Decision Making
1979 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
Libraries
Use these libraries to find Decision Making models and implementationsLatest papers with no code
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
Our method is inspired by our observation that what we call disturbance instructions significantly exacerbate hallucinations in multimodal fusion modules.
Manipulating Neural Path Planners via Slight Perturbations
In this paper, we propose a novel approach to specify and inject a range of hidden malicious behaviors, known as backdoors, into neural path planners.
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving.
Modeling uncertainty for Gaussian Splatting
We present Stochastic Gaussian Splatting (SGS): the first framework for uncertainty estimation using Gaussian Splatting (GS).
Uncertainty-Aware SAR ATR: Defending Against Adversarial Attacks via Bayesian Neural Networks
Adversarial attacks have demonstrated the vulnerability of Machine Learning (ML) image classifiers in Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems.
Modeling Sustainable City Trips: Integrating CO2 Emissions, Popularity, and Seasonality into Tourism Recommender Systems
Our paper introduces a novel approach for assigning a sustainability indicator (SF index) for city trips accessible from the users' starting point, integrating Co2e analysis, destination popularity, and seasonal demand.
Optimization-based Prompt Injection Attack to LLM-as-a-Judge
LLM-as-a-Judge is a novel solution that can assess textual information with large language models (LLMs).
Addressing Myopic Constrained POMDP Planning with Recursive Dual Ascent
Lagrangian-guided Monte Carlo tree search with global dual ascent has been applied to solve large constrained partially observable Markov decision processes (CPOMDPs) online.
Deep Support Vectors
While the success of deep learning is commonly attributed to its theoretical equivalence with Support Vector Machines (SVM), the practical implications of this relationship have not been thoroughly explored.
Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion
Integrating components from convolutional neural networks and state space models in medical image segmentation presents a compelling approach to enhance accuracy and efficiency.