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Found 12 papers, 6 papers with code
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
Two properties unique to the nature of sequential recommendation models may impair their robustness - the cascade effects induced during training and the model's tendency to rely too heavily on temporal information.
Generating Negative Samples for Sequential Recommendation
Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative.
ELECRec: Training Sequential Recommenders as Discriminators
A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training.
Improving Contrastive Learning with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Intent Contrastive Learning for Sequential Recommendation
Specifically, we introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering.
Self-supervised Learning for Sequential Recommendation with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Modeling Dynamic Attributes for Next Basket Recommendation
However, properly integrating them into user interest models is challenging since attribute dynamics can be diverse such as time-interval aware, periodic patterns (etc.
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
Ranked #1 on
Electron Microscopy Image Segmentation
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Electron Microscopy Image Segmentation
Image Segmentation
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Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
Another limitation of GCN when used on graph-based text representation tasks is that, GCNs do not consider the order information of nodes in graph.
Transferable Adversarial Perturbations
We first show that maximizing distance between natural images and their adversarial examples in the intermediate feature maps can improve both white-box attacks (with knowledge of the model parameters) and black-box attacks.
Dense Transformer Networks
The proposed dense transformer modules are differentiable, thus the entire network can be trained.
