Wireless time-series generation is useful for emerging applications that will drive the adoption and integration of machine learning tasks within next-generation networks, such as waveform classification in shared spectrum bands and interpretation of the physical world through integrated sensing and communications. Unl...
Yi-Yang Li, Sai Shankar Narasimhan, Priam Alataris et al.· 0 citations
Preliminary results from a geo-distributed LLM training prototype that treats networking constraints as first-order design concerns are presented, motivating adaptive networking support for synchronization, compression, placement, telemetry, and recovery in geo-distributed LLM training.
Zi-Yue Luo, Jiaxuan Cai, Cedric Le Denmat et al.· Conference on Applications,...· 0 citations
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