228 citations · 756 across the 12 of their papers we have counts for
13 papers
Diffusion Domain Expansion: Learning to Coordinate Pre-trained Diffusion Models
Egor Lifar, Semyon Savkin, Timur Garipov +2
In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects and handle more complex condit…
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
gpt-oss-120b & gpt-oss-20b Model Card
OpenAI, :, Sandhini Agarwal +124
We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert trans…
Generative Data Mining with Longtail-Guided Diffusion
David S. Hayden, Mao Ye, Timur Garipov +6
It is difficult to anticipate the myriad challenges that a predictive model will encounter once deployed. Common practice entails a reactive, cyclical approach: model deployment, d…
Compositional Sculpting of Iterative Generative Processes
Timur Garipov, Sebastiaan De Peuter, Ge Yang +3
High training costs of generative models and the need to fine-tune them for specific tasks have created a strong interest in model reuse and composition. A key challenge in composi…
Adversarial Support Alignment
Shangyuan Tong, Timur Garipov, Yang Zhang +2
We study the problem of aligning the supports of distributions. Compared to the existing work on distribution alignment, support alignment does not require the densities to be matc…
The Benefits of Pairwise Discriminators for Adversarial Training
Shangyuan Tong, Timur Garipov, Tommi Jaakkola
Adversarial training methods typically align distributions by solving two-player games. However, in most current formulations, even if the generator aligns perfectly with data, a s…
Subspace Inference for Bayesian Deep Learning
Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko +3
Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty. However, scaling Ba…
A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley Maddox, Timur Garipov, Pavel Izmailov +2
We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA),…
Bayesian Incremental Learning for Deep Neural Networks
Max Kochurov, Timur Garipov, Dmitry Podoprikhin +3
In industrial machine learning pipelines, data often arrive in parts. Particularly in the case of deep neural networks, it may be too expensive to train the model from scratch each…
Averaging Weights Leads to Wider Optima and Better Generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov +2
Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence. We show that simple a…
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin +2
The loss functions of deep neural networks are complex and their geometric properties are not well understood. We show that the optima of these complex loss functions are in fact c…
Ultimate tensorization: compressing convolutional and FC layers alike
Timur Garipov, Dmitry Podoprikhin, Alexander Novikov +1
Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity. To tackle this problem, [1] developed a tens…