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conftrace_

data efficiency

160 papers

Papers

Breaking the Resource Monopoly: LLM Post-Training and Serving with Modest Data and Compute AAAI 2026 Can You Learn to See Without Images? Procedural Warm-Up for Vision Transformers CVPR 2026 LaCo: Layer-wise Compensation for Pruned Large Language Models ACL 2026 Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction CVPR 2026 Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems CVPR 2026 Towards Aligned and Efficient Large Language Models AAAI 2026 UNSEEN: Enhancing Dataset Pruning from a Generalization Perspective AAAI 2026 Zero-Shot Coreset Selection via Iterative Subspace Sampling WACV 2026 Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review CoNLL 2025 A Comparison of Elementary Baselines for BabyLM EMNLP 2025 Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm EMNLP 2025 A Little Human Data Goes A Long Way ACL 2025 CondenseLM: LLMs-driven Text Dataset Condensation via Reward Matching EMNLP 2025 Craw4LLM: Efficient Web Crawling for LLM Pretraining ACL 2025 CycleOIE: A Low-Resource Training Framework For Open Information Extraction COLING 2025 DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation CVPR 2025 Disentangling and Integrating Relational and Sensory Information in Transformer Architectures ICML 2025 Dynamic Jointly Batch Selection for Data Efficient Machine Translation Fine-Tuning EMNLP 2025 Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration ACL 2025 Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks WACV 2025 EvA: Erasing Spurious Correlations with Activations ICLR 2025 Exploring Graph Representations of Logical Forms for Language Modeling ACL 2025 FedCS: Coreset Selection for Federated Learning CVPR 2025 Findings of the Third BabyLM Challenge: Accelerating Language Modeling Research with Cognitively Plausible Data EMNLP 2025 HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning AAAI 2025