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- DeepInception: Hypnotize Large Language Model to Be Jailbreaker438
A lightweight method to take advantage of the LLMs' personification capabilities to construct $\textit{a virtual, nested scene}$, allowing it to realize an adaptive way to escape the usage control in a normal scenario.
- Sparse-Interest Network for Sequential Recommendation192
A novel Sparse Interest NEtwork (SINE) is proposed, which develops an interest aggregation module to actively predict the user's current intention and then use it to explicitly model multiple interests for next-item prediction.
- Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI186
This survey is the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism, and discusses potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning.
- Learning on Attribute-Missing Graphs149
This article makes a shared-latent space assumption on graphs and develops a novel distribution matching-based GNN called structure-attribute transformer (SAT) for attribute-missing graphs that shows better performance than other methods on both link prediction and node attribute completion tasks.
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- Deep Learning From Noisy Image Labels With Quality Embedding140
A probabilistic model is proposed, which explicitly introduces an extra variable to represent the trustworthiness of noisy labels, termed as the quality variable, which effectively minimizes the influence of label noise and outperforms the state-of-the-art deep learning approaches.
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- Class-Balancing Diffusion Models80
This work investigates the problem of significant degradation in both diversity and fidelity when the diffusion model is trained on datasets with classimbalanced distributions, and proposes Class-Balancing Diffusion Models (CBDM) that are trained with a distribution adjustment regularizer as a solution.
- Device-Cloud Collaborative Learning for Recommendation78
This work is among the first attempts to study the Device-Cloud Collaborative Learning (DCCL) framework and proposes a novel MetaPatch learning approach on the device side to efficiently achieve "thousands of people with thousands of models'' given a centralized cloud model.
- Safeguarded Dynamic Label Regression for Noisy Supervision61
This work proposes a Latent Class-Conditional Noise model (LCCN) that models the noise transition in a Bayesian form and specially deduce a dynamic label regression method for LCCN to iteratively infer the latent true labels and jointly train the classifier and model the noise.
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- Collaborative Learning for Weakly Supervised Object Detection52
This paper proposes an end-to-end Weakly Supervised Collaborative Detection Network, which trains a weakly supervised learner and a strongly supervised learners jointly by enforcing partial feature sharing and prediction consistency.
- Synthesis of chiral N-aryl-α-amino acids by PdCu catalyzed couplings of chiral α-amino acids with aryl halides46
A concise synthesis of chiral N-aryl-α-amino acids, the common core structures for several biologically important molecules, by PdCu catalyzed coupling of L- or D-β-AMino acids and aryl halides is described.
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- Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation40
This survey employs agnostic active sampling theory within the Probably Approximately Correct (PAC) framework to analyze the generalization error and label complexity associated with learning from low-resource data in both model-agnostic supervised and unsupervised settings.
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- FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation35
This work introduces a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices and provides theoretical analysis of the possible benefit from FedSkip.
- How Does Disagreement Benefit Co-teaching?32
Empirical results on noisy versions of MNIST, CIFAR-10 and NEWS demonstrate that Co-teaching+ is much superior to the state-of-the-art methods in the robustness of trained deep models.
- Bayes EMbedding (BEM)30
BEM is presented, a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs and integrates them with the pre-trained embeddings from the behavior graphs via aBayesian generative model and is able to mutually refine the embeddeds from both sides while preserving their own topological structures.
- Preparation of 1,2-Diaryl(heteroaryl)pyrroles and -3-methylpyrroles from N-Allylbenzotriazole30
Numerous 1,2-diaryl(heteroaryl)pyrroles and -3-methylpyr roles were prepared in a two-step procedure from N-allylbenzotriazoles via intramolecular oxidative cyclization in the presence of Pd(II) catalyst.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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