📉 Model Quantization - miterion · Scour

Efficient Quantization of Mixture-of-Experts with Theoretical Generalization Guarantees 🏎️TensorRT

arxiv.org·15h

Attn-QAT: Making 4-Bit Attention Actually Work ⚡Flash Attention

haoailab.com·1d

MF-QAT: Multi-Format Quantization-Aware Training for Elastic Inference 🏎️TensorRT

arxiv.org·6d

MoBiE: Efficient Inference of Mixture of Binary Experts under Post-Training Quantization ⚡ONNX Runtime

arxiv.org·15h

RUQuant: Towards Refining Uniform Quantization for Large Language Models 🏎️TensorRT

arxiv.org·2d

QAPruner: Quantization-Aware Vision Token Pruning for Multimodal Large Language Models 🏎️TensorRT

arxiv.org·3d

TC-AE: Unlocking Token Capacity for Deep Compression Autoencoders 🏎️TensorRT

arxiv.org·15h

Zero-Shot Quantization via Weight-Space Arithmetic 🏎️TensorRT

arxiv.org·2d

A mathematical framework for parameter recovery in large language models via a joint Euclidean mirror 🎓Model Distillation

arxiv.org·15h

Neural Network Pruning via QUBO Optimization 🔗Kernel Fusion

arxiv.org·1d

Collaborative Multi-Mode Pruning for Vision-Language Models 🎓Model Distillation

arxiv.org·3d

Rethinking Generalization in Reasoning SFT: A Conditional Analysis on Optimization, Data, and Model Capability 🎓Model Distillation

arxiv.org·15h

MUXQ: Mixed-to-Uniform Precision MatriX Quantization via Low-Rank Outlier Decomposition 🎯Tensor Cores

arxiv.org·2d

Beyond Loss Values: Robust Dynamic Pruning via Loss Trajectory Alignment 📊Gradient Accumulation

arxiv.org·15h

SoLA: Leveraging Soft Activation Sparsity and Low-Rank Decomposition for Large Language Model Compression 📊Gradient Accumulation

arxiv.org·2d

Fast NF4 Dequantization Kernels for Large Language Model Inference 🧩Attention Kernels

arxiv.org·3d

DINO-QPM: Adapting Visual Foundation Models for Globally Interpretable Image Classification 🧮cuDNN

arxiv.org·15h

Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks 🏎️TensorRT

arxiv.org·2d

Sparse Bayesian Learning Algorithms Revisited: From Learning Majorizers to Structured Algorithmic Learning using Neural Networks 🎓Model Distillation

arxiv.org·3d

R\'enyi Attention Entropy for Patch Pruning 🧩Attention Kernels

arxiv.org·2d

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