Jingge Wang
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126
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Vision×1ML×1
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2026
CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
CORE-MTL proposes a representation-centric framework that uses causal orthogonal representations to disentangle task-relevant structure from nuisance variation in multi-task learning, achieving superior generalization.
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