DBLP is a training-phase-aware bounded-loss transport protocol that reduces end-to-end distributed ML training time by 24.4% on average (up to 33.9%) and achieves up to 5.88x communication speedup during microbursts while maintaining comparable test accuracy.
Deepseek-vl: Towards real-world vision-language understanding,
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ChatENV fine-tunes Qwen-2.5-VL on a 177k-image dataset of temporal satellite pairs with sensor metadata to support interactive temporal and what-if reasoning for environmental monitoring.
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DBLP: Phase-Aware Bounded-Loss Transport for Burst-Resilient Distributed ML Training
DBLP is a training-phase-aware bounded-loss transport protocol that reduces end-to-end distributed ML training time by 24.4% on average (up to 33.9%) and achieves up to 5.88x communication speedup during microbursts while maintaining comparable test accuracy.
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ChatENV: An Interactive Vision-Language Model for Sensor-Guided Environmental Monitoring and Scenario Simulation
ChatENV fine-tunes Qwen-2.5-VL on a 177k-image dataset of temporal satellite pairs with sensor metadata to support interactive temporal and what-if reasoning for environmental monitoring.