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Found 7 papers, 6 papers with code
EnergyVis: Interactively Tracking and Exploring Energy Consumption for ML Models
The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language.
MalNet: A Large-Scale Image Database of Malicious Software
Computer vision is playing an increasingly important role in automated malware detection with the rise of the image-based binary representation.
A Large-Scale Database for Graph Representation Learning
With the rapid emergence of graph representation learning, the construction of new large-scale datasets is necessary to distinguish model capabilities and accurately assess the strengths and weaknesses of each technique.
ELF: An Early-Exiting Framework for Long-Tailed Classification
The natural world often follows a long-tailed data distribution where only a few classes account for most of the examples.
Ranked #40 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Evaluating Graph Vulnerability and Robustness using TIGER
By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field.
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
UnMask detects such attacks and defends the model by rectifying the misclassification, re-classifying the image based on its robust features.
REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference.
