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Found 11 papers, 4 papers with code
RD2Bench: Toward Data-Centric Automatic R&D
The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments.
Variational Stochastic Gradient Descent for Deep Neural Networks
We model gradient updates as a probabilistic model and utilize stochastic variational inference (SVI) to derive an efficient and effective update rule.
A Critical Look at Classic Test-Time Adaptation Methods in Semantic Segmentation
This pronounced emphasis on classification might lead numerous newcomers and engineers to mistakenly assume that classic TTA methods designed for classification can be directly applied to segmentation.
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction
Then, we conduct investigations and reveal the fact that: In contrast to humans, the representative state-of-the-art (SOTA) models in DocRE exhibit different decision rules.
Model-Contrastive Federated Domain Adaptation
Federated domain adaptation (FDA) aims to collaboratively transfer knowledge from source clients (domains) to the related but different target client, without communicating the local data of any client.
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
Extensive experiments on two real-world telecom fraud detection datasets demonstrate that our proposed method is effective for the graph imbalance problem, outperforming the state-of-the-art GNNs and GNN-based fraud detectors.
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection
But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work.
Does Debiasing Inevitably Degrade the Model Performance
We use our theoretical framework to explain why the current debiasing methods cause performance degradation.
Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust
To validate our theoretical analysis, we further propose another method using our proposed Causality-Aware Self-Attention Mechanism (CASAM) to guide the model to learn the underlying causality knowledge in legal texts.
ATPL: Mutually enhanced adversarial training and pseudo labeling for unsupervised domain adaptation
Accordingly, ATPL will use the pseudo-labeled information to improve the adversarial training process, which can guarantee the feature transferability by generating adversarial data to fill in the domain gap.
Contextualized End-to-End Neural Entity Linking
We propose yet another entity linking model (YELM) which links words to entities instead of spans.
