The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
The paper proposes a test‑time optimization technique that converts a frozen sequence reconstructor into an imputer for missing time‑series channels. By treating missing entries as optimization variables, clamping observed data, and backpropagating to minimize reconstruction error only on observed coordinates, the method avoids the need for artificial mask augmentation or full‑channel dropout during training. Experiments on five real‑world datasets show that this refinement improves mean absolute error in most dataset‑backbone combinations and can outperform mask‑aware imputers on some datasets, though it requires more inference time and depends on cross‑channel coupling.
arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.
arXiv:2606. 06328v1 Announce Type: new Abstract: In healthcare, multimodal time series tasks often operate on incomplete observations in practice, for example when ECG segments are lost because electrodes detach or an entire respiratory channel is unavailable during overnight monitoring.
arXiv:2610.20342v1 Announce Type: new Abstract: Missing data is a pervasive challenge in machine learning, often degrading predictive performance and complicating model deployment. Most existing appr...
arXiv:2507. 07754v3 Announce Type: replace-cross Abstract: Machine unlearning is usually evaluated by what the classifier outputs: forget-set accuracy, confidence, membership-inference scores.
The paper introduces the concept of protocol divergence, showing that identical nominal missing rates can lead to vastly different learning regimes in incomplete multi‑view clustering. It critiques existing evaluation practices that ignore observation structure and proposes CRAFT, a train‑once framework that fuses observed views with mask‑aware attention, enabling efficient deployment across multiple missing‑view protocols. Experiments on CUB, MultiFashion, and other benchmarks demonstrate CRAFT’s superior performance and significant computational savings through checkpoint reuse.