8 papers
Explainable Human-in-the-Loop Segmentation via Critic Feedback Signals
Pouya Shaeri, Ryan T. Woo, Yasaman Mohammadpour +1
Segmentation models achieve high accuracy on benchmarks but often fail in real-world domains by relying on spurious correlations instead of true object boundaries. We propose a hum…
Sentiment and Social Signals in the Climate Crisis: A Survey on Analyzing Social Media Responses to Extreme Weather Events
Pouya Shaeri, Yasaman Mohammadpour, Alimohammad Beigi +1
Extreme weather events driven by climate change, such as wildfires, floods, and heatwaves, prompt significant public reactions on social media platforms. Analyzing the sentiment ex…
Tri-Accel: Curvature-Aware Precision-Adaptive and Memory-Elastic Optimization for Efficient GPU Usage
Mohsen Sheibanian, Pouya Shaeri, Alimohammad Beigi +2
Deep neural networks are increasingly bottlenecked by the cost of optimization, both in terms of GPU memory and compute time. Existing acceleration techniques, such as mixed precis…
MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory
Pouya Shaeri, Arash Karimi, Ariane Middel
Neural networks are often benchmarked using standard datasets such as MNIST, FashionMNIST, or other variants of MNIST, which, while accessible, are limited to generic classes such…
MID-L: Matrix-Interpolated Dropout Layer with Layer-wise Neuron Selection
Pouya Shaeri, Ariane Middel
Modern neural networks often activate all neurons for every input, leading to unnecessary computation and inefficiency. We introduce Matrix-Interpolated Dropout Layer (MID-L), a no…
A Multimodal Physics-Informed Neural Network Approach for Mean Radiant Temperature Modeling
Pouya Shaeri, Saud AlKhaled, Ariane Middel
Outdoor thermal comfort is a critical determinant of urban livability, particularly in hot desert climates where extreme heat poses challenges to public health, energy consumption,…