arXiv Machine Learning

Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture - Bridging Predictive and Generative Self-Supervised Learning

arXiv Machine Learning

Jul 27

Unbiased Open World Regularization for Fair Self-Supervised Learning

arXiv:2607. 22149v1 Announce Type: new Abstract: Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spurious biases in the dataset.

By L{\'e}o Nicollier (CB, ATT), Marc Pic (ATT), Pablo Mus{\'e} (CB, IFUMI), Enric Meinhardt-Llopis (CB), Gabriele Facciolo (CB)

rag diffusion benchmarks safety

arXiv Computer Vision

Sep 1

GenFirst: Generation Before Reconstruction for Stable End-to-End Latent Generative Modeling

arXiv:2608.29335v1 Announce Type: new Abstract: Latent generative models typically follow a two-stage pipeline, training a variational autoencoder for reconstruction and then a generative model on th...

By Guangting Zheng, Yiyuan Zhang, Tao Yang, Yunpeng Chen, Rui Zhu, Jiajun Deng, Yanyong Zhang

diffusion

arXiv Machine Learning

Jul 21

Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations

arXiv:2607. 16725v1 Announce Type: cross Abstract: Conditional generative modeling remains a challenging problem in semi-supervised settings where labeled data is scarce but unlabeled samples are abundant.

By Changyu Liu, Yuling Jiao, Jian Huang

safety

arXiv Machine Learning

Jul 7

Self-Supervised Learning from Structural Invariance

arXiv:2602. 02381v2 Announce Type: replace Abstract: Joint-embedding self-supervised learning (SSL), the key paradigm for unsupervised representation learning from visual data, learns from invariances between semantically-related data pairs.

By Yipeng Zhang, Hafez Ghaemi, Jungyoon Lee, Shahab Bakhtiari, Eilif B. Muller, Laurent Charlin

rag efficiency

arXiv Machine Learning

Sep 30

High-Dimensional Simulation-Based Inference in Latent Spaces

arXiv:2609.37381v1 Announce Type: new Abstract: Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially...

By Lars K\"uhmichel, Stefan T. Radev, Bhanu Prasanna Koppolu, Masoumeh Davoudi, Jerry M. Huang, Paul-Christian B\"urkner

diffusion

arXiv Machine Learning

Jul 28

Self-Distillation of Hidden Layers for Self-Supervised Representation Learning

arXiv:2603. 15553v2 Announce Type: replace-cross Abstract: The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.

By Scott C. Lowe, Anthony Fuller, Sageev Oore, Evan Shelhamer, Graham W. Taylor

rag computer-vision efficiency

arXiv Machine Learning

Aug 12

Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images

arXiv:2606. 21806v2 Announce Type: replace Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains.

By Jingyuan Chen, Kangrui Ruan, Junzhe Zhang

rag benchmarks

arXiv AI

Jun 12

Hellinger Multimodal Variational Autoencoders

arXiv:2601. 06572v4 Announce Type: replace-cross Abstract: Multimodal variational autoencoders (VAEs) are widely used for weakly supervised generative learning with multiple modalities.

By Huyen Vo, Isabel Valera

multimodal benchmarks

arXiv AI

3d ago

Learning to Be Guided: Shaping Latent Geometry for Classifier-Free Guidance

arXiv:2610.22885v1 Announce Type: cross Abstract: Classifier-free guidance (CFG) is a standard component of conditional diffusion models, and guided generation performance has become the \textit{de f...

By Haoming Liu, Junhao Zhu, Shaoyi Zheng, Yuanhe Guo, Jinnuo Liu, Shenji Wan, Hongyi Wen

rag diffusion benchmarks

arXiv Machine Learning

Oct 15

TabFORGE: A Tabular Foundation Model for Generative Modelling

arXiv:2605.09424v2 Announce Type: replace Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modali...

By Xiangjian Jiang, Mingxuan Liu, Nikola Simidjievski, Tassilo Klein, Mateja Jamnik

llms rag diffusion benchmarks safety data-engineering

arXiv Machine Learning

Sep 30

Self-Supervised Representation Learning as Mutual Information Maximization

arXiv:2510.01345v2 Announce Type: replace Abstract: Self-supervised representation learning (SSRL) has demonstrated remarkable empirical success, yet its underlying principles remain insufficiently u...

By Akhlaqur Rahman Sabby, Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu

efficiency

arXiv Machine Learning

Jun 16

Structured Nonparametric Variational Inference for Dependent Latent Modeling

arXiv:2606. 15458v1 Announce Type: cross Abstract: Variational inference (VI) is a core engine of modern AI, enabling scalable approximate Bayesian learning and uncertainty-aware training of large probabilistic and generative models.

By Yuda Shao, Zhiling Gu, Shan Yu

computer-vision

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