Our proposed DropCov can be flexibly integrated with existing deep architectures (e.g., CNNs [ Qinghua Hu is the corresponding author and is with Engineering Research Center of City intelligence and Digital Governance, Ministry of Education of the People's Republic of China. VGG-VD on three small-scale fine-grained datasets) show 0.5 is the best choices of As listed in Table S2, we can see that single L T module brings a little gain for plain GCP . Compared to B-CNN + L T (79.62% training accuracy), plain GCP GCP + L T, while B-CNN + L T achieves significant improvement over B-CNN and plain GCP . On the contrary, the samples involving less redundant information (e.g., scene) have large Such these phenomena show the consistency with our finding. Is second-order information helpful for large-scale visual recognition?

Ukrainian authorities said Russian attacks before the talks killed 14 people across the country. This includes a drone attack on an apartment building in Kharkiv that killed at least seven people. The victims on Monday also included three people who were killed in a ballistic missile strike on the southeastern city of Zaporizhzhia, the regional governor said, adding that another 23 were wounded. Ukraine's air force said Russia launched a total of four Iskander-M ballistic missiles and 140 Shahed and decoy drones across Ukraine overnight, of which 88 drones were shot down or jammed. In Russia, falling debris from a Ukrainian drone shot down by Russian forces set off fires in a hospital building and on the grounds of an oil refinery, south of the city of Volgograd, regional governor Andrei Bocharov said in a post on Telegram.

In this paper, we leverage optimal transport (OT) which has gained momentum in the machine learning community to propose a novel and scalable biclustering model that generalizes several classical biclustering approaches.

Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge.

The increasing reliance on ML models in high-stakes tasks has raised a major concern about fairness violations.

Chalkidis et al. ( 2019) introduces the ECtHR dataset that consists of 11k cases from the European Court of Human Rights. Niklaus et al. ( 2021) releases the Swiss-Judgements-Prediction dataset that consists of 85k multilingual cases-German, French, and Italian-from the Federal Supreme Court of Switzerland. Xiao et al. ( 2018) introduces the CAIL dataset which consists of 2.7m Chinese criminal cases. The court debates are not publicly available in Korea. Chalkidis et al. ( 2022a) introduces a benchmark dataset for legal NLU in English focusing on Chalkidis et al. ( 2022b) investigate legal fairness over four legal judgement datasets with additional A.2 Precedent redaction rule Data subjected to anonymization are as follows Other personally identifible information: Social security number is deleted.

Adversarial attacks based on randomized search schemes have obtained state-of-the-art results in black-box robustness evaluation recently.