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Found 11 papers, 6 papers with code
ShieldGemma 2: Robust and Tractable Image Content Moderation
We introduce ShieldGemma 2, a 4B parameter image content moderation model built on Gemma 3.
An Oversampling-enhanced Multi-class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient-reported Outcomes
Patient-reported outcomes (PROs) directly collected from cancer patients being treated with radiation therapy play a vital role in assisting clinicians in counseling patients regarding likely toxicities.
Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion Aggregation
To the best of our knowledge, this is one of the pioneering efforts to formulate a hierarchical aggregation framework in the trusted multi-view learning domain.
Dynamic Evidence Decoupling for Trusted Multi-view Learning
To mitigate this, researchers propose trusted multi-view learning methods that estimate classification probabilities and uncertainty by learning the class distributions for each instance.
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis.
TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content
In addition, we design a trustworthy decision mechanism that integrates subjective user perspective and objective item perspective to dynamically evaluate the uncertainty of prediction results.
Trusted Multi-view Learning with Label Noise
This motivates us to delve into a new generalized trusted multi-view learning problem: how to develop a reliable multi-view learning model under the guidance of noisy labels?
Reliable Conflictive Multi-View Learning
To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data.
Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering
The two modules can effectively utilize and enhance each other, promoting the model to learn discriminative embeddings.
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.
