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- Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification879
This article proposes an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks and shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.
- TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance652
This work extracts samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions.
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- COMIC: Multi-view Clustering Without Parameter Selection369
A novel objective function to project raw data into one space in which the projection embraces the geometric consistency (GC) and the cluster assignment consistency (CAC) and could achieve promising performance in learning view-specific representation and eliminating the heterogeneous gaps across different views.
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- Communication-Efficient Federated Learning With Compensated Overlap-FedAvg220
Overlap-FedAvg is an innovative framework that loosed the chain-like constraint of federated learning and paralleled the model training phase with the model communication phase (i.e., uploading local models and downloading the global model), so that the latter phase could be totally covered by the former phase.
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- Learning Event-Based Motion Deblurring200
This paper starts from a sequential formulation of event-based motion deblurring, then shows how its optimization can be unfolded with a novel end-toend deep architecture, and proposes a differentiable directional event filtering module to effectively extract rich boundary prior from the evolution of events.
- Multi-view Spectral Clustering Network156
To deeply cluster multi-view data, MvSCN incorporates the local invariance within every single view and the consistency across different views into a novel objective function, where theLocal invariance is defined by a deep metric learning network rather than the Euclidean distance adopted by traditional approaches.
- Partially View-aligned Clustering151
A novel multi-view clustering method termed partially view-aligned clustering (PVC), which proposes to use a differentiable surrogate of the non-differentiable Hungarian algorithm and recasts it as a pluggable module to establish the category-level correspondence of the unaligned data.
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- A Deep Learning-Based End-to-End Composite System for Hand Detection and Gesture Recognition79
This work proposes a deep learning-based architecture to jointly detect and classify hand gestures and demonstrates that the proposed architecture is efficient and robust, and outperforms other approaches in both the hand detection and gesture classification tasks.
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- GLGE: A New General Language Generation Evaluation Benchmark75
The General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks, is presented and a leaderboard with strong baselines including MASS, BART, and ProphetNet is built.
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- Unsupervised Multi-Manifold Clustering by Learning Deep Representation.71
A novel deep manifold clustering method for learning effective deep representations and partitioning a dataset into clusters where each cluster contains data points from a single nonlinear manifold that can be intuitively extended to cluster out-of-sample datum.
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- Partial Differential Equations Meet Deep Neural Networks: A Survey60
This survey fills the gap in a systematic review of the diverse neural network approaches for PDEs by categorizing and reviewing the current progress of deep NNs (DNNs) for PDEs, and offers a broader taxonomy and analyze applications across scientific, engineering, and medical fields.
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- RikiNet: Reading Wikipedia Pages for Natural Question Answering59
This paper introduces a new model, called RikiNet, which reads Wikipedia pages for natural question answering, which is the first single model that outperforms the single human performance.
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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