abluva’s Post

Are you interested in learning how we can avoid the storage and training of an AI/ML model at the UE while retaining ML gains? Please look at our recently published article in IEEE TWC, co-authored with Luca R. and Mohamad Assaad. Focusing on massive MIMO CSI feedback, which has been proposed as one of the use cases in 3GPP Rel-18 AI/ML study items, we address the CSI feedback overhead reduction problem. Also, we focus on CSI feedback selection in a multiuser environment while enhancing CSI quality.

XFeat is poised to revolutionize image matching in low-resource environments. By balancing computational efficiency with robust performance, it offers a scalable solution for a wide range of computer vision applications. In this blog, Senior Engineer, Machine Learning Rajan Sharma shares insights on the published paper ‘XFeat: Accelerated Features for Lightweight Image Matching’ and what makes this a significant breakthrough in the world of Machine Learning. Check it out: https://lnkd.in/gA9jtB75 #TechSpeak #techblog #Xfeat #MachineLearning

Attending Mr. Sai Pradyumna's masterclass on "Understanding Machine Learning" offers a valuable opportunity to delve deeper into the intricacies of this transformative field under the guidance of a knowledgeable graduate student researcher from Georgia Institute of Technology, USA. #MachineLearning #Nxtwave

In this paper, the authors develop and validate a concept of #Deep #Neural #Network (#DNN)-based channel quality prediction between any two devices based on a low-complexity and easy-to-create #digital #twin. The digital twin serves for a generation of a large synthetic training dataset for channel quality prediction. As the low-complexity digital twin cannot capture all real-world aspects of the channels, they enhance the digital twin with real-world measured and artificially augmented inputs via transfer learning. The proposed concept is implemented and validated in software defined mobile network. They demonstrate that the proposed concept predicts the channel quality with a very high accuracy (mean average error of only 0.66 dB) in a real-world complex indoor scenario. Such error is sufficient for practical applications of the developed channel quality prediction concept and the error is few times lower than the error achievable by state-of-the-art solutions. ---- Zdenek Becvar, Jan Plachý, Pavel Mach, Anastas Nikolov, David Gesbert More details can be found at this link: https://lnkd.in/eAp6G-aj

Our paper, titled "Heterogeneous Federated Learning via Generative Model-Aided Knowledge Distillation in the Edge," has been accepted by IEEE IoT-J. This work is an extension of our previous work (Fed2KD: https://lnkd.in/gmpSxSij) on federated learning to mitigate the model and data heterogeneity. The early access version of this new article can be found here: https://lnkd.in/gE9B7buj Or here: https://lnkd.in/gXaQ7Fur

Researchers from the IMDEA Software Institute, Universidad Carlos III de Madrid, and NEC Laboratories Europe GmbH have unveiled a new potential framework that significantly boosts the efficiency and practicality of verifiable computing. Detailed in their latest paper, “Modular Sumcheck Proofs with Applications to Machine Learning and Image Processing,” and presented at the ACM CHI Conference on computer and communications security, this framework tackles the longstanding issues of scalability and modularity in cryptographic proof systems. Damien Robissout, a research programmer at IMDEA Software and co-author of the study, noted, “Our benchmarks indicate that our proofs are five times fasterto generate and ten times faster to verify than existing solutions, marking a significant advancement in the field.” Read our full article 👉 https://lnkd.in/gHJ7Qs27 #LabHorizons #ai #data #dataprocessing #datamanagement #imageprocessing #machinelearning #ml #artificialintelligence

Excited to share our latest work, "Distributed ℓ0 Sparse Aggregative Optimization," now published with IEEE! This paper presents a fully distributed algorithm for sparse convex optimization in aggregative settings, allowing networked agents to solve complex problems collaboratively. By combining an Augmented Lagrangian approach with Projected Aggregative Tracking and Block Coordinated Descent, it demonstrates promising results in distributed machine learning applications. Thanks to the co-authors for their collaboration on this paper! 🔗 Read more: https://lnkd.in/d7kWzrvf #DistributedOptimization #SparseOptimization #IEEECASE2024

Dive into the ground breaking strategies revealed by the University of Surrey, including accelerated data processing, streamlined research focus, eco-friendly GPU utilization, and effective data management solutions, all on WEKA. Don't miss out on these invaluable insights. Watch our on-demand webinar. https://lnkd.in/eqPxSa5X #AIResearch #Innovation #UniversityofSurrey #dataprocessing #GPU #datamanagement

