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Model Compression

1503 directly classified papers

Papers per year

Papers

Distilling Autoregressive Models to Obtain High-Performance Non-autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed AAAI 2024
MetaMix: Meta-State Precision Searcher for Mixed-Precision Activation Quantization AAAI 2024
FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing AAAI 2024
FedMut: Generalized Federated Learning via Stochastic Mutation AAAI 2024
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks AAAI 2024
DNNLasso: Scalable Graph Learning for Matrix-Variate Data AISTATS 2024
Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes AAAI 2024
Layer Compression of Deep Networks with Straight Flows AAAI 2024
Compressing Image-to-Image Translation GANs Using Local Density Structures on Their Learned Manifold AAAI 2024
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs ACL 2024
Resource Efficient Deep Learning Hardware Watermarks with Signature Alignment AAAI 2024
ShareBERT: Embeddings Are Capable of Learning Hidden Layers AAAI 2024
Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion AAAI 2024
Make RepVGG Greater Again: A Quantization-Aware Approach AAAI 2024
Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models AAAI 2024
PTMQ: Post-training Multi-Bit Quantization of Neural Networks AAAI 2024
Leveraging Normalization Layer in Adapters with Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning AAAI 2024
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression AAAI 2024
Teacher as a Lenient Expert: Teacher-Agnostic Data-Free Knowledge Distillation AAAI 2024
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models NIPS 2024
Data Shunt: Collaboration of Small and Large Models for Lower Costs and Better Performance AAAI 2024
Adaptive Compression in Federated Learning via Side Information AISTATS 2024
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding AAAI 2024
Progressively Knowledge Distillation via Re-parameterizing Diffusion Reverse Process AAAI 2024
ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference AAAI 2024