On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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4 results for “Coma”

Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unitq-bio.QM

Study developed phenotypes for ICU patients' brain dysfunction states.

problem Underdiagnosis of acute brain dysfunction in ICU patients.

method Created algorithms to quantify and cluster brain dysfunction states.

result Developed three phenotypes of ICU patients' brain dysfunction states.

ICU Brain Dysfunction Phenotyping Delirium Coma

2023-03-09abs ↗pdf ↗

Conformal online model aggregationstat.ML

COMA combines prediction sets from multiple models for online, adaptive prediction.

problem Combining multiple prediction models with uncertainty guarantees.

method Online model aggregation using weighted voting of conformal prediction sets.

result COMA retains coverage guarantees under negative correlation assumptions.

conformal prediction model aggregation online learning uncertainty quantification distributed systems

2024-03-22abs ↗pdf ↗

Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learningcs.AI

New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.

problem Challenges in cooperative multi-agent reinforcement learning, especially credit assignment and large action spaces.

method Decomposed Soft Actor-Critic (mSAC) method with Q network architecture, discrete probabilistic policy, and counterfactual advantage function.

result Significantly outperforms policy-based approach COMA and achieves competitive results with SOTA value-based approach Qmix.

Reinforcement Learning Multi-Agent Systems Soft Actor-Critic Decomposition StarCraft II

2021-04-14abs ↗pdf ↗

An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event Outcomesstat.ML

This monograph introduces deep learning models for predicting time-to-event outcomes.

problem Predicting critical events and their timing from time series data.

method Neural networks and deep learning models for survival analysis.

result Improved accuracy in predicting time-to-event outcomes using deep learning.

deep learning survival analysis time-to-event neural networks neural differential equations

2024-10-01abs ↗pdf ↗