Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.
Accelerate data mining and deep learning applications via Intel onapi. Provide sample code and examples in SJSU CMPE255 Data Mining class (https://catalog.sjsu.edu/preview_course_nopop.php?catoid=12&coid=58423) to demonstrate the importance of acceleration and use Intel OneAPI as one technological e
This is a program i designed to plot the Mandelbrot set, it can do this either with 1 thread, all threads or utilising SYCL via intels DPC++ compiler included in OneAPI toolkit to use GPU acceleration.
XTASK enables extreme fine-grained parallelism across modern many-core architectures with hundreds of cores by implementing a novel lock-less multiple producer multiple consumer, out-of-order queuing mechanism for managing parallel tasks.
A ROS package that brings intel's oneAPI to the ROS framework.
This repository provides an example of vector summation on ROS using the Intel oneAPI framework. With oneAPI, the summation operation can be run parallelly on CPUs, GPUs and even Intel FPGA devices.
Gavin AI is a project, created by Scot_Survivor (Joshua Shiells) & ShmarvDogg, which aims to have Englsih human like conversations through the use of AI and ML. Gavin works on the Transformer architecture however, Performer & FNet architectures are being investigated for better scaling.
we propose a multilingual ensemble-based model that can identify offensive content targeted against an individual (or group) in low resource Dravidian language. Our model is able to handle code-mixed data as well as instances where the script used is mixed (for instance, Tamil and Latin).
Leveraging Generative Adversarial Networks to create self-driving data at scale is crucial. By emphasizing a focus on DCGANs, I’m focusing on creating high-quality self-driving images that can be used to train and improve the performance of computer vision models.
Used Convolutional Neural Networks + Deep Learning algorithms to create a learned agent that can perform binary classification of malignant vs benign skin cancer.
In the age of AI, algorithms must efficiently cope with vast data sets. We propose a performance-portable implementation of Locality-Sensitive Hashing (LSH), an approximate k-nearest neighbors algorithm, using different SYCL implementations—ComputeCpp, hipSYCL, DPC++—supporting multiple GPUs.
