Decision Making
2034 papers with code • 1 benchmarks • 38 datasets
Decision Making is a complex task that involves analyzing data (of different level of abstraction) from disparate sources and with different levels of certainty, merging the information by weighing in on some data source more than other, and arriving at a conclusion by exploring all possible alternatives.
Source: Complex Events Recognition under Uncertainty in a Sensor Network
Benchmarks
These leaderboards are used to track progress in Decision Making
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Libraries
Use these libraries to find Decision Making models and implementations
D4RL
Charades-STA
FairFace
VizDoom
TORCS
LogiQA
ShARC
GazeFollow
Latest papers
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
vdblm/ExPerior
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We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information.
Enhancing Decision Analysis with a Large Language Model: pyDecision a Comprehensive Library of MCDA Methods in Python
In addition to these features, pyDecision has integrated ChatGPT, an advanced Large Language Model, where decision-makers can use ChatGPT to discuss and compare the outcomes of different methods, providing a more interactive and intuitive understanding of the solutions.
Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus
We illustrate with our two use cases their advantages: they generate step-by-step pathways that are self-explanatory; and their correctness is competitive when compared to state-of-the-art approaches.
GloSoFarID: Global multispectral dataset for Solar Farm IDentification in satellite imagery
Solar Photovoltaic (PV) technology is increasingly recognized as a pivotal solution in the global pursuit of clean and renewable energy.
Percentile Criterion Optimization in Offline Reinforcement Learning
The percentile criterion is approximately solved by constructing an \emph{ambiguity set} that contains the true model with high probability and optimizing the policy for the worst model in the set.
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
Large language models (LLMs) have fueled many intelligent agent tasks, such as web navigation -- but most existing agents perform far from satisfying in real-world webpages due to three factors: (1) the versatility of actions on webpages, (2) HTML text exceeding model processing capacity, and (3) the complexity of decision-making due to the open-domain nature of web.
Predictive Analytics of Varieties of Potatoes
We explore the application of machine learning algorithms to predict the suitability of Russet potato clones for advancement in breeding trials.
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
danielsun94/conversational_diagnosis
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The advancement of medical artificial intelligence (AI) has set the stage for the realization of conversational diagnosis, where AI systems mimic human doctors by engaging in dialogue with patients to deduce diagnoses.
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation
Deep Neural Networks (DNNs) are widely used for visual classification tasks, but their complex computation process and black-box nature hinder decision transparency and interpretability.
Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields
In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code.
