Machine Learning Model
Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.
Papers
Artificial intelligence system based on multi-value classification of fully connected neural network for construction management
Tetyana Honcharenko, Roman Akselrod, Andrii Shpakov, Oleksandr Khomenko
Prevent Car Accidents by Using AI
Sri Siddhartha Reddy Gudemupati, Yen Ling Chao, Lakshmi Praneetha Kotikalapudi, Ebrima Ceesay
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data
Fangcheng Fu, Huanran Xue, Yong Cheng, Yangyu Tao, Bin Cui
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
Patrick Hemmer, Sebastian Schellhammer, Michael Vössing, Johannes Jakubik, Gerhard Satzger
Mapping fNIRS to fMRI with Neural Data Augmentation and Machine Learning Models
Jihyun Hur, Jaeyeong Yang, Hoyoung Doh, Woo-Young Ahn
A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission
Shaina Raza
Specifying and Testing $k$-Safety Properties for Machine-Learning Models
Maria Christakis, Hasan Ferit Eniser, Jörg Hoffmann, Adish Singla, Valentin Wüstholz
GAN based Data Augmentation to Resolve Class Imbalance
Sairamvinay Vijayaraghavan, Terry Guan, Jason, Song
Science through Machine Learning: Quantification of Poststorm Thermospheric Cooling
Richard J. Licata, Piyush M. Mehta, Daniel R. Weimer, Douglas P. Drob, W. Kent Tobiska, Jean Yoshii
PD-DWI: Predicting response to neoadjuvant chemotherapy in invasive breast cancer with Physiologically-Decomposed Diffusion-Weighted MRI machine-learning model
Maya Gilad, Moti Freiman
A Human-Centric Take on Model Monitoring
Murtuza N Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
On Efficient Approximate Queries over Machine Learning Models
Dujian Ding, Sihem Amer-Yahia, Laks VS Lakshmanan
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui