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Found 14 papers, 3 papers with code
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i. e., links in a graph) with items.
Repeated Padding as Data Augmentation for Sequential Recommendation
Specifically, we use the original interaction sequences as the padding content and fill it to the padding positions during model training.
Representation Surgery for Multi-Task Model Merging
That is, there is a significant discrepancy in the representation distribution between the merged and individual models, resulting in poor performance of merged MTL.
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation
In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures.
AdaMerging: Adaptive Model Merging for Multi-Task Learning
This approach aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data.
Continual Learning From a Stream of APIs
Next, we train the CL model by minimizing the gap between the responses of the CL model and the black-box API on synthetic data, to transfer the API's knowledge to the CL model.
Federated Domain Generalization: A Survey
FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy.
Data Augmented Flatness-aware Gradient Projection for Continual Learning
In this paper, we first revisit the gradient projection method from the perspective of flatness of loss surface, and find that unflatness of the loss surface leads to catastrophic forgetting of the old tasks when the projection constraint is reduced to improve the performance of new tasks.
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation
However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}.
CD$^2$: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance
By analyzing the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is a root cause of VC and IT problems in deep learning model.
Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation
However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed.
Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search
In the deployment of deep neural models, how to effectively and automatically find feasible deep models under diverse design objectives is fundamental.
A Comprehensive Survey of Incentive Mechanism for Federated Learning
Federated learning utilizes various resources provided by participants to collaboratively train a global model, which potentially address the data privacy issue of machine learning.
VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i. e., the gap between images' visual features (low-level) and labels' semantic features (high-level).
