Saved in:
| Main Author: | Li, Zehua |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.06977 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
by: Stritzel, Oliver, et al.
Published: (2025)
by: Stritzel, Oliver, et al.
Published: (2025)
Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
by: Wang, Shouren, et al.
Published: (2025)
by: Wang, Shouren, et al.
Published: (2025)
Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
by: Yang, Zixuan, et al.
Published: (2024)
by: Yang, Zixuan, et al.
Published: (2024)
Robust Knowledge Transfer in Tiered Reinforcement Learning
by: Huang, Jiawei, et al.
Published: (2023)
by: Huang, Jiawei, et al.
Published: (2023)
On the Replicability and Reproducibility of Deep Learning in Software Engineering
by: Liu, Chao, et al.
Published: (2020)
by: Liu, Chao, et al.
Published: (2020)
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)
by: Brix, Christopher, et al.
Published: (2023)
by: Brix, Christopher, et al.
Published: (2023)
A Practical Guide Towards Interpreting Time-Series Deep Clinical Predictive Models: A Reproducibility Study
by: Fan, Yongda, et al.
Published: (2026)
by: Fan, Yongda, et al.
Published: (2026)
DeepCDCL: An CDCL-based Neural Network Verification Framework
by: Liu, Zongxin, et al.
Published: (2024)
by: Liu, Zongxin, et al.
Published: (2024)
ThermoQA: A Three-Tier Benchmark for Evaluating Thermodynamic Reasoning in Large Language Models
by: Düzkar, Kemal
Published: (2026)
by: Düzkar, Kemal
Published: (2026)
Towards Federated Domain Unlearning: Verification Methodologies and Challenges
by: Tam, Kahou, et al.
Published: (2024)
by: Tam, Kahou, et al.
Published: (2024)
Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior
by: Zhou, Zhiyuan, et al.
Published: (2022)
by: Zhou, Zhiyuan, et al.
Published: (2022)
Deep Configuration Performance Learning: A Systematic Survey and Taxonomy
by: Gong, Jingzhi, et al.
Published: (2024)
by: Gong, Jingzhi, et al.
Published: (2024)
Towards Understanding the Optimization Mechanisms in Deep Learning
by: Qi, Binchuan, et al.
Published: (2025)
by: Qi, Binchuan, et al.
Published: (2025)
PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generators
by: Earle, Sam, et al.
Published: (2024)
by: Earle, Sam, et al.
Published: (2024)
PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning
by: Wu, John, et al.
Published: (2026)
by: Wu, John, et al.
Published: (2026)
Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
by: Aguilar-Bejarano, Eduardo, et al.
Published: (2025)
by: Aguilar-Bejarano, Eduardo, et al.
Published: (2025)
Imitation Game: Reproducing Deep Learning Bugs Leveraging an Intelligent Agent
by: Shah, Mehil B, et al.
Published: (2025)
by: Shah, Mehil B, et al.
Published: (2025)
LLMForge: Multi-Backend Hardware-Aware Neural Architecture Search with Infinite-Head Attention for Edge Language Models
by: Jiang, Xinting, et al.
Published: (2026)
by: Jiang, Xinting, et al.
Published: (2026)
DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time Scaling
by: Wang, Qingyuan, et al.
Published: (2024)
by: Wang, Qingyuan, et al.
Published: (2024)
Probabilistic Runtime Verification, Evaluation and Risk Assessment of Visual Deep Learning Systems
by: Torpmann-Hagen, Birk, et al.
Published: (2025)
by: Torpmann-Hagen, Birk, et al.
Published: (2025)
Procrustean Bed for AI-Driven Retrosynthesis: A Unified Framework for Reproducible Evaluation
by: Morgunov, Anton, et al.
Published: (2025)
by: Morgunov, Anton, et al.
Published: (2025)
Deep Generative Model for Mechanical System Configuration Design
by: Etesam, Yasaman, et al.
Published: (2024)
by: Etesam, Yasaman, et al.
Published: (2024)
Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory
by: Zhang, Haozhen, et al.
Published: (2026)
by: Zhang, Haozhen, et al.
Published: (2026)
BaxBench: Can LLMs Generate Correct and Secure Backends?
by: Vero, Mark, et al.
Published: (2025)
by: Vero, Mark, et al.
Published: (2025)
Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective
by: Zeng, Zhiyuan, et al.
Published: (2024)
by: Zeng, Zhiyuan, et al.
Published: (2024)
UrbanPulse: A Cross-City Deep Learning Framework for Ultra-Fine-Grained Population Transfer Prediction
by: Yang, Hongrong, et al.
Published: (2025)
by: Yang, Hongrong, et al.
Published: (2025)
What Do Machine Learning Researchers Mean by "Reproducible"?
by: Raff, Edward, et al.
Published: (2024)
by: Raff, Edward, et al.
Published: (2024)
Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework
by: Zhou, Dongliang, et al.
Published: (2025)
by: Zhou, Dongliang, et al.
Published: (2025)
Designing Skill-Compatible AI: Methodologies and Frameworks in Chess
by: Hamade, Karim, et al.
Published: (2024)
by: Hamade, Karim, et al.
Published: (2024)
Towards Automated Formal Verification of Backend Systems with LLMs
by: Xu, Kangping, et al.
Published: (2025)
by: Xu, Kangping, et al.
Published: (2025)
MUC-G4: Minimal Unsat Core-Guided Incremental Verification for Deep Neural Network Compression
by: Li, Jingyang, et al.
Published: (2025)
by: Li, Jingyang, et al.
Published: (2025)
Cost-Efficient Multimodal LLM Inference via Cross-Tier GPU Heterogeneity
by: Yu, Donglin
Published: (2026)
by: Yu, Donglin
Published: (2026)
DeepGate3: Towards Scalable Circuit Representation Learning
by: Shi, Zhengyuan, et al.
Published: (2024)
by: Shi, Zhengyuan, et al.
Published: (2024)
Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
by: Ahmed, Waqas, et al.
Published: (2026)
by: Ahmed, Waqas, et al.
Published: (2026)
Towards Adversarially Robust Deep Metric Learning
by: Ke, Xiaopeng
Published: (2025)
by: Ke, Xiaopeng
Published: (2025)
Full Bayesian Significance Testing for Neural Networks
by: Liu, Zehua, et al.
Published: (2024)
by: Liu, Zehua, et al.
Published: (2024)
A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction
by: Huang, Rui, et al.
Published: (2026)
by: Huang, Rui, et al.
Published: (2026)
Learning with Logical Constraints but without Shortcut Satisfaction
by: Li, Zenan, et al.
Published: (2024)
by: Li, Zenan, et al.
Published: (2024)
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers
by: Liao, Brian Hsuan-Cheng, et al.
Published: (2022)
by: Liao, Brian Hsuan-Cheng, et al.
Published: (2022)
Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach
by: She, Ruifeng, et al.
Published: (2025)
by: She, Ruifeng, et al.
Published: (2025)
Similar Items
-
Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
by: Stritzel, Oliver, et al.
Published: (2025) -
Enhancing Player Enjoyment with a Two-Tier DRL and LLM-Based Agent System for Fighting Games
by: Wang, Shouren, et al.
Published: (2025) -
Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
by: Yang, Zixuan, et al.
Published: (2024) -
Robust Knowledge Transfer in Tiered Reinforcement Learning
by: Huang, Jiawei, et al.
Published: (2023) -
On the Replicability and Reproducibility of Deep Learning in Software Engineering
by: Liu, Chao, et al.
Published: (2020)