Image de couverture de livre

DiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Vali, Mohammad Hassan, Bäckström, Tom, Solin, Arno
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization
par: Vali, Mohammad Hassan, et autres
Publié: (2024)

Compressing 3D Gaussian Splatting by Noise-Substituted Vector Quantization
par: Wang, Haishan, et autres
Publié: (2025)

Stein's Lemma for the Reparameterization Trick with Exponential Family Mixtures
par: Lin, Wu, et autres
Publié: (2019)

Importance Weighted Variational Inference without the Reparameterization Trick
par: Daudel, Kamélia, et autres
Publié: (2026)

Restructuring Vector Quantization with the Rotation Trick
par: Fifty, Christopher, et autres
Publié: (2024)

Learning to Approximate Particle Smoothing Trajectories via Diffusion Generative Models
par: Tamir, Ella, et autres
Publié: (2024)

Revisiting Adaptive Rounding with Vectorized Reparameterization for LLM Quantization
par: Zhou, Yuli, et autres
Publié: (2026)

Sparsely Supervised Diffusion
par: Zhao, Wenshuai, et autres
Publié: (2026)

Smol-GS: Compact Representations for Abstract 3D Gaussian Splatting
par: Wang, Haishan, et autres
Publié: (2025)

End-to-End Training for Back-Translation with Categorical Reparameterization Trick
par: Heo, DongNyeong, et autres
Publié: (2022)

iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
par: Scannell, Aidan, et autres
Publié: (2024)

Approximate Bayesian Inference via Bitstring Representations
par: Sladek, Aleksanteri, et autres
Publié: (2025)

Heterophily-informed Message Passing
par: Wang, Haishan, et autres
Publié: (2025)

Improving Discrete Diffusion Models via Structured Preferential Generation
par: Rissanen, Severi, et autres
Publié: (2024)

Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs
par: Rissanen, Severi, et autres
Publié: (2024)

Physics-Informed Variational State-Space Gaussian Processes
par: Hamelijnck, Oliver, et autres
Publié: (2024)

Variational Gaussian Process Diffusion Processes
par: Verma, Prakhar, et autres
Publié: (2023)

Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices
par: Viset, Frida, et autres
Publié: (2024)

Streamlining Prediction in Bayesian Deep Learning
par: Li, Rui, et autres
Publié: (2024)

Non-separable Spatio-temporal Graph Kernels via SPDEs
par: Nikitin, Alexander, et autres
Publié: (2021)

Good practices for evaluation of machine learning systems
par: Ferrer, Luciana, et autres
Publié: (2024)

Privacy Leakage via Output Label Space and Differentially Private Continual Learning
par: Tobaben, Marlon, et autres
Publié: (2024)

Never Saddle for Reparameterized Steepest Descent as Mirror Flow
par: Jacobs, Tom, et autres
Publié: (2026)

Privacy Disclosure of Similarity Rank in Speech and Language Processing
par: Bäckström, Tom, et autres
Publié: (2025)

Flatness Improves Backbone Generalisation in Few-shot Classification
par: Li, Rui, et autres
Publié: (2024)

Sources of Uncertainty in 3D Scene Reconstruction
par: Klasson, Marcus, et autres
Publié: (2024)

GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
par: Cai, Yaohui, et autres
Publié: (2026)

ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions
par: Li, Yule, et autres
Publié: (2025)

Generalized Radius and Integrated Codebook Transforms for Differentiable Vector Quantization
par: You, Haochen, et autres
Publié: (2026)

VeFA: Vector-Based Feature Space Adaptation for Robust Model Fine-Tuning
par: Wang, Peng, et autres
Publié: (2025)

Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
par: Laabid, Najwa, et autres
Publié: (2024)

Function-space Parameterization of Neural Networks for Sequential Learning
par: Scannell, Aidan, et autres
Publié: (2024)

DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers
par: Sharify, Sayeh, et autres
Publié: (2026)

Diffusion Fine-Tuning via Reparameterized Policy Gradient of the Soft Q-Function
par: Kang, Hyeongyu, et autres
Publié: (2025)

RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization
par: Li, Zhikai, et autres
Publié: (2024)

Sign-In to the Lottery: Reparameterizing Sparse Training From Scratch
par: Gadhikar, Advait, et autres
Publié: (2025)

Sequential Causal Discovery with Noisy Language Model Priors
par: Verma, Prakhar, et autres
Publié: (2025)

Self-Attention Decomposition For Training Free Diffusion Editing
par: Anand, Tharun, et autres
Publié: (2025)

CAMP-HiVe: Cyclic Pair Merging based Efficient DNN Pruning with Hessian-Vector Approximation for Resource-Constrained Systems
par: Uddin, Mohammad Helal, et autres
Publié: (2025)

Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach
par: Chen, Chao, et autres
Publié: (2023)