Explore the cutting-edge of artificial intelligence (AI), machine learning, optimization, signal processing, wireless communications, networking, and broader networked systems. Join us as we dive deep into frontier research papers, uncover groundbreaking innovations, and discuss the possibilities shaping the future of technology and science.
Deep Dive into Networked AI
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10 episodes
Updated Daily
Accepts GuestsHas SponsorsLocation 🇨🇳
48
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
Quality58
Social0
YouTube93
Engagement0
Podcast Overview
Explore the cutting-edge of artificial intelligence (AI), machine learning, optimization, signal processing, wireless communications, networking, and broader networked systems. Join us as we dive deep into frontier research papers, uncover groundbreaking innovations, and discuss the possibilities shaping the future of technology and science.
Language
🇺🇲
Publishing Since
12/13/2024
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48
Podcast Authority
BetaFairBased on show quality, social media presence, reviews, charts, and more
Quality58
Social0
YouTube93
Engagement0
8
Excellent Areas
0
Good Performance
11
Growth Opportunities
excellent
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Every 6 days
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poor
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Recent Episodes
February 10, 2025
[Deep Dive] AirGNN: Graph Neural Networks over the air for Wireless Networks
<p>In this episode, we explore the nuances and key considerations of implementing graph neural networks as decentralized applications in wireless networks, such as source localization, multi-robot flocking, and wireless channel management — a core theme of this podcast, especially in this season. This discussion is based on a journal paper published in IEEE Transactions on Signal Processing, authored by Zhan Gao and Deniz Gündüz.</p><p><br></p><p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p><p>🎧 Read the paper here: [IEEE TSP](https://ieeexplore.ieee.org/document/9042352)</p><p>📷 Cover Image Source: imagine.art, Microsoft Designer</p><p>🎵 BGM: Artlist.io</p><p>🛠️ Credits: NotebookLM by Google</p><p><br></p>
January 31, 2025
[Deep Dive] GLANCE: Graph-based Learnable Digital Twin for Wireless Networks
<p>In this episode, we dive into the applications of graph neural networks as a learnable digital twin of network simulators, which can accelerate network optimization by its fast and differentiable prediction of networking key performance indicators (KPIs). This episode is based on a preprient authored by Boning Li, et al. </p> <p><br></p> <p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p> <p>🎧 Read the paper here: [<a href="https://arxiv.org/pdf/2408.09040" target="_blank" rel="noopener noreferer">arXiv</a>]</p> <p>📷 Cover Image Source: imagine.art, Microsoft Designer</p> <p>🎵 BGM: Artlist.io</p> <p>🛠️ Credits: NotebookLM by Google</p>
January 24, 2025
[Deep Dive] Opportunities and Challenges of Graph Neural Networks in Electrical Engineering
<p>In this episode, we dive into a survey paper of the applications of graph neural networks in electrical engineering. This episode is based on our recent publication in Nature Review Electrical Engineering. </p> <p><br></p> <p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p> <p>🎧 Read the paper here: [<a href="https://rdcu.be/dP0LU" target="_blank" rel="ugc noopener noreferrer">Online</a>]</p> <p>📷 Cover Image Source: imagine.art, Microsoft Designer</p> <p>🎵 BGM: Artlist.io</p> <p>🛠️ Credits: NotebookLM by Google</p>
10 total episodes available
Deep-dive analytics for Deep Dive into Networked AI
Frequently asked questions
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- What is Deep Dive into Networked AI?
- How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 6 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
No, this podcast does not typically feature guests.
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