Search Results for author:
Found 9 papers, 3 papers with code
Scaling Model Checking for DNN Analysis via State-Space Reduction and Input Segmentation (Extended Version)
This in turn allows the analysis of NN safety properties using the new framework, in addition to all the NN properties already included with FANNet.
UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks
In contrast, UnbiasedNets provides a notable improvement over existing works, while even reducing the robustness bias significantly in some cases, as observed by comparing the NNs trained on the diversified and original datasets.
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables
We are successful in identifying Pareto-optimal designs, which can reduce the storage overhead of the DNN by ~30MB for a quality loss of less than 0. 5%.
Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks
Continual learning is essential for all real-world applications, as frozen pre-trained models cannot effectively deal with non-stationary data distributions.
MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices
To reduce the overhead of data acquisition, we propose a single power-port current acquisition block using current sensors in time-division multiplexing, which increases accuracy while incurring reduced area overhead.
m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks
Overall, the data consists of 307 images, each of which is annotated for the organs and different surgical instruments present in the scene.
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
With a constant improvement in the network architectures and training methodologies, Neural Networks (NNs) are increasingly being deployed in real-world Machine Learning systems.
SIMCom: Statistical Sniffing of Inter-Module Communications for Run-time Hardware Trojan Detection
We present a run-time methodology for HT detection that employs a multi-parameter statistical traffic modeling of the communication channel in a given System-on-Chip (SoC), named as SIMCom.
Towards Formal Fault Tree Analysis using Theorem Proving
Fault Tree Analysis (FTA) is a dependability analysis technique that has been widely used to predict reliability, availability and safety of many complex engineering systems.
