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TitleStatusHype
Decoding, Fast and Slow: A Case Study on Balancing Trade-Offs in Incremental, Character-level Pragmatic Reasoning—0
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization—0
Deep Active Learning by Model Interpretability—0
Deep Active Learning for Object Detection with Mixture Density Networks—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations—0
Deep Active Learning with Budget Annotation—0
Emotion-Gradient Metacognitive RSI (Part I): Theoretical Foundations and Single-Agent Architecture—0
AdAEM: An Adaptively and Automated Extensible Measurement of LLMs' Value Difference—0
Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response—0
Deep Learning Image Recognition for Non-images—0
Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks—0
Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards—0
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning—0
Dialogue Policies for Learning Board Games through Multimodal Communication—0
Differentiable Greedy Networks—0
Differentially Private Sequential Learning—0
Converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion—0
Diffusion Approximations for a Class of Sequential Testing Problems—0
Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty—0
Directed Homophily-Aware Graph Neural Network—0
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport—0
Are LLM-generated plain language summaries truly understandable? A large-scale crowdsourced evaluation—0
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