ECCS-EPSRC: Neural Joint Source-Channel Coding: The Interplay Between Theory and Practice — NSF Award to Texas A&M Engineering Exp
Machine learning (ML) techniques are demonstrating record-breaking performances on many tasks such as speech recognition, image recognition, composing new documents, and even solving mathematics Olympiad problems. Encouraged by these remarkable results, machine learning-based techniques are now being researched to desi
| Award title | ECCS-EPSRC: Neural Joint Source-Channel Coding: The Interplay Between Theory and Practice |
|---|---|
| Award ID | 2433631 |
| Awardee | Texas A&M Engineering Experiment Station |
| City | COLLEGE STATION |
| State | TX |
| Amount obligated | $450,000 |
| Principal investigator | Krishna Narayanan |
| Program | CSCS: Circuits and Systems for |
| Start date | 05/01/2025 |
| Abstract | Machine learning (ML) techniques are demonstrating record-breaking performances on many tasks such as speech recognition, image recognition, composing new documents, and even solving mathematics Olympiad problems. Encouraged by these remarkable results, machine learning-based techniques are now being researched to design better communication systems. An important communication problem is the transmission of sources such as speech, images, and video over noisy communication networks. To efficient |
| Source | NSF Awards |
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