Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
Advances in Neural Information Processing Systems , volume=
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
LTE-ODE repurposes local truncation error as an unsupervised dynamic attention mask that preserves continuous Neural ODE evolution in stable regions while triggering discrete compensation only at anomaly points in large-scale traffic data.
A deep kernel learning architecture with transformer feature extraction on clinical-BERT embeddings and Gaussian process backend identifies three glaucoma subgroups by decoupling progression trajectories from current visual acuity in multimodal EHR data.
citing papers explorer
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What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
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Local Truncation Error-Guided Neural ODEs for Large Scale Traffic Forecasting
LTE-ODE repurposes local truncation error as an unsupervised dynamic attention mask that preserves continuous Neural ODE evolution in stable regions while triggering discrete compensation only at anomaly points in large-scale traffic data.
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Deep Kernel Learning for Stratifying Glaucoma Trajectories
A deep kernel learning architecture with transformer feature extraction on clinical-BERT embeddings and Gaussian process backend identifies three glaucoma subgroups by decoupling progression trajectories from current visual acuity in multimodal EHR data.