ConTex learns a global intervention strategy via a decomposed temporal-conditional encoder architecture to generate consistent, sparse counterfactuals for time series models in a single forward pass.
1223–1228
7 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
AC-GATE is a lag-gated neural encoder that conditions lag-weight distributions on entity proxies to recover heterogeneous lags as structural model outputs in panel time series.
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
FedKLPR introduces KL-divergence-guided training, pruning-aware weighted aggregation, and cross-round recovery to achieve 40-42% communication reduction on ResNet-50 while preserving competitive accuracy in federated person re-identification across eight datasets.
RDMA hash tables face five structural challenges—remote round-trips, one-sided versus two-sided ops, concurrency without remote CPUs, RNIC translation caches, and elastic resizing—and the paper organizes prior designs plus open directions.
citing papers explorer
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ConTex: Reformulating Counterfactual Generation For Time Series Forecasting
ConTex learns a global intervention strategy via a decomposed temporal-conditional encoder architecture to generate consistent, sparse counterfactuals for time series models in a single forward pass.
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RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
RABC-Net achieves 86.58% DICE and 79.47% JAC on skin lesion segmentation across ISIC-2017, ISIC-2018, and PH2 using only pseudo-labels and no manual masks for training or adaptation.
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Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series
AC-GATE is a lag-gated neural encoder that conditions lag-weight distributions on entity proxies to recover heterogeneous lags as structural model outputs in panel time series.
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Out-of-Distribution Generalization in Time Series: A Survey
This is the first comprehensive survey of OOD generalization methodologies for time series, organized across data distribution, representation learning, and OOD evaluation.
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MoGERNN: An Inductive Traffic Predictor for Unobserved Locations
MoGERNN uses a mixture-of-graph-experts module and encoder-decoder structure to predict traffic states at unobserved locations and remain effective when the sensor network changes.
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FedKLPR: KL-Guided Pruning-Aware Federated Learning for Person Re-Identification
FedKLPR introduces KL-divergence-guided training, pruning-aware weighted aggregation, and cross-round recovery to achieve 40-42% communication reduction on ResNet-50 while preserving competitive accuracy in federated person re-identification across eight datasets.
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Hash Table Design for RDMA:Challenges and Opportunities
RDMA hash tables face five structural challenges—remote round-trips, one-sided versus two-sided ops, concurrency without remote CPUs, RNIC translation caches, and elastic resizing—and the paper organizes prior designs plus open directions.