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Found 20 papers, 8 papers with code
Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation
Recommender systems use users' historical interactions to learn their preferences and deliver personalized recommendations from a vast array of candidate items.
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories
Mobile Edge Computing (MEC) broadens the scope of computation and storage beyond the central network, incorporating edge nodes close to end devices.
Token-level Direct Preference Optimization
Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions.
Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment
The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy.
Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters.
Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach
In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system.
Contrastive Collaborative Filtering for Cold-Start Item Recommendation
To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation.
Provable Unrestricted Adversarial Training without Compromise with Generalizability
Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i. e., the accuracy on natural examples) because they make a tradeoff between them.
Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System
The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing.
Time Lag Aware Sequential Recommendation
First, the existing methods often lack the simultaneous consideration of the global stability and local fluctuation of user preference, which might degrade the learning of a user's current preference.
AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback
This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided.
Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions
Empirical studies showed a strong correlation between flatness of the loss landscape at a solution and its generalizability, and stochastic gradient descent (SGD) is crucial in finding the flat solutions.
Many Field Packet Classification with Decomposition and Reinforcement Learning
Our method consists of the decomposition of fields into subsets and building separate decision trees on those subsets using a deep reinforcement learning procedure.
Global-and-Local Collaborative Learning for Co-Salient Object Detection
In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning
To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users.
Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning
Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference.
Dual Adversarial Variational Embedding for Robust Recommendation
First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns.
AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback
In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group.
Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations
Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item.
Deep Collaborative Embedding for information cascade prediction
First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion.
