DeepKeep’s Post

Most existing adversarial attack methods generally rely on ideal assumptions, which is unreasonable for practical applications. In this letter, a practical #threat #model which utilizes #adversarial #attacks for anti-eavesdropping is proposed and a physical #intra-#class #universal #adversarial #perturbation (#IC-#UAP) crafting method against DL-based wireless signal classifiers is then presented. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP attack against the samples in a given class from a batch of samples in the class. Then, the authors develop a #physical #attack #algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. ---- Ruiqi Li, Hongshu Liao, Jiancheng An, Chau Yuen, Lu Gan More details can be found at this link: https://lnkd.in/gPSEkGnS

Most existing adversarial attack methods generally rely on ideal assumptions, which is unreasonable for practical applications. In this letter, a practical #threat #model which utilizes #adversarial #attacks for anti-eavesdropping is proposed and a physical #intra-#class #universal #adversarial #perturbation (#IC-#UAP) crafting method against DL-based wireless signal classifiers is then presented. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP attack against the samples in a given class from a batch of samples in the class. Then, the authors develop a #physical #attack #algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. ---- @Ruiqi Li, @Hongshu Liao, Jiancheng An, Chau Yuen, @Lu Gan More details can be found at this link: https://lnkd.in/gPSEkGnS

Low- Power Image Classification with the BrainChip Akida 1000 https://lnkd.in/enFaeY_Y

New tutorial on image classification using Ultralytics HUB 🔥🔥🔥 Image classification uses machine learning algorithms to categorize images, employing neural networks for pattern recognition from visual data. Ultralytics HUB, developed by the creators of YOLOv5 and YOLOv8, simplifies data visualization, AI model training, and real-world deployment. It supports image classification with deep learning algorithms and neural networks, playing a role in advancing AI. In this video, Nicolai Nielsen will guide you through the steps of image classification using HUB. What's Included 😍 ✅ Overview of image classification ✅ Finetuning the image classification model on custom data using Ultralytics HUB ✅ Custom dataset overview (brain tumor) ✅ Overview of Ultralytics HUB Cloud training process ✅ Training metrics, deployment, and export process insights Watch now 👇 https://lnkd.in/df8_8br9 #computervision #youtubetutorial #yolov8 #imageclassification #aiandml

This letter proposes a practical threat model that utilizes #adversarial #attacks for anti-eavesdropping and presents a physical #intra-#class #universal #adversarial #perturbation (#IC-#UAP) crafting method against DL-based wireless signal classifiers. First, an IC-UAP algorithm is proposed based on the threat model to craft a stronger UAP #attack #against the samples in a given class from a batch of samples in the class. Then, the authors develop a #physical #attack #algorithm based on the IC-UAP method, in which perturbations are optimized under random shifting to enhance the robustness of IC-UAPs against the unsynchronization between adversarial attacks and attacked signals. ----@Ruiqi Li, Jiancheng An, @Hongshu Liao,Chau Yuen, @Lu Gan More details can be found at this link: https://lnkd.in/gPrcuKGV

Check out: I, Cyborg: Using Co-Intelligence

Are you interested in a behind-the-scenes view of how AI works? Check out this 35-minute video with Samyam Rajbhandari from DeepSpeed! In it, he explains how DeepSpeed has played a significant role in advancing the training of large language models. GPUs do all the hard work for LLM’s, right? Well, they do need some help from tools like DeepSpeed. DeepSpeed allows researchers and practitioners to train larger models more efficiently, pushing the boundaries of what is possible in natural language processing and other domains. Join Samyam as he leads you through DeepSpeed's journey of addressing the challenges associated with LLM training and explains how they solved them. This video offers a piece of significant recent AI history that anybody working with LLMs or interested in the field won't want to miss. #AI #DeepSpeed #ailearning #aimodels https://lnkd.in/eZskJjRA

🌟 Excited to announce our latest blog post on "ONNXPruner: ONNX-Based General Model Pruning Adapter" (arXiv:2404.08016v1). We delve into the challenges faced in applying model pruning algorithms across platforms and introduce ONNXPruner, a versatile pruning adapter designed for ONNX format models. With its use of node association trees and a tree-level evaluation method, ONNXPruner demonstrates strong adaptability and increased efficacy across diverse deep learning frameworks and hardware platforms. Check out the full post for insights into advancing the practical application of model pruning: https://bit.ly/3Jk2c1u #MachineLearning #DeepLearning #ONNX #ModelPruning

What does particle physics and national security have in common? One of our leading AI and data scientists, ashutosh malgaonkar shares in his most recent article how particle experiments are some of the most important for the nation's security due to their relation to energy. This project exemplifies our dedication to pushing the boundaries of innovation and utilizing AI in novel ways to safeguard our nation's security interests. Together, we're shaping the future of security with intelligence, ingenuity, and advanced technology. #NationalSecurity #ParticlePhysics #AI #Innovation https://lnkd.in/efxSWPtt

Sparking Efficiency: A Neuro-Inspired Approach to Pruning Large Language Models Researchers have developed a groundbreaking technique called NEURO PRUNE that revolutionizes the way we optimize transformer-based Large Language Models (LLMs). This neuro-inspired topological sparse training algorithm induces structured sparsity at multiple levels, leading to high-performance, low-cost AI that could transform natural language processing. Check out the full paper to learn how NEURO PRUNE taps into the power of preferential attachment and redundancy elimination, mirroring characteristics observed in brain functional networks ETK Video overview: https://buff.ly/49tVEIj arXiv pre-print: https://buff.ly/3VQFx5Q #AI #MachineLearning #NLP #LanguageModels #Efficiency #ModelOptimization #NeuroinspiredAlgorithms #Research #Innovation