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Found 11 papers, 7 papers with code
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
FedLPS leverages principles from transfer learning to facilitate the deployment of multiple tasks on a single device by dividing the local model into a shareable encoder and task-specific encoders.
Adaptive Hypergraph Network for Trust Prediction
In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations.
CETN: Contrast-enhanced Through Network for CTR Prediction
Click-through rate (CTR) Prediction is a crucial task in personalized information retrievals, such as industrial recommender systems, online advertising, and web search.
Ranked #4 on
Click-Through Rate Prediction
on Avazu
OptIForest: Optimal Isolation Forest for Anomaly Detection
Extensive experiments on a series of benchmarking datasets for comparative and ablation studies demonstrate that our approach can efficiently and robustly achieve better detection performance in general than the state-of-the-arts including the deep learning based methods.
ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence
We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes.
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning
Inspired by the recent success of sequence modeling in RL and the use of masked language model for pre-training, we propose a masked model for pre-training in RL, RePreM (Representation Pre-training with Masked Model), which trains the encoder combined with transformer blocks to predict the masked states or actions in a trajectory.
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.
A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS
Moreover, they are trained and evaluated on the benchmark datasets with adequate labels, which are expensive to obtain in a commercial dialogue system.
Source Inference Attacks in Federated Learning
However, existing MIAs ignore the source of a training member, i. e., the information of which client owns the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients.
Diversity-aware Web APIs Recommendation with Compatibility Guarantee
Afterwards, with the diverse correlation subgraphs, we model the compatible web APIs recommendation problem to be a minimum group Steiner tree search problem.
Membership Inference Attacks on Machine Learning: A Survey
In recent years, MIAs have been shown to be effective on various ML models, e. g., classification models and generative models.
