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Found 15 papers, 6 papers with code
Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers
To improve the faithfulness, we propose insertion/deletion metric-aware explanation-based optimization (ID-ExpO), which optimizes differentiable predictors to improve both the insertion and deletion scores of the explanations while maintaining their predictive accuracy.
Action Class Relation Detection and Classification Across Multiple Video Datasets
The Meta Video Dataset (MetaVD) provides annotated relations between action classes in major datasets for human action recognition in videos.
Learning Decorrelated Representations Efficiently Using Fast Fourier Transform
Barlow Twins and VICReg are self-supervised representation learning models that use regularizers to decorrelate features.
Gaussian Process Regression With Interpretable Sample-Wise Feature Weights
In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model.
Training Deep Models to be Explained with Fewer Examples
For improving the interpretability, reducing the number of examples in the explanation model is important.
MetaVD: A Meta Video Dataset for enhancing human action recognition datasets
To realize this solution, we constructed a meta video dataset from the existing datasets for human action recognition, referred to as MetaVD.
Gaussian Process Regression with Local Explanation
In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model.
Neural Generators of Sparse Local Linear Models for Achieving both Accuracy and Interpretability
Additionally, the prediction is interpretable because it is obtained by the inner product between the simplified representations and the sparse weights, where only a small number of weights are selected by our gate module in the NGSLL.
Video Caption Dataset for Describing Human Actions in Japanese
Each caption in our dataset describes a video in the form of "who does what and where."
STAIR Actions: A Video Dataset of Everyday Home Actions
A new large-scale video dataset for human action recognition, called STAIR Actions is introduced.
A Nonparametric Delayed Feedback Model for Conversion Rate Prediction
In this paper, we propose a nonparametric delayed feedback model for CVR prediction that represents the distribution of the time delay without assuming a parametric distribution, such as an exponential or Weibull distribution.
Learning from Noisy Label Distributions
In this problem, each instance with a feature vector belongs to at least one group.
STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset
In recent years, automatic generation of image descriptions (captions), that is, image captioning, has attracted a great deal of attention.
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions
We propose a kernel-based method for finding matching between instances across different domains, such as multilingual documents and images with annotations.
Latent Support Measure Machines for Bag-of-Words Data Classification
With the latent SMM, a latent vector is associated with each vocabulary term, and each document is represented as a distribution of the latent vectors for words appearing in the document.
