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SubQuad tackles the computational bottleneck of adaptive immune repertoire analysis with a near-quadratic-free approach that also corrects for dataset imbalances, enabling more equitable and scalable biomarker discovery.
Achieve practical privacy-utility trade-offs in multimodal sentiment analysis with Missing-by-Design (MBD), a framework that surgically removes modality-specific information without full retraining.
Achieve robust multimodal sentiment analysis, even with noisy or incomplete data, by explicitly modeling hierarchical relationships in hyperbolic space.
Make your multimodal models immune to missing modalities with a training framework that selectively collapses modality information, boosting robustness without sacrificing performance.
Generate expansive, high-fidelity 3D environments with Gaussian Splatting using significantly less data by intelligently sampling viewpoints and hallucinating details with diffusion models.
Achieve a better trade-off between search precision and carbon footprint with GaiaFlow, a framework that uses semantic-guided diffusion tuning to make neural search systems more sustainable.
