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  1. Book ; Online: No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms

    Vladymyrov, Max

    2019  

    Abstract: Nonlinear embedding manifold learning methods provide invaluable visual insights into the structure of high-dimensional data. However, due to a complicated nonconvex objective function, these methods can easily get stuck in local minima and their ... ...

    Abstract Nonlinear embedding manifold learning methods provide invaluable visual insights into the structure of high-dimensional data. However, due to a complicated nonconvex objective function, these methods can easily get stuck in local minima and their embedding quality can be poor. We propose a natural extension to several manifold learning methods aimed at identifying pressured points, i.e. points stuck in poor local minima and have poor embedding quality. We show that the objective function can be decreased by temporarily allowing these points to make use of an extra dimension in the embedding space. Our method is able to improve the objective function value of existing methods even after they get stuck in a poor local minimum.

    Comment: 10 pages, NeurIPS 2019
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Publishing date 2019-06-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Transluminal Pillars-Their Origin and Role in the Remodelling of the Zebrafish Caudal Vein Plexus.

    Röss, Helena / Aaldijk, Dea / Vladymyrov, Mykhailo / Odriozola, Adolfo / Djonov, Valentin

    International journal of molecular sciences

    2023  Volume 24, Issue 23

    Abstract: Intussusceptive pillars, regarded as a hallmark of intussusceptive angiogenesis, have been described in developing vasculature of many organs and organisms. The aim of this study was to resolve the question about pillar formation and their further ... ...

    Abstract Intussusceptive pillars, regarded as a hallmark of intussusceptive angiogenesis, have been described in developing vasculature of many organs and organisms. The aim of this study was to resolve the question about pillar formation and their further maturation employing zebrafish caudal vein plexus (CVP). The CVP development was monitored by in vivo confocal microscopy in high spatio-temporal resolution using the transgenic zebrafish model
    MeSH term(s) Animals ; Zebrafish ; Morphogenesis ; Hemodynamics ; Intussusception ; Intravital Microscopy ; Neovascularization, Physiologic/physiology
    Language English
    Publishing date 2023-11-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms242316703
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Training trajectories, mini-batch losses and the curious role of the learning rate

    Sandler, Mark / Zhmoginov, Andrey / Vladymyrov, Max / Miller, Nolan

    2023  

    Abstract: Stochastic gradient descent plays a fundamental role in nearly all applications of deep learning. However its ability to converge to a global minimum remains shrouded in mystery. In this paper we propose to study the behavior of the loss function on ... ...

    Abstract Stochastic gradient descent plays a fundamental role in nearly all applications of deep learning. However its ability to converge to a global minimum remains shrouded in mystery. In this paper we propose to study the behavior of the loss function on fixed mini-batches along SGD trajectories. We show that the loss function on a fixed batch appears to be remarkably convex-like. In particular for ResNet the loss for any fixed mini-batch can be accurately modeled by a quadratic function and a very low loss value can be reached in just one step of gradient descent with sufficiently large learning rate. We propose a simple model that allows to analyze the relationship between the gradients of stochastic mini-batches and the full batch. Our analysis allows us to discover the equivalency between iterate aggregates and specific learning rate schedules. In particular, for Exponential Moving Average (EMA) and Stochastic Weight Averaging we show that our proposed model matches the observed training trajectories on ImageNet. Our theoretical model predicts that an even simpler averaging technique, averaging just two points a many steps apart, significantly improves accuracy