Search Results for author:
Found 10 papers, 1 papers with code
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation
Boolean algebraic manipulation is at the core of logic synthesis in Electronic Design Automation (EDA) design flow.
GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement
In this paper, we present GATSPI, a novel GPU accelerated logic gate simulator that enables ultra-fast power estimation for industry sized ASIC designs with millions of gates.
Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks
The novel corona virus (Covid-19) has introduced significant challenges due to its rapid spreading nature through respiratory transmission.
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work.
Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association
In this paper, we propose a novel graph convolution network framework to learn features from a graph built with multi-source similarity information to predict circRNA-disease associations.
Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data
An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer.
Understanding 3D CNN Behavior for Alzheimer's Disease Diagnosis from Brain PET Scan
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis.
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery
We propose an ensembled model to predict CABG readmissions using pre-discharge perioperative data and machine learning survival analysis techniques.
Towards Robust Lung Segmentation in Chest Radiographs with Deep Learning
Automated segmentation of Lungs plays a crucial role in the computer-aided diagnosis of chest X-Ray (CXR) images.
An Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification
Alzheimer's Disease destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities.
