When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction
Fuente:
arXiv
Guardado en:
| Autores principales: | Yu, Simin, Fathima, Sufia |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
When Data Falls Short: Grokking Below the Critical Threshold
por: Singh, Vaibhav, et al.
Publicado: (2025)
por: Singh, Vaibhav, et al.
Publicado: (2025)
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical
por: Behera, Adarsh Prasad, et al.
Publicado: (2024)
por: Behera, Adarsh Prasad, et al.
Publicado: (2024)
Chemical Reaction Extraction from Long Patent Documents
por: Jadhav, Aishwarya, et al.
Publicado: (2024)
por: Jadhav, Aishwarya, et al.
Publicado: (2024)
When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study
por: Yan, Jun, et al.
Publicado: (2026)
por: Yan, Jun, et al.
Publicado: (2026)
Learning Chemical Reaction Representation with Reactant-Product Alignment
por: Zeng, Kaipeng, et al.
Publicado: (2024)
por: Zeng, Kaipeng, et al.
Publicado: (2024)
Asymptotic Theory of Iterated Empirical Risk Minimization, with Applications to Active Learning
por: Cui, Hugo, et al.
Publicado: (2026)
por: Cui, Hugo, et al.
Publicado: (2026)
GAE Falls Short in Imperfect-Information Self-Play Reinforcement Learning
por: Fan, Zhiyuan, et al.
Publicado: (2026)
por: Fan, Zhiyuan, et al.
Publicado: (2026)
Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning
por: Li, Simin, et al.
Publicado: (2025)
por: Li, Simin, et al.
Publicado: (2025)
Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study
por: Zhang, Xingxuan, et al.
Publicado: (2025)
por: Zhang, Xingxuan, et al.
Publicado: (2025)
ULU: A Unified Activation Function
por: Huo, Simin
Publicado: (2025)
por: Huo, Simin
Publicado: (2025)
An Empirical Study of Federated Prompt Learning for Vision Language Model
por: Wang, Zhihao, et al.
Publicado: (2025)
por: Wang, Zhihao, et al.
Publicado: (2025)
Bridging the Perceptual-Statistical Gap in Dysarthria Assessment: Why Machine Learning Still Falls Short
por: Gurugubelli, Krishna
Publicado: (2025)
por: Gurugubelli, Krishna
Publicado: (2025)
An Empirical Study of $μ$P Learning Rate Transfer
por: Lingle, Lucas
Publicado: (2024)
por: Lingle, Lucas
Publicado: (2024)
An Empirical Study of Self-supervised Learning with Wasserstein Distance
por: Yamada, Makoto, et al.
Publicado: (2023)
por: Yamada, Makoto, et al.
Publicado: (2023)
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
por: Greer, Ross, et al.
Publicado: (2024)
por: Greer, Ross, et al.
Publicado: (2024)
When Intelligence Fails: An Empirical Study on Why LLMs Struggle with Password Cracking
por: Rehman, Mohammad Abdul, et al.
Publicado: (2025)
por: Rehman, Mohammad Abdul, et al.
Publicado: (2025)
When Context Sticks: Studying Interference in In-Context Learning
por: Rød, Hanna, et al.
Publicado: (2026)
por: Rød, Hanna, et al.
Publicado: (2026)
An Empirical Study of Aegis
por: Saragih, Daniel, et al.
Publicado: (2024)
por: Saragih, Daniel, et al.
Publicado: (2024)
ADAGE: Active Defenses Against GNN Extraction
por: Xu, Jing, et al.
Publicado: (2025)
por: Xu, Jing, et al.
Publicado: (2025)
Fine-tuning for Better Few Shot Prompting: An Empirical Comparison for Short Answer Grading
por: Walsh, Joel, et al.
Publicado: (2025)
por: Walsh, Joel, et al.
Publicado: (2025)
Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering
por: Nikandrou, Mavina, et al.
Publicado: (2022)
por: Nikandrou, Mavina, et al.
Publicado: (2022)
What Can RL Bring to VLA Generalization? An Empirical Study
por: Liu, Jijia, et al.
Publicado: (2025)
por: Liu, Jijia, et al.
