:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Murray, Seoirse, Qi, Allison, Qian, Timothy, Schulman, John, Burns, Collin, Price, Sara
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2602.05910
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Assessing the Potential for Catastrophic Failure in Dynamic Post-Training Quantization
par: Frank, Logan, et autres
Publié: (2025)

Data Generation for Hardware-Friendly Post-Training Quantization
par: Dikstein, Lior, et autres
Publié: (2024)

From Autoencoders to CycleGAN: Robust Unpaired Face Manipulation via Adversarial Learning
par: Guo, Collin, et autres
Publié: (2025)

Discovering Latent Knowledge in Language Models Without Supervision
par: Burns, Collin, et autres
Publié: (2022)

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training
par: Hu, Pingbang, et autres
Publié: (2026)

The Role of Generator Access in Autoregressive Post-Training
par: Rege, Amit Kiran
Publié: (2026)

Lower Bounds on the Size of Markov Equivalence Classes
par: Jahn, Erik, et autres
Publié: (2025)

Causal Identification in Time Series Models
par: Jahn, Erik, et autres
Publié: (2025)

Aggregation Hides Out-of-Distribution Generalization Failures from Spurious Correlations
par: Salaudeen, Olawale, et autres
Publié: (2025)

Training ML Models with Predictable Failures
par: Schwarzer, Will, et autres
Publié: (2026)

Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation
par: Chen, Hang, et autres
Publié: (2024)

A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies
par: Wasswa, Hassan, et autres
Publié: (2025)

CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
par: Wang, Haoyu, et autres
Publié: (2024)

A Hybrid Enumeration Framework for Optimal Counterfactual Generation in Post-Acute COVID-19 Heart Failure
par: Cheng, Jingya, et autres
Publié: (2025)

SMIXAE: Towards Unsupervised Manifold Discovery in Language Models
par: Francel, Collin
Publié: (2026)

Khatri-Rao Clustering for Data Summarization
par: Ciaperoni, Martino, et autres
Publié: (2026)

Prototype Guided Post-pretraining for Single-Cell Representation Learning
par: Weerasekara, Sachini, et autres
Publié: (2026)

Causal Discovery under Latent Class Confounding
par: Mazaheri, Bijan, et autres
Publié: (2023)

Post-Training Augmentation Invariance
par: Eikenberry, Keenan, et autres
Publié: (2025)

Spend Wisely: Maximizing Post-Training Gains in Iterative Synthetic Data Bootstrapping
par: Yang, Pu, et autres
Publié: (2025)

Synthetic Survival Data Generation for Heart Failure Prognosis Using Deep Generative Models
par: Puttanawarut, Chanon, et autres
Publié: (2025)

Training Dynamics Impact Post-Training Quantization Robustness
par: Catalan-Tatjer, Albert, et autres
Publié: (2025)

DBellQuant: Breaking the Bell with Double-Bell Transformation for LLMs Post Training Binarization
par: Ye, Zijian, et autres
Publié: (2025)

Steerable Scene Generation with Post Training and Inference-Time Search
par: Pfaff, Nicholas, et autres
Publié: (2025)

Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training
par: Shu, Dong, et autres
Publié: (2026)

Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data
par: Akter, Syeda Nahida, et autres
Publié: (2025)

The Limits of Preference Data for Post-Training
par: Zhao, Eric, et autres
Publié: (2025)

Convex Dataset Valuation for Post-Training
par: Zeng, Siqi, et autres
Publié: (2026)

Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning
par: Xiao, Qian, et autres
Publié: (2024)

Towards Decentralized and Sustainable Foundation Model Training with the Edge
par: Xue, Leyang, et autres
Publié: (2025)

MetaAug: Meta-Data Augmentation for Post-Training Quantization
par: Pham, Cuong, et autres
Publié: (2024)

Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts
par: Morrison, Jacob, et autres
Publié: (2026)

Learning-Zone Energy: Online Data Selection for Efficient RL Post-Training
par: Cui, Peng, et autres
Publié: (2026)

CoScale-RL: Efficient Post-Training by Co-Scaling Data and Computation
par: Chen, Yutong, et autres
Publié: (2026)

Generate Any Scene: Scene Graph Driven Data Synthesis for Visual Generation Training
par: Gao, Ziqi, et autres
Publié: (2024)

Q-Fusion: Diffusing Quantum Circuits
par: Beaudoin, Collin, et autres
Publié: (2025)

Evaluating Effects of Augmented SELFIES for Molecular Understanding Using QK-LSTM
par: Beaudoin, Collin, et autres
Publié: (2025)

Apriel-1.5-OpenReasoner: RL Post-Training for General-Purpose and Efficient Reasoning
par: Pardinas, Rafael, et autres
Publié: (2026)

Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
par: Chidambaram, Keertana, et autres
Publié: (2026)

Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy
par: Das, Dibakar, et autres
Publié: (2022)