TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
Gomez, Lukasz Kaiser, and Illia Polosukhin
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A new attention mechanism adds persistent homology and Euler-based topological structure to time-series models via validation-gated residuals, yielding RMSE reductions of 12.5-47.8% in paired tests on synthetic and real datasets when geometry is predictive.
ABSS ranks diffusion seeds by early cross-attention strength to prompt core tokens and retains only the top-k for full generation, yielding consistent gains in alignment and quality on Stable Diffusion variants.
citing papers explorer
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TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations
TrajGANR learns continuous neural representations of trajectories to enable fine-grained alignment with street-view images and locations in a joint multimodal self-supervised objective, outperforming prior geospatial MSSL methods on urban mobility and road tasks.
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Global and Local Topology-Aware Attention with Persistent Homology and Euler Biases for Time-Series Forecasting
A new attention mechanism adds persistent homology and Euler-based topological structure to time-series models via validation-gated residuals, yielding RMSE reductions of 12.5-47.8% in paired tests on synthetic and real datasets when geometry is predictive.
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Boosting Text-to-Image Diffusion Models via Core Token Attention-Based Seed Selection
ABSS ranks diffusion seeds by early cross-attention strength to prompt core tokens and retains only the top-k for full generation, yielding consistent gains in alignment and quality on Stable Diffusion variants.