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- IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing141
A uniform IM benchmark is proposed, for the first time, to assess how well IAD algorithms perform, which includes various levels of supervision, learning paradigms, memory usage and inference speed, and efficiency.
- Class-Aware Contrastive Semi-Supervised Learning140
This work proposes a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL), which is a drop-in helper to improve the pseudo-label quality and enhance the model's robustness in the real-world setting and successfully emphasizes clean label learning and simultaneously reduce noisy label learning.
- Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost140
This paper introduces a transportation resource sharing strategy to address the multi-depot green vehicle routing problem, and incorporates the time-dependency of speed as well as piecewise penalty costs for earliness and tardiness of deliveries.
- Morphological Feature Visualization of Alzheimer’s Disease via Multidirectional Perception GAN131
By introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently and clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain.
- Artificial intelligence, physiological genomics, and precision medicine129
Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.
- Collaborative two-echelon multicenter vehicle routing optimization based on state–space–time network representation128
An empirical case study of a logistics network in Chongqing suggests that the proposed collaboration mechanism with SST network representation can reduce costs, improve transportation efficiency, and contribute to efficient and sustainable logistics network operations.
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- Neural Retrievers are Biased Towards LLM-Generated Content76
A quantitative evaluation of IR models in scenarios where both human-written and LLM-generated texts are involved indicates that neural retrieval models tend to rank LLM-generated documents higher, and proposes a plug-and-play debiased constraint for the optimization objective to mitigate the source bias.
- End-to-End Beam Retrieval for Multi-Hop Question Answering66
Beam Retrieval is introduced, an end-to-end beam retrieval framework for multi-hop QA that helps the authors' supervised reader achieve new state-of-the-art performance and substantially improves the few-shot QA performance of LLMs.
- Multimodal Representations Learning and Adversarial Hypergraph Fusion for Early Alzheimer’s Disease Prediction57
A novel multimodal representation learning and adversarial hypergraph fusion (MRL-AHF) framework for Alzheimer's disease diagnosis using complete trimodal images and provides a possible way to understand the underlying mechanisms of disorder's progression by analyzing the abnormal brain connections.
- The general inner-outer iteration method based on regular splittings for the PageRank problem44
A general inner-outer (GIO) iteration method is presented for solving the PageRank problem and the preconditioner derived from the GIO iteration can be used to accelerate the Krylov subspace methods, such as GMRES method.
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- Neural Modeling of Buying Behaviour for E-Commerce from Clicking Patterns28
This paper provides a direct method in modeling the buying pattern in a clicking session by simply using the time-stamp of the clicks and shows that the result is comparable to using more massive feature engineering that requires session summarizing.
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- Optimal Synthesis of Mechanisms for Path Generation Using Refined Numerical Representation Based Model and AIS Based Searching Method26
An optimal synthesis method is presented, in which a new path-description method named refined numerical representation is proposed to define the object function of the optimization model for path generation, and then the artificial immune network searching method is introduced to search candidate solutions.
- Infinite Kernel Learning: Generalization Bounds and Algorithms25
A new kernel learning method is proposed based on a novel measure of generalization error, called principal eigenvalue proportion (PEP), which can learn the optimal kernel with sharp generalization bounds over the convex hull of a possibly infinite set of basic kernels.
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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 2026-10-12. 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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