🚨ECCV 2024 Paper Alert 🚨 ➡️Paper Title: ZIGMA: A DiT-style Zigzag Mamba Diffusion Model 🌟Few pointers from the paper 🎯The diffusion model has long been plagued by scalability and quadratic complexity issues, especially within transformer-based structures. In this study, Authors aimed to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation. 🎯 Firstly, They identified a critical oversight in most current Mamba-based vision methods, namely the lack of consideration for spatial continuity in the scan scheme of Mamba. 🎯Secondly, building upon this insight, they introduced “Zigzag Mamba”, a simple, plug-and-play, minimal-parameter burden, DiT style solution, which outperforms Mamba-based baselines and demonstrates improved speed and memory utilization compared to transformer-based baselines. 🎯 Lastly, they integrated Zigzag Mamba with the Stochastic Interpolant framework to investigate the scalability of the model on large-resolution visual datasets, such as FacesHQ 1024 × 1024 and UCF101, MultiModal-CelebA-HQ, and MS COCO 256 × 256. 🏢Organization: CompVis group at Ludwig-Maximilians-Universität München , Munich Center for Machine Learning 🧙Paper Authors: Tao HU, Stefan Baumann, Ming Gui, Olga Grebenkova, Pingchuan Ma , Johannes Fischer , Björn Ommer 1️⃣Read the Full Paper here: https://lnkd.in/gzbKYzix 2️⃣Project Page: https://taohu.me/zigma/ 3️⃣Code: https://lnkd.in/gTDz8w_r 4️⃣Presentation: https://lnkd.in/gz9YmfUh Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements. #ECCV2024 #diffusionmodel

🎉 Excited to share our latest paper, "𝗗𝘆𝗘𝗱𝗴𝗲𝗚𝗔𝗧: 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗘𝗱𝗴𝗲 𝘃𝗶𝗮 𝗚𝗿𝗮𝗽𝗵 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗘𝗮𝗿𝗹𝘆-𝗦𝘁𝗮𝗴𝗲 𝗙𝗮𝘂𝗹𝘁 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗜𝗜𝗼𝗧 𝗦𝘆𝘀𝘁𝗲𝗺𝘀", authored by Mengjie Zhao and Olga Fink, is now accepted for publication in 𝗜𝗘𝗘𝗘 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗼𝗳 𝗧𝗵𝗶𝗻𝗴𝘀 𝗝𝗼𝘂𝗿𝗻𝗮𝗹! ⚠ Unforeseen faults in complex industrial systems, like chemical process plants, can cause significant production disruptions, safety breaches, accelerated equipment wear, and inconsistent product quality. #EarlyFaultDetection is crucial, but it's challenging because faults often induce only subtle changes in the relationships between #IIoT sensors. These changes remain hidden within the complex spatial-temporal dependencies of sensor data. While recent graph neural network (#GNN) methods model these dependencies, they lack the sensitivity to detect these crucial early-stage changes. Our research introduces a novel graph-based approach targeting these relationship changes, enabling superior early fault detection in IIoT systems. 🚀 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝗶𝗻𝗴 𝗗𝘆𝗘𝗱𝗴𝗲𝗚𝗔𝗧: Addressing these critical limitations, our latest research presents 𝗗𝘆𝗘𝗱𝗴𝗲𝗚𝗔𝗧 (𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗘𝗱𝗴𝗲 𝘃𝗶𝗮 𝗚𝗿𝗮𝗽𝗵 𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻). Our main contributions are: 1. 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 - Instead of focusing only on node dynamics (𝘪.𝘦., 𝘵𝘩𝘦 𝘪𝘯𝘵𝘳𝘪𝘯𝘴𝘪𝘤 𝘥𝘺𝘯𝘢𝘮𝘪𝘤𝘴 𝘰𝘧 𝘢 𝘴𝘦𝘯𝘴𝘰𝘳), DyedgeGAT also models edge dynamics (𝘪.𝘦., 𝘵𝘩𝘦 𝘦𝘷𝘰𝘭𝘷𝘪𝘯𝘨 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘴𝘦𝘯𝘴𝘰𝘳𝘴) 2. 𝗜𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗶𝗻𝗴 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 - By modeling control variable and external factors as operating condition context in node dynamics, DyEdgeGAT can distinguish between true faults and normal system variations under different conditions, greatly reducing false alarms. 🌐 𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝗢𝘂𝗿 𝗪𝗼𝗿𝗸: We're excited to share our research findings, code, and data with the community. We invite you to explore, contribute, and collaborate: https://lnkd.in/eV-gD4CT. We're keen to hear your insights, feedback, or potential applications you envision with DyEdgeGAT. Let's push the boundaries of what's possible in IIoT together! #GraphLearning #GraphNeuralNetworks #IIoT #MultivariateTimeSeries #UnsupervisedFaultDetection