neural network compression

FP=XINT: Representing Neural Networks via Low-Bit Series Basis Functions AAAI 2026 FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection AAAI 2026 Aligning Sizes of Intermediate Layers by LoRA Adapter for Knowledge Distillation NAACL 2025 APHQ-ViT: Post-Training Quantization with Average Perturbation Hessian Based Reconstruction for Vision Transformers CVPR 2025 Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression CVPR 2025 Enhancing Diversity for Data-free Quantization CVPR 2025 LaCoOT: Layer Collapse through Optimal Transport ICCV 2025 Mask in the Mirror: Implicit Sparsification ICLR 2025 Robust Machine Unlearning for Quantized Neural Networks via Adaptive Gradient Reweighting with Similar Labels ICCV 2025 Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation AAAI 2025 Talking Head Anime 4: Distillation for Real-Time Performance WACV 2025 Till the Layers Collapse: Compressing a Deep Neural Network Through the Lenses of Batch Normalization Layers. AAAI 2025 BMRS: Bayesian Model Reduction for Structured Pruning NeurIPS 2024 Comparing the Decision-Making Mechanisms by Transformers and CNNs via Explanation Methods CVPR 2024 DSD²: Can We Dodge Sparse Double Descent and Compress the Neural Network Worry-Free? AAAI 2024 Geometry-aware training of factorized layers in tensor Tucker format NeurIPS 2024 Implicit neural obfuscation for privacy preserving medical image sharing MIDL 2024 Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings ICLR 2024 One-pass Multiple Conformer and Foundation Speech Systems Compression and Quantization Using An All-in-one Neural Model Interspeech 2024 One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization Training AAAI 2024 Quantization of Large Language Models with an Overdetermined Basis UAI 2024 Towards Meta-Pruning via Optimal Transport ICLR 2024 Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach AISTATS 2024 Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training NeurIPS 2024 Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression JMLR 2024