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TitleStatusHype
MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation—0
Marginal and training-conditional guarantees in one-shot federated conformal predictionCode0
PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4—0
Inference with non-differentiable surrogate loss in a general high-dimensional classification framework—0
Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment—0
A Constraint-Enforcing Reward for Adversarial Attacks on Text Classifiers—0
Are Large Language Models Moral Hypocrites? A Study Based on Moral Foundations—0
Defeaters and Eliminative Argumentation in Assurance 2.0—0
Red Teaming Language Models for Processing Contradictory DialoguesCode0
Towards Consistent and Explainable Motion Prediction using Heterogeneous Graph Attention—0
Does Machine Bring in Extra Bias in Learning? Approximating Fairness in Models PromptlyCode0
Multi-Source Conformal Inference Under Distribution ShiftCode0
A distribution-free valid p-value for finite samples of bounded random variables—0
RS-Reg: Probabilistic and Robust Certified Regression Through Randomized SmoothingCode0
Scalable Subsampling Inference for Deep Neural Networks—0
RGBD-Glue: General Feature Combination for Robust RGB-D Point Cloud Registration—0
VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence—0
Data-Efficient and Robust Task Selection for Meta-Learning—0
LyS at SemEval-2024 Task 3: An Early Prototype for End-to-End Multimodal Emotion Linking as Graph-Based ParsingCode0
A Sharp Test for the Judge Leniency Design—0
Coherent Design of Wind Turbine Controllers Considering Transitions between Operating Regions using Fuzzy Membership Functions—0
On the Shape of Brainscores for Large Language Models (LLMs)—0
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization—0
Automated Program Repair: Emerging trends pose and expose problems for benchmarks—0
Data-driven Error Estimation: Upper Bounding Multiple Errors with No Technical Debt—0
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