Publicado: (2025)
Learning Shortest Paths When Data is Scarce
por: Matsypura, Dmytro, et al.
Publicado: (2026)
por: Matsypura, Dmytro, et al.
Publicado: (2026)
Neural CRNs: A Natural Implementation of Learning in Chemical Reaction Networks
por: Nagipogu, Rajiv Teja, et al.
Publicado: (2024)
por: Nagipogu, Rajiv Teja, et al.
Publicado: (2024)
Modular Multi-Task Learning for Chemical Reaction Prediction
por: Pang, Jiayun, et al.
Publicado: (2026)
por: Pang, Jiayun, et al.
Publicado: (2026)
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
por: Tang, Han, et al.
Publicado: (2024)
por: Tang, Han, et al.
Publicado: (2024)
When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
por: Muller, Christophe, et al.
Publicado: (2025)
por: Muller, Christophe, et al.
Publicado: (2025)
When Self-Belief Misleads: Active Label Acquisition for Reinforcement Learning with Verifiable Rewards
por: Wang, Li, et al.
Publicado: (2026)
por: Wang, Li, et al.
Publicado: (2026)
When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
por: Dale, Ashley S., et al.
Publicado: (2025)
por: Dale, Ashley S., et al.
Publicado: (2025)
Balancing Molecular Information and Empirical Data in the Prediction of Physico-Chemical Properties
por: Zenn, Johannes, et al.
Publicado: (2024)
por: Zenn, Johannes, et al.
Publicado: (2024)
Dynamic Activation Pitfalls in LLaMA Models: An Empirical Study
por: Ma, Chi, et al.
Publicado: (2024)
por: Ma, Chi, et al.
Publicado: (2024)
When the Coffee Feature Activates on Coffins: An Analysis of Feature Extraction and Steering for Mechanistic Interpretability
por: Ronge, Raphael, et al.
Publicado: (2026)
por: Ronge, Raphael, et al.
Publicado: (2026)
Class-Balanced and Reinforced Active Learning on Graphs
por: Yu, Chengcheng, et al.
Publicado: (2024)
por: Yu, Chengcheng, et al.
Publicado: (2024)
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations
por: Bajracharya, Pradeep, et al.
Publicado: (2024)
por: Bajracharya, Pradeep, et al.
Publicado: (2024)
Learning When to Adapt
por: Zindari, Ali, et al.
Publicado: (2026)
por: Zindari, Ali, et al.
Publicado: (2026)
An Empirical Study of Qwen3 Quantization
por: Zheng, Xingyu, et al.
Publicado: (2025)
por: Zheng, Xingyu, et al.
Publicado: (2025)
An Empirical Study of Scaling Laws for Transfer
por: Barnett, Matthew
Publicado: (2024)
por: Barnett, Matthew
Publicado: (2024)
Text-Augmented Multimodal LLMs for Chemical Reaction Condition Recommendation
por: Zhang, Yu, et al.
Publicado: (2024)
por: Zhang, Yu, et al.
Publicado: (2024)
Implicit Neural Representations for Chemical Reaction Paths
por: Ramakrishnan, Kalyan, et al.
Publicado: (2025)
por: Ramakrishnan, Kalyan, et al.
Publicado: (2025)
Active Learning for Direct Preference Optimization
por: Kveton, Branislav, et al.
Publicado: (2025)
por: Kveton, Branislav, et al.
Publicado: (2025)
Ejemplares similares
-
When Data Falls Short: Grokking Below the Critical Threshold
por: Singh, Vaibhav, et al.
Publicado: (2025) -
Exploring the Boundaries of On-Device Inference: When Tiny Falls Short, Go Hierarchical
por: Behera, Adarsh Prasad, et al.
Publicado: (2024) -
Chemical Reaction Extraction from Long Patent Documents
por: Jadhav, Aishwarya, et al.
Publicado: (2024) -
When Muon Optimizer Meets Adversarial Training: A Theoretical and Empirical Study
por: Yan, Jun, et al.
Publicado: (2026) -
Learning Chemical Reaction Representation with Reactant-Product Alignment
por: Zeng, Kaipeng, et al.
Publicado: (2024)