Efficient Computing

Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning ACL 2024 Croissant: A Metadata Format for ML-Ready Datasets NeurIPS 2024 CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers ICML 2024 CTRLorALTer: Conditional LoRAdapter for Efficient 0-Shot Control & Altering of T2I Models ECCV 2024 D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles WACV 2024 DAISY: Data Adaptive Self-Supervised Early Exit for Speech Representation Models Interspeech 2024 DAMRO: Dive into the Attention Mechanism of LVLM to Reduce Object Hallucination EMNLP 2024 Dancing with Still Images: Video Distillation via Static-Dynamic Disentanglement CVPR 2024 DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices NeurIPS 2024 Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality ICLR 2024 Data-efficient Large Vision Models through Sequential Autoregression ICML 2024 Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond ICML 2024 Data-Efficient Multimodal Fusion on a Single GPU CVPR 2024 Data Filtering Networks ICLR 2024 Data-free Distillation of Diffusion Models with Bootstrapping ICML 2024 Data-free Neural Representation Compression with Riemannian Neural Dynamics ICML 2024 Data-independent Module-aware Pruning for Hierarchical Vision Transformers ICLR 2024 Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks COLING 2024 Data Poisoning Quantization Backdoor Attack ECCV 2024 Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum NeurIPS 2024 DBD-CI: Doubling the Band Density for Bilateral Cochlear Implants Interspeech 2024 DB-LLM: Accurate Dual-Binarization for Efficient LLMs ACL 2024 DDK: Distilling Domain Knowledge for Efficient Large Language Models NeurIPS 2024 Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs COLING 2024 Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models EMNLP 2024