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Found 7 papers, 4 papers with code
Resolving Training Biases via Influence-based Data Relabeling
To achieve this, we use influence functions to estimate how relabeling a training sample would affect model's test performance and further develop a novel relabeling function R. We theoretically prove that applying R to relabel harmful training samples allows the model to achieve lower test loss than simply discarding them for any classification tasks using cross-entropy loss.
Differentiable Neural Input Search for Recommender Systems
The dimensions of different feature embeddings are often set to a same value empirically, which limits the predictive performance of latent factor models.
Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions
Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models.
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Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis
The results demonstrate the effectiveness and efficiency of FIA, and the usefulness of the generated explanations for the recommendation results.
Revisiting Flow Information for Traffic Prediction
However, existing traffic prediction methods focus on modeling complex spatiotemporal traffic correlations and seldomly study the influence of the original traffic flows among regions.
Explaining Latent Factor Models for Recommendation with Influence Functions
Latent factor models (LFMs) such as matrix factorization achieve the state-of-the-art performance among various Collaborative Filtering (CF) approaches for recommendation.
A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations
We leverage both the information from monitoring stations and urban data that are closely related to air quality, including POIs, road networks and meteorology.
