Feature Importance
246 papers with code • 6 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Feature Importance
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Libraries
Use these libraries to find Feature Importance models and implementations
Digits
iris
Wine
Hotel Sales
simply-CLEVR
Latest papers
Dual feature-based and example-based explanation methods
Kozlov992/Dual-Explanation
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A new approach to the local and global explanation is proposed.
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopes
This thesis comprises the first three chapters dedicated to providing an overview of Gamma Ray-Bursts (GRBs), their properties, the instrumentation used to detect them, and Artificial Intelligence (AI) applications in the context of GRBs, including a literature review and future prospects.
AFS-BM: Enhancing Model Performance through Adaptive Feature Selection with Binary Masking
yigitturali/afs_bm-algorithm
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In particular, we do the joint optimization and binary masking to continuously adapt the set of features and model parameters during the training process.
FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks
With the steady rise of the use of AI in bio-technical applications and the widespread adoption of genomics sequencing, an increasing amount of AI-based algorithms and tools is entering the research and production stage affecting critical decision-making streams like drug discovery and clinical outcomes.
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models
hugozhl/cafe
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Guided by our design philosophy, we further propose a multi-level hash embedding framework to optimize the embedding tables of non-hot features.
Predicting Postoperative Nausea And Vomiting Using Machine Learning: A Model Development and Validation Study
Therefore, prognostic tools for the prediction of early and delayed PONV were developed in this study with the aim of achieving satisfactory predictive performance.
Neural Network Pruning by Gradient Descent
The rapid increase in the parameters of deep learning models has led to significant costs, challenging computational efficiency and model interpretability.
Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution
Also, we demonstrate that Sweetwater effectively uncovers biologically meaningful patterns during the training process, increasing the reliability of the results.
A novel post-hoc explanation comparison metric and applications
Explanatory systems make the behavior of machine learning models more transparent, but are often inconsistent.
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
jgethanchen/gaia-ood
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NeurIPS 2023
This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns.
