"gaussian mixture models" Papers
18 papers found
Conference
Attention-based clustering
Rodrigo Maulen Soto, Pierre Marion, Claire Boyer
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
Yunzhen Feng, Elvis Dohmatob, Pu Yang et al.
Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach
Yuchen Liang, Peizhong Ju, Yingbin Liang et al.
Gaussian Mixture Counterfactual Generator
Jong-Hoon Ahn, Akshay Vashist
Go With the Flow: Fast Diffusion for Gaussian Mixture Models
George Rapakoulias, Ali Reza Pedram, Fengjiao Liu et al.
High-dimension Prototype is a Better Incremental Object Detection Learner
Yanjie Wang, Liqun Chen, Tianming Zhao et al.
How Much is a Noisy Image Worth? Data Scaling Laws for Ambient Diffusion.
Giannis Daras, Yeshwanth Cherapanamjeri, Constantinos C Daskalakis
Restricted Spectral Gap Decomposition for Simulated Tempering Targeting Mixture Distributions
Jhanvi Garg, Krishnakumar Balasubramanian, Quan Zhou
Transformers are almost optimal metalearners for linear classification
Roey Magen, Gal Vardi
Understanding Contrastive Learning via Gaussian Mixture Models
Parikshit Bansal, Ali Kavis, Sujay Sanghavi
Analyzing $D^\alpha$ seeding for $k$-means
Etienne Bamas, Sai Ganesh Nagarajan, Ola Svensson
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Zenan Ling, Longbo Li, Zhanbo Feng et al.
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
Wonho Bae, Jing Wang, Danica J. Sutherland
Federated Generalized Category Discovery
Nan Pu, Wenjing Li, Xinyuan Ji et al.
GMM-IKRS: Gaussian Mixture Models for Interpretable Keypoint Refinement and Scoring
Emanuele Santellani, Martin Zach, Christian Sormann et al.
Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
Huy Nguyen, Pedram Akbarian, Nhat Ho
Theoretical insights for diffusion guidance: A case study for Gaussian mixture models
Yuchen Wu, Minshuo Chen, Zihao Li et al.
This Probably Looks Exactly Like That: An Invertible Prototypical Network
Zachariah Carmichael, Timothy Redgrave, Daniel Gonzalez Cedre et al.