MAEPose is a masked autoencoder that learns spatiotemporal representations from unlabeled mmWave radar videos to estimate human poses, outperforming baselines by up to 22.1% in MPJPE.
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Trains a 42M-parameter Spanish cybersecurity LLM from scratch with curriculum phases and achieves 0.23 tool-selection accuracy after SFT mixture rebalancing to 1:21 tool-use ratio.
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MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video
MAEPose is a masked autoencoder that learns spatiotemporal representations from unlabeled mmWave radar videos to estimate human poses, outperforming baselines by up to 22.1% in MPJPE.
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VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use
Trains a 42M-parameter Spanish cybersecurity LLM from scratch with curriculum phases and achieves 0.23 tool-selection accuracy after SFT mixture rebalancing to 1:21 tool-use ratio.