model compression

Compressing Explicit Voxel Grid Representations: Fast NeRFs Become Also Small WACV 2023 Compressing Transformers: Features Are Low-Rank, but Weights Are Not! AAAI 2023 Compressing Volumetric Radiance Fields to 1 MB CVPR 2023 Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities JMLR 2023 Condensing Multilingual Knowledge with Lightweight Language-Specific Modules EMNLP 2023 Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference NeurIPS 2023 Constructing Deep Spiking Neural Networks From Artificial Neural Networks With Knowledge Distillation CVPR 2023 Content-Aware Token Sharing for Efficient Semantic Segmentation With Vision Transformers CVPR 2023 Continual Knowledge Distillation for Neural Machine Translation ACL 2023 Cost-effective Artificial Neural Networks IJCAI 2023 Cost-effective Distillation of Large Language Models ACL 2023 Co-training and Co-distillation for Quality Improvement and Compression of Language Models EMNLP 2023 CP3: Channel Pruning Plug-In for Point-Based Networks CVPR 2023 CrAM: A Compression-Aware Minimizer ICLR 2023 CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness AAAI 2023 Curriculum Temperature for Knowledge Distillation AAAI 2023 Data-free Knowledge Distillation for Fine-grained Visual Categorization ICCV 2023 Data-Free Knowledge Distillation via Feature Exchange and Activation Region Constraint CVPR 2023 Dataset Distillation with Convexified Implicit Gradients ICML 2023 Decomposed Prompt Tuning via Low-Rank Reparameterization EMNLP 2023 Deep Model Compression Also Helps Models Capture Ambiguity ACL 2023 Defending Against Patch-Based Backdoor Attacks on Self-Supervised Learning CVPR 2023 Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time ICML 2023 Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models EMNLP 2023 DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization ICCV 2023