Towards A Unified Information Bottleneck Framework for Time Series Explanations
Explaining deep learning models operating on time series data is crucial in various applications that require transparent and interpretable insights into model behavior. {Existing explanation methods generally fall into two categories: attribution-based explanations, which identify the temporal regions most responsible for a prediction, and counterfactual explanations, which reveal how an input should be modified to alter the model's decision.} {Despite valuable insights, these two fields are largely studied independently. This disconnect leaves attribution methods lacking causal validation, w
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: luo”
- FuzzyOverlapping authors or contributors · 62%DietrichGebert/ponytail →
“Shared author/contributor keys: cheng”
- LinkedLinked via arxiv author · 85%Xu Zheng →
“Towards A Unified Information Bottleneck Framework for Time Series Explanations”
- LinkedLinked via arxiv author · 85%Zichuan Liu →
“Towards A Unified Information Bottleneck Framework for Time Series Explanations”
- LinkedLinked via arxiv author · 85%Zhuomin Chen →
“Towards A Unified Information Bottleneck Framework for Time Series Explanations”
- LinkedLinked via arxiv author · 85%Mayur Akewar →
“Towards A Unified Information Bottleneck Framework for Time Series Explanations”
- LinkedLinked via arxiv author · 85%Janki Bhimani →
“Towards A Unified Information Bottleneck Framework for Time Series Explanations”