CONSIGN applies conformal prediction to segmentation by incorporating spatial structure through decomposition, producing tighter and more interpretable uncertainty estimates with error guarantees.
What uncertainties do we need in bayesian deep learning for computer vision?Advances in Neural Information Processing Systems, 30
2 Pith papers cite this work. Polarity classification is still indexing.
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Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.
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CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition
CONSIGN applies conformal prediction to segmentation by incorporating spatial structure through decomposition, producing tighter and more interpretable uncertainty estimates with error guarantees.
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Temporal Reasoning Is Not the Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA
Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.