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Found 21 papers, 5 papers with code
An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
To address this issue, in this paper, we focus on the problem of Partial Label Learning with Augmented Class (PLLAC), where one or more augmented classes are not visible in the training stage but appear in the inference stage.
MCICSAM: Monte Carlo-guided Interpolation Consistency Segment Anything Model for Semi-Supervised Prostate Zone Segmentation
Segment Anything Model (SAM) is a new large model for natural image segmentation, but there are some challenges in medical imaging.
AsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
Specifically, the disambiguation network is trained with self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence.
Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching
Moreover, OPAL employs a two-stage training strategy, in which the pre-train is to generate soft interests from historical interactions under the guidance of orthogonal hyper-categories of micro-videos and the fine-tune is to reinforce the degree of disentanglement among the interests and learn the temporal evolution of each interest of each user.
Denoising Long- and Short-term Interests for Sequential Recommendation
User interests can be viewed over different time scales, mainly including stable long-term preferences and changing short-term intentions, and their combination facilitates the comprehensive sequential recommendation.
Tuning Vision-Language Models with Candidate Labels by Prompt Alignment
In order to improve its robustness, we propose a simple yet effective framework that better leverages the prior knowledge of VLMs to guide the learning process with candidate labels.
A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation
Moreover, we conduct cross-view contrastive learning to keep the consistency between node embeddings from the two different views.
Towards Inductive Robustness: Distilling and Fostering Wave-induced Resonance in Transductive GCNs Against Graph Adversarial Attacks
We then prove that merely three MP iterations within GCNs can induce signal resonance between nodes and edges, manifesting as a coupling between nodes and their distillable surrounding local subgraph.
An attention-based deep learning network for predicting Platinum resistance in ovarian cancer
Multimodal data from PET/CT images of the regions of interest (ROI) were used to predict platinum resistance in patients.
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
In this paper, we propose the Graph Agent Network (GAgN) to address the aforementioned vulnerabilities of GNNs.
Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation
We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor.
Sufficient Control of Complex Networks
In this paper, we propose to study on sufficient control of complex networks which is to control a sufficiently large portion of the network, where only the quantity of controllable nodes matters.
FedCliP: Federated Learning with Client Pruning
The prevalent communication efficient federated learning (FL) frameworks usually take advantages of model gradient compression or model distillation.
Improving Micro-video Recommendation by Controlling Position Bias
Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario.
Improving Micro-video Recommendation via Contrastive Multiple Interests
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention.
Uncovering the Source of Machine Bias
We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions.
A Behavior-aware Graph Convolution Network Model for Video Recommendation
Interactions between users and videos are the major data source of performing video recommendation.
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks
Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with.
Empowering Patients Using Smart Mobile Health Platforms: Evidence From A Randomized Field Experiment
A comparison of mobile vs. PC version of the same app demonstrates that mobile has a stronger effect than PC in helping patients make these behavioral modifications with respect to diet, exercise and lifestyle, which leads to an improvement in their healthcare outcomes.
What Makes a Star Teacher? A Hierarchical BERT Model for Evaluating Teacher's Performance in Online Education
Based on these insights, we then propose a hierarchical course BERT model to predict teachers' performance in online education.
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack
In this paper, we first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks.
