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Showing 226–250 of 2646 papers

TitleStatusHype
Dynamic Degradation Decomposition Network for All-in-One Image Restoration—0
Faithful Logic Embeddings in HOL -- Deep and Shallow—0
Was Tournament Selection All We Ever Needed? A Critical Reflection on Lexicase Selection—0
Mixing Any Cocktail with Limited Ingredients: On the Structure of Payoff Sets in Multi-Objective MDPs and its Impact on Randomised Strategies—0
MegaLoc: One Retrieval to Place Them AllCode2
All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark—0
All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty—0
All That Glitters is Not Novel: Plagiarism in AI Generated Research—0
Speed and Conversational Large Language Models: Not All Is About Tokens per Second—0
Sparsity May Be All You Need: Sparse Random Parameter AdaptationCode0
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting—0
Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting—0
I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies—0
Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above—0
Image compositing is all you need for data augmentation—0
Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient EvaluationCode0
Pricing is All You Need to Improve Traffic Routing—0
One Size doesn't Fit All: A Personalized Conversational Tutoring Agent for Mathematics Instruction—0
One for All: A General Framework of LLMs-based Multi-Criteria Decision Making on Human Expert Level—0
OCT Data is All You Need: How Vision Transformers with and without Pre-training Benefit Imaging—0
Human-centered explanation does not fit all: The interplay of sociotechnical, cognitive, and individual factors in the effect AI explanations in algorithmic decision-making—0
All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators—0
Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Measures—0
Distraction is All You Need for Multimodal Large Language Model Jailbreaking—0
Is Depth All You Need? An Exploration of Iterative Reasoning in LLMsCode0
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