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Found 8 papers, 3 papers with code
Controllable Preference Optimization: Toward Controllable Multi-Objective Alignment
In practice, the multifaceted nature of human preferences inadvertently introduces what is known as the "alignment tax" -a compromise where enhancements in alignment within one objective (e. g., harmlessness) can diminish performance in others (e. g., helpfulness).
FreeFlow: A Comprehensive Understanding on Diffusion Probabilistic Models via Optimal Transport
The blooming diffusion probabilistic models (DPMs) have garnered significant interest due to their impressive performance and the elegant inspiration they draw from physics.
DiFace: Cross-Modal Face Recognition through Controlled Diffusion
In this context, face recognition through textual description presents a unique and promising solution that not only transcends the limitations from application scenarios but also expands the potential for research in the field of cross-modal face recognition.
Generative Flow Network for Listwise Recommendation
In this work, we aim to learn a policy that can generate sufficiently diverse item lists for users while maintaining high recommendation quality.
Exploration and Regularization of the Latent Action Space in Recommendation
To overcome this challenge, we propose a hyper-actor and critic learning framework where the policy decomposes the item list generation process into a hyper-action inference step and an effect-action selection step.
Self-adjusting optimization algorithm for solving the setunion knapsack problem
It shows that element-based design is not suitable for solving SUKP.
Sampling Before Training: Rethinking the Effect of Edges in the Process of Training Graph Neural Networks
Graph neural networks (GNN) demonstrate excellent performance on many graph-based tasks; however, they also impose a heavy computational burden when trained on a large-scale graph.
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding
Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference.
