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Found 12 papers, 2 papers with code
Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable.
How does this interaction affect me? Interpretable attribution for feature interactions
Feature attribution is a way to analyze the impact of features on predictions.
Detecting Statistical Interactions from Neural Network Weights
Interpreting neural networks is a crucial and challenging task in machine learning.
Can I trust you more? Model-Agnostic Hierarchical Explanations
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models.
Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability
Neural networks are known to model statistical interactions, but they entangle the interactions at intermediate hidden layers for shared representation learning.
Extracting Interpretable Concept-Based Decision Trees from CNNs
In an attempt to gather a deeper understanding of how convolutional neural networks (CNNs) reason about human-understandable concepts, we present a method to infer labeled concept data from hidden layer activations and interpret the concepts through a shallow decision tree.
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
In this paper we propose a novel human-centered approach for detecting forgery in face images, using dynamic prototypes as a form of visual explanations.
U-LanD: Uncertainty-Driven Video Landmark Detection
We tackle a specifically challenging problem, where training labels are noisy and highly sparse.
Interpretable Artificial Intelligence through the Lens of Feature Interaction
Interpretation of deep learning models is a very challenging problem because of their large number of parameters, complex connections between nodes, and unintelligible feature representations.
DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction
To overcome the challenge brought by DHEN's deeper and multi-layer structure in training, we propose a novel co-designed training system that can further improve the training efficiency of DHEN.
Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation
We study a mismatch between the deep learning recommendation models' flat architecture, common distributed training paradigm and hierarchical data center topology.
