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- 226
- Fuzzy reasoning spiking neural P system for fault diagnosis218
This work extends SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and proposes the fuzzy reasoning spiking neural P systems (FRSN P systems), which are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process.
- Graph-to-Tree Learning for Solving Math Word Problems213
Graph2Tree is proposed, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions for math word problem (MWP) solution expressions.
- 185
- Learning Sparse Representation With Variational Auto-Encoder for Anomaly Detection185
A novel sparse representation framework that learns dictionaries based on the latent space of variational auto-encoder for large-scale data sets and outperforms competing algorithms in all kinds of anomaly detection tasks.
- 183
- Zero-Shot Hashing via Transferring Supervised Knowledge162
A novel hashing scheme, termed zero-shot hashing (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of " seen" categories is proposed and an efficient alternating algorithm is developed to solve the ZSH model.
- 150
- Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption132
In this paper, impulsive synchronization of coupled delayed neural networks with actuator saturation is presented and the proposed image encryption system has high security properties.
- Learning in high-dimensional multimedia data: the state of the art129
A survey of feature transformation, feature selection and feature encoding, three approaches fighting the consequences of the curse of dimensionality on multimedia learning.
- 101
- Modeling of nonlinear system based on deep learning framework99
It is shown by experimental results that the proposed model with fewer parameters and iteration number can reconstruct the nonlinear system accurately and depict the memory effect, the non linear distortion and the dynamic performance of system precisely.
- Mining Contextual Information Beyond Image for Semantic Segmentation98
This paper proposes to mine the contextual information beyond individual images to further augment the pixel representations and proposes a representation consistent learning strategy to make the classification head better address intra-class compactness and inter-class dispersion.
- UQLIPS: a real-time near-duplicate video clip detection system98
Using a large collection of TV commercials, UQLIPS clearly demonstrates that it is feasible to perform real-time NDVC detection with high accuracy.
- Abnormal event detection for video surveillance using deep one-class learning88
A novel end-to-end model which integrates the one-class Support Vector Machine (SVM) into Convolutional Neural Network (CNN), named Deep One-Class (DOC) model is proposed, and the robust loss function derived from theOne-class SVM is proposed to optimize the parameters of this model.
- An anomaly-introduced learning method for abnormal event detection84
This work proposes an anomaly-introduced learning (AL) method to detect abnormal events, which makes use of abnormal information and prune testing instances with a coarse filter and reduce time cost of computing SRC.
- Heterogeneous data fusion for predicting mild cognitive impairment conversion81
A novel sparse regression method to fuse the auxiliary data into the predictor data for the pMCI/sMCI classification and shows the proposed method achieved the best classification performance, compared to the best comparison method, in terms of four evaluation metrics.
- Unsupervised meta-learning for few-shot learning80
An unsupervised meta-learning algorithm that learns from an unlabeled dataset and adapts to downstream human-specific tasks with few labeled data and constructs tasks using clustering embedding methods and data augmentation functions to satisfy two critical class distinction requirements is proposed.
- MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving78
This work argues that injecting numerical properties into symbolic placeholders with contextualized representation learning schema can provide a way out of the dilemma in the number representation issue here and builds MWP-BERT, an effective contextual number representation PLM.
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- Bounded coordinate system indexing for real-time video clip search75
This work proposes a novel video clip representation model called the Bounded Coordinate System (BCS), which is the first single representative capturing the dominating content and content—changing trends of a video clip and demonstrates that BCS achieves very high search accuracy according to human judgment.
- Teacher-Student Networks with Multiple Decoders for Solving Math Word Problem72
This paper proposes a novel approach, TSN-MD, by leveraging the teacher network to integrate the knowledge of equivalent solution expressions and then to regularize the learning behavior of the student network to addressMath word problem challenges.
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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 2026-10-14. 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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