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Found 14 papers, 4 papers with code
Data-centric Prediction Explanation via Kernelized Stein Discrepancy
Existing example-based prediction explanation methods often bridge test and training data points through the model's parameters or latent representations.
Within-basket Recommendation via Neural Pattern Associator
Within-basket recommendation (WBR) refers to the task of recommending items to the end of completing a non-empty shopping basket during a shopping session.
Self-supervised Representation Learning From Random Data Projectors
Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations.
fAux: Testing Individual Fairness via Gradient Alignment
We propose a novel criterion for evaluating individual fairness and develop a practical testing method based on this criterion which we call fAux (pronounced fox).
Scalable Whitebox Attacks on Tree-based Models
Adversarial robustness is one of the essential safety criteria for guaranteeing the reliability of machine learning models.
PUMA: Performance Unchanged Model Augmentation for Training Data Removal
It then complements the negative impact of removing marked data by reweighting the remaining data optimally.
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models
Explaining the influence of training data on deep neural network predictions is a critical tool for debugging models through data curation.
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction
We additionally introduce a novel Frobenius norm-based contrastive learning objective to improve latent representational generalization. Empirically, we validate MAPSED on two publicly accessible urban crime datasets for spatiotemporal sparse event prediction, where MAPSED outperforms both classical and state-of-the-art deep learning models.
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering
In order to capture multifaceted user preferences, existing recommender systems either increase the encoding complexity or extend the latent representation dimension.
Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering
One-class collaborative filtering (OC-CF) is a common class of recommendation problem where only the positive class is explicitly observed (e. g., purchases, clicks).
Scalable Planning with Deep Neural Network Learned Transition Models
But there remains one major problem for the task of control -- how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model techniques that ignore model structure and do not easily extend to mixed discrete and continuous domains?
Aesthetic Features for Personalized Photo Recommendation
Many photography websites such as Flickr, 500px, Unsplash, and Adobe Behance are used by amateur and professional photography enthusiasts.
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding
Variational Autoencoders (VAEs) are a popular generative model, but one in which conditional inference can be challenging.
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Given recent deep learning results that demonstrate the ability to effectively optimize high-dimensional non-convex functions with gradient descent optimization on GPUs, we ask in this paper whether symbolic gradient optimization tools such as Tensorflow can be effective for planning in hybrid (mixed discrete and continuous) nonlinear domains with high dimensional state and action spaces?
