reesekneeland
authored
a
paper
12 months ago