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Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model
Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance.
Quick and Reliable LoRa Physical-layer Data Aggregation through Multi-Packet Reception
Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5. 3$\times$ in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice.
Evaluating the Practicality of Learned Image Compression
On CPU, the latency of our implementation is comparable with JPEG XL.
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