CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.
Multimodal fusion refiner networks
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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SubQuad reports near-subquadratic immune-repertoire analysis with fairness-aware clustering, but its central performance and coverage claims are not supported by reproducible artifacts.
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Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective
CmIR uses causal inference to separate invariant causal representations from spurious ones in multimodal data, improving generalization under distribution shifts and noise via invariance, mutual information, and reconstruction constraints.
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SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework
SubQuad reports near-subquadratic immune-repertoire analysis with fairness-aware clustering, but its central performance and coverage claims are not supported by reproducible artifacts.