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Modeling Dynamic Missingness of Implicit Feedback for Recommendation
Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang
Collaborative filtering methods based on implicit feedback (e.g., purchase records and browsing history) are widely used in recommender systems. Compared to explicit feedback (e.g., 1-5 star ratings), implicit feedback is more abundant and accessible in real-world applications. However, the missing data of implicit feedback also brings two challenges.
Learning GANs and Ensembles Using Discrepancy
Ben Adlam, Corinna Cortes, Mehryar Mohri, Ningshan Zhang
Neural Information Processing Systems http://nips.cc/
Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
To improve the robustness of deep classifiers against adversarial perturbations, many approaches have been proposed, such as designing new architectures with better robustness properties (e.g., Lipschitz-capped networks), or modifying the
Controlling Neural Level Sets
Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman
The level sets of a Deep Neural Network (DNN) are known to capture important characteristics andproperties ofthenetwork.
Chaining Mutual Information and Tightening Generalization Bounds
Amir Asadi, Emmanuel Abbe, Sergio Verdu
Neural Information Processing Systems http://nips.cc/
Multi-view Masked Contrastive Representation Learning for Endoscopic Video Analysis
Specifically, we propose a multi-view masking strategy for addressing the challenges of endoscopic videos.
Invertible Convolutional Flow
Mahdi Karami, Dale Schuurmans, Jascha Sohl-Dickstein, Laurent Dinh, Daniel Duckworth
As an alternative, we investigate a set ofnovel normalizing flows based on the circular and symmetric convolutions.
Generalised f-Mean Aggregation for Graph Neural Networks
Graph Neural Network (GNN) architectures are defined by their implementations of update and aggregation modules. While many works focus on new ways to parametrise the update modules, the aggregation modules receive comparatively little attention. Because it is difficult to parametrise aggregation functions, currently most methods select a "standard aggregator" such as mean, sum, or max . While this selection is often made without any reasoning, it has been shown that the choice in aggregator has a significant impact on performance, and the best choice in aggregator is problem-dependent. Since aggregation is a lossy operation, it is crucial to select the most appropriate aggregator in order to minimise information loss. In this paper, we present GenAgg, a generalised aggregation operator, which parametrises a function space that includes all standard aggregators. In our experiments, we show that GenAgg is able to represent the standard aggregators with much higher accuracy than baseline methods. We also show that using GenAgg as a drop-in replacement for an existing aggregator in a GNN often leads to a significant boost in performance across various tasks.