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- Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation61
Resistance Training using Prior Bias (RTPB) uses a distributed-based prior bias to improve models' detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories.
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- A survey of malware behavior description and analysis56
This paper conducts a survey on malware behavior description and analysis considering three aspects: malware behavior described, behavior analysis methods, and visualization techniques.
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- UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup54
This work proposes a simple yet practical framework, called uncertainty-aware mixup (UMIX), to mitigate the overfitting issue in over-parameterized models by reweighting the ''mixed'' samples according to the sample uncertainty.
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- SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks30
This work proposes a simple yet effective plug-and-play module, SkipNode, to overcome the performance degradation of deep GCNs and provides both theoretical analysis and empirical evaluation to demonstrate the efficacy of SkipNode and its superiority over SOTA baselines.
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- Three-Dimensional Measurement Approach in Small FOV and Confined Space Using an Electronic Endoscope17
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- Dual-branch Density Ratio Estimation for Signed Network Embedding13
Extensive empirical results demonstrate that DDRE not only significantly outperforms the methods based on density ratio estimation but also achieves competitive performance compared with other types of methods such as graph likelihood, generative adversarial networks, and graph convolutional networks.
- Transcribing Natural Languages for the Deaf via Neural Editing Programs13
This work designs a new neural agent that learns to synthesize and execute editing programs, conditioned on sentence contexts and partial editing results, that outperforms previous glossification models by a large margin.
- On the Guaranteed Almost Equivalence Between Imitation Learning From Observation and Demonstration13
It is proved that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness, and suggested that it can be safely applied in practice without sacrificing the performance compared to L fD.
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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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