Alexey Gritsenko
Alexey is a Google Brain resident based in Amsterdam. He joined the residency programme after spending several years in industry working on computational advertising, and after obtaining a doctorate degree in Bioinformatics from the Delft University of Technology, where he used computational methods to study sequence determinants of protein synthesis.
During that time Alexey developed a broad interest in machine learning, but was primarily exposed to its applications. He is excited about switching to the research side of machine learning and views the residency as an opportunity to work on topics he would not otherwise be exposed to.
Alexey’s current research interests lie at the intersection of ML Fairness and generative models, but he’s always excited to talk about biology and genetics!
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Scaling Vision Transformers to 22 Billion Parameters
Josip Djolonga
Basil Mustafa
Piotr Padlewski
Justin Gilmer
Mathilde Caron
Rodolphe Jenatton
Lucas Beyer
Michael Tschannen
Anurag Arnab
Carlos Riquelme
Gamaleldin Elsayed
Fisher Yu
Avital Oliver
Fantine Huot
Mark Collier
Vighnesh Birodkar
Yi Tay
Alexander Kolesnikov
Filip Pavetić
Thomas Kipf
Xiaohua Zhai
Neil Houlsby
Arxiv (2023)
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The scaling of Transformers has driven breakthrough capabilities for language models.
At present, the largest large language models (LLMs) contain upwards of 100B parameters.
Vision Transformers (ViT) have introduced the same architecture to image and video modeling, but these have not yet been successfully scaled to nearly the same degree; the largest dense ViT contains 4B parameters. We present a recipe for highly efficient training of a 22B-parameter ViT and perform a wide variety of experiments on the resulting model. When evaluated on downstream tasks (often with a lightweight linear model on frozen features) ViT22B demonstrates increasing performance with scale. We further observe other interesting benefits of scale, including an improved tradeoff between bias and performance, an improved alignment to human visual perception in terms of shape/texture bias, and improved robustness. ViT22B demonstrates the potential for "LLM-like'' scaling in vision, and provides key steps towards getting there.
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Simple Open-Vocabulary Object Detection with Vision Transformers
Austin Stone
Maxim Neumann
Dirk Weissenborn
Alexey Dosovitskiy
Anurag Arnab
Zhuoran Shen
Xiaohua Zhai
Thomas Kipf
Neil Houlsby
ECCV (Poster) (2022)
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Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, we propose a strong recipe for transferring image-text models to open-vocabulary object detection. We use a standard Vision Transformer architecture with minimal modifications, contrastive image-text pre-training, and end-to-end detection fine-tuning. Our analysis of the scaling properties of this setup shows that increasing image-level pre-training and model size yield consistent improvements on the downstream detection task. We provide the adaptation strategies and regularizations needed to attain very strong performance on zero-shot text-conditioned and one-shot image-conditioned object detection. Code and models are available on GitHub (https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).
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IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression
Rianne van den Berg
Casper Kaae Sønderby
ICLR 2021, ICLR 2021 (to appear)
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In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transformations for integer-valued random variables. Their discrete nature makes them particularly suitable for lossless compression with entropy coding schemes. We start by investigating a recent theoretical claim that states that invertible flows for discrete random variables are less flexible than their continuous counterparts. We demonstrate with a proof that this claim does not hold for integer discrete flows due to the embedding of data with finite support into the countably infinite integer lattice. Furthermore, we zoom in on the effect of gradient bias due to the straight-through estimator in integer discrete flows, and demonstrate that its influence is highly dependent on architecture choices and less prominent than previously thought. Finally, we show how different modifications to the architecture improve the performance of this model class for lossless compression.
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A Spectral Energy Distance for Parallel Speech Synthesis
Nal Kalchbrenner
Rianne van den Berg
(2020)
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Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now outperforming traditional concatenative systems. A downside of such autoregressive models is that they require executing tens of thousands of sequential operations per second of generated audio, making them ill-suited for deployment on specialized deep learning hardware. Here, we propose a new learning method that allows us to train highly parallel models of speech, without requiring access to an analytical likelihood function. Our approach is based on a generalized energy distance between the distributions of the generated and real audio. This spectral energy distance is a proper scoring rule with respect to the distribution over magnitude-spectrograms of the generated waveform audio and offers statistical consistency guarantees. The distance can be calculated from minibatches without bias, and does not involve adversarial learning, yielding a stable and consistent method for training implicit generative models. Empirically, we achieve state-of-the-art generation quality among implicit generative models, as judged by the recently proposed cFDSD metric. When combining our method with adversarial techniques, we also improve upon the recently proposed GAN-TTS model in terms of Mean Opinion Score as judged by trained human evaluators.
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BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
Yoni Halpern
Neural Information Processing Systems: Workshop on Ethical, Social and Governance Issues in AI (2018)
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We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal model representations not to encode sensitive information, which has the effect of pushing downstream predictors towards exhibiting demographic parity with respect to the sensitive information. The net result is that these BriarPatches provide an intervention mechanism available at user level, and complements prior research on fair representations that were previously only applicable by model developers and ML experts.
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