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Found 8 papers, 0 papers with code
Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle
Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone.
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.
Ranked #5 on
MMR total
on MRR-Benchmark
(using extra training data)
Disease Progression Modeling Workbench 360
In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
WNTRAC: AI Assisted Tracking of Non-pharmaceutical Interventions Implemented Worldwide for COVID-19
We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6, 000 NPIs implemented worldwide since the start of the pandemic.
A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records
Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes.
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society.
Multi-Objective Reinforcement Learning
reinforcement-learning
Effect of secular trend in drug effectiveness study in real world data
Second, we included a covariate in the model for the number of days between the date of approval of the new drug and the treatment (new or standard) start date.
