Predicting decision-making in the future: Human versus Machine

使用深度神经决策树与森林预测患者康复或死亡

Predicting Patient Recovery or Mortality Using Deep Neural Decision Tree and Forest

本研究面向COVID-19患者死亡风险预测，比较九种机器学习与深度学习模型，发现深度神经决策森林仅凭临床数据即可达到80%准确率且表现最佳。

Transfer Learning versus Multi-agent Learning regarding Distributed Decision-Making in Highway Traffic

Transportation and traffic are currently undergoing a rapid increase in terms of both scale and complexity. At the same time, an increasing share of traffic participants are being transformed into agents driven or supported by artificial intelligence resulting in mixed-intelligence traffic. This work explores the implications of distributed decision-making in mixed-intelligence traffic. The investigations are carried out on the basis of an online-simulated highway scenario, namely the MIT \emph{DeepTraffic} simulation. In the first step traffic agents are trained by means of a deep reinforceme…

注视信息引导的 Vision Transformers：在不确定性下预测驾驶决策

Gaze-Informed Vision Transformers: Predicting Driving Decisions Under Uncertainty

本文提出融合眼动注视信息的 ViT 与 FAX 损失，使模型注意力对齐人类注视，从而提升真实及虚拟现实高不确定性驾驶场景中的左右决策预测性能。

Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch

Foreground object segmentation is a critical step for many image analysis tasks. While automated methods can produce high-quality results, their failures disappoint users in need of practical solutions. We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods. The framework is based on a prediction module that estimates the quality of given algorithm-drawn segmentations. We demonstrate the value of the framework for two novel tasks r…

Predicting and visualizing psychological attributions with a deep neural network

Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions. Recent work has shown that it is possible to predict perception of memorability, trustworthiness, intelligence and other attributes in human face images. The most successful of these approaches require face images expertly annotated with key facial landmarks. We demonstrate a Convolutional Neural Network (CNN) model that is able to perform the same task without the need for landmark features, thereby greatly …

MAD-Guard：封闭式多模态取证任务中自回归生成与直接决策接口的受控研究

MAD-Guard: Controlled Study of Autoregressive Generation versus Direct Decision Interfaces for Closed Multimodal Forensic Tasks

提出MAD-Guard，在Qwen3-VL-8B上对比自回归生成与直接决策接口，发现直接头可大幅降低延迟和校准误差，并提升多任务归因性能。