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reacted to aaditya's post with ā¤ļø about 1 month ago
Last Week in Medical AI: Top Research Papers/Models šŸ”„ (November 2 -November 9, 2024) šŸ… Medical AI Paper of the Week: Exploring Large Language Models for Specialist-level Oncology Care Medical LLM & Other Models: - GSCo: Generalist-Specialist AI Collaboration - PediatricsGPT: Chinese Pediatric Assistant - MEG: Knowledge-Enhanced Medical QA - AutoProteinEngine: Multimodal Protein LLM Frameworks and Methodologies: - BrainSegFounder: 3D Neuroimage Analysis - PASSION: Sub-Saharan Dermatology Dataset - SAM for Lung X-ray Segmentation - Label Critic: Data-First Approach - Medprompt Runtime Strategies Medical LLM Applications: - CataractBot: Patient Support System - CheX-GPT: X-ray Report Enhancement - CardioAI: Cancer Cardiotoxicity Monitor - HealthQ: Healthcare Conversation Chain - PRObot: Diabetic Retinopathy Assistant Medical LLMs & Benchmarks: - MediQ: Clinical Reasoning Benchmark - Touchstone: Segmentation Evaluation - Medical LLM Adaptation Progress - Fine-Tuning Medical QA Strategies AI in Healthcare Ethics: - Healthcare Robotics with LLMs - XAI in Clinical Practice - Precision Rehabilitation Framework - Multimodal AI Challenges Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well! - Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090 - YouTube: https://youtu.be/ad0uTnYuTo8 - Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN
reacted to aaditya's post with šŸš€ about 1 month ago
Last Week in Medical AI: Top Research Papers/Models šŸ”„ (November 2 -November 9, 2024) šŸ… Medical AI Paper of the Week: Exploring Large Language Models for Specialist-level Oncology Care Medical LLM & Other Models: - GSCo: Generalist-Specialist AI Collaboration - PediatricsGPT: Chinese Pediatric Assistant - MEG: Knowledge-Enhanced Medical QA - AutoProteinEngine: Multimodal Protein LLM Frameworks and Methodologies: - BrainSegFounder: 3D Neuroimage Analysis - PASSION: Sub-Saharan Dermatology Dataset - SAM for Lung X-ray Segmentation - Label Critic: Data-First Approach - Medprompt Runtime Strategies Medical LLM Applications: - CataractBot: Patient Support System - CheX-GPT: X-ray Report Enhancement - CardioAI: Cancer Cardiotoxicity Monitor - HealthQ: Healthcare Conversation Chain - PRObot: Diabetic Retinopathy Assistant Medical LLMs & Benchmarks: - MediQ: Clinical Reasoning Benchmark - Touchstone: Segmentation Evaluation - Medical LLM Adaptation Progress - Fine-Tuning Medical QA Strategies AI in Healthcare Ethics: - Healthcare Robotics with LLMs - XAI in Clinical Practice - Precision Rehabilitation Framework - Multimodal AI Challenges Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well! - Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090 - YouTube: https://youtu.be/ad0uTnYuTo8 - Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN
reacted to aaditya's post with šŸ¤— about 1 month ago
Last Week in Medical AI: Top Research Papers/Models šŸ”„ (November 2 -November 9, 2024) šŸ… Medical AI Paper of the Week: Exploring Large Language Models for Specialist-level Oncology Care Medical LLM & Other Models: - GSCo: Generalist-Specialist AI Collaboration - PediatricsGPT: Chinese Pediatric Assistant - MEG: Knowledge-Enhanced Medical QA - AutoProteinEngine: Multimodal Protein LLM Frameworks and Methodologies: - BrainSegFounder: 3D Neuroimage Analysis - PASSION: Sub-Saharan Dermatology Dataset - SAM for Lung X-ray Segmentation - Label Critic: Data-First Approach - Medprompt Runtime Strategies Medical LLM Applications: - CataractBot: Patient Support System - CheX-GPT: X-ray Report Enhancement - CardioAI: Cancer Cardiotoxicity Monitor - HealthQ: Healthcare Conversation Chain - PRObot: Diabetic Retinopathy Assistant Medical LLMs & Benchmarks: - MediQ: Clinical Reasoning Benchmark - Touchstone: Segmentation Evaluation - Medical LLM Adaptation Progress - Fine-Tuning Medical QA Strategies AI in Healthcare Ethics: - Healthcare Robotics with LLMs - XAI in Clinical Practice - Precision Rehabilitation Framework - Multimodal AI Challenges Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well! - Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090 - YouTube: https://youtu.be/ad0uTnYuTo8 - Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN
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reacted to aaditya's post with ā¤ļøšŸš€šŸ¤— about 1 month ago
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3362
Last Week in Medical AI: Top Research Papers/Models šŸ”„
(November 2 -November 9, 2024)

šŸ… Medical AI Paper of the Week:
Exploring Large Language Models for Specialist-level Oncology Care

Medical LLM & Other Models:
- GSCo: Generalist-Specialist AI Collaboration
- PediatricsGPT: Chinese Pediatric Assistant
- MEG: Knowledge-Enhanced Medical QA
- AutoProteinEngine: Multimodal Protein LLM

Frameworks and Methodologies:
- BrainSegFounder: 3D Neuroimage Analysis
- PASSION: Sub-Saharan Dermatology Dataset
- SAM for Lung X-ray Segmentation
- Label Critic: Data-First Approach
- Medprompt Runtime Strategies

Medical LLM Applications:
- CataractBot: Patient Support System
- CheX-GPT: X-ray Report Enhancement
- CardioAI: Cancer Cardiotoxicity Monitor
- HealthQ: Healthcare Conversation Chain
- PRObot: Diabetic Retinopathy Assistant

Medical LLMs & Benchmarks:
- MediQ: Clinical Reasoning Benchmark
- Touchstone: Segmentation Evaluation
- Medical LLM Adaptation Progress
- Fine-Tuning Medical QA Strategies

AI in Healthcare Ethics:
- Healthcare Robotics with LLMs
- XAI in Clinical Practice
- Precision Rehabilitation Framework
- Multimodal AI Challenges

Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!

- Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090
- YouTube: https://youtu.be/ad0uTnYuTo8
- Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN
reacted to m-ric's post with ā¤ļø 2 months ago
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1985
šŸŒŸšŸŒŽ Cohere releases Aya 8B & 32B: SOTA multilingual models for 23 languages !

How did they manage to beat top contenders while also adding 23 languages?

šŸ”„ š—§š—暝—®š—¶š—» š—¼š—» š˜€š˜†š—»š˜š—µš—²š˜š—¶š—° š—±š—®š˜š—®:
ā€¢ Synthetic data has been said to cause model-collapse after too much training
ā€¢ Cohere has introduced "data arbitrage" to prevent this by strategically sampling from a pool of several teacher models instead of one single teacher
ā€¢ First train a model pool for each different groups of languages, and employ an internal Reward Model named "Arbiter" to evaluate and select the optimal generation. Then only the best generation is kept as the final completion for each prompt
āž”ļø This process is particularly effective for multilingual setting, where no single teacher model performs in all languages : here "Multilingual Arbitrage" singlehandedly improves win rates of the 8B model vs Gemma-2-9B by 10 points!

šŸ§© š—Øš˜€š—² š—ŗš—¼š—±š—²š—¹ š—ŗš—²š—暝—“š—¶š—»š—“: Rather than struggling to find the right mix of data in training a single model for multilingual use, just train language specific models then merge them!
ā€¢ Maximize diversity between merged checkpoints by training each on different language families.
ā€¢ Experimented fancy techniques (SLERP, TIES, DARE-TIES) but found out weighted averaging to be the most consistent!
āž”ļø Merging had 3x more gains at high 35B scale vs the 8B scale - consistent with literature findings that merging is more effective at scale

āš”ļø š—šš—暝—²š—®š˜ š—½š—²š—暝—³š—¼š—暝—ŗš—®š—»š—°š—²: Automatic evaluations on Arena-Hard-Auto dataset:
āž”ļø Aya Expanse 8B beats models from its weight class such as Gemma 2 9B, Llama 3.1 8B, and the recent Ministral 8B, with win rates ranging from 60.4% to 70.6%
āž”ļø Aya Expanse 32B outperforms Gemma 2 27B, Mistral 8x22B, and Llama 3.1 70B (2x its size)
ā€¢ āš ļø But this performance eval comes from only one benchmark! Let's wait for Open LLM leaderboard evals;

šŸ”’ CC by NC license

Blog post here: https://huggingface.co/blog/aya-expanse
reacted to jjokah's post with šŸ‘ 4 months ago
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1865
šŸ”— Neural Network ā€€(1 Byte explainer for everybody)

Just like our brain, a Neural Network is made up of interconnected "neurons". These neurons work together by learning from (input) data and getting better at tasks (in the hidden layer) to give (output) predictions or decisions.
New activity in Tevatron/dse-phi3-docmatix-v1 5 months ago

Update README.md

#2 opened 5 months ago by
mesut07