compute efficiency

Efficient Knowledge Editing via Minimal Precomputation ACL 2025 FlexiDiT: Your Diffusion Transformer Can Easily Generate High-Quality Samples with Less Compute CVPR 2025 FreqTS: Frequency-Aware Token Selection for Accelerating Diffusion Models AAAI 2025 Increased Compute Efficiency and the Diffusion of AI Capabilities AAAI 2025 Masked Generative Nested Transformers with Decode Time Scaling ICML 2025 Scaling Trends in Language Model Robustness ICML 2025 Slamming: Training a Speech Language Model on One GPU in a Day ACL 2025 Algorithmic progress in language models NeurIPS 2024 Efficient Lifelong Model Evaluation in an Era of Rapid Progress NeurIPS 2024 LibAMM: Empirical Insights into Approximate Computing for Accelerating Matrix Multiplication NeurIPS 2024 On the Scalability of Diffusion-based Text-to-Image Generation CVPR 2024 Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations NeurIPS 2024 SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention NeurIPS 2024 HyperT5: Towards Compute-Efficient Korean Language Modeling ACL 2023 Learning to Compress Prompts with Gist Tokens NeurIPS 2023 Learning To Zoom and Unzoom CVPR 2023 Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute Interspeech 2023 AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning NeurIPS 2022 Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training EMNLP 2022 Scale Efficiently: Insights from Pretraining and Finetuning Transformers ICLR 2022 Efficient Video Classification Using Fewer Frames CVPR 2019 WRPN: Wide Reduced-Precision Networks ICLR 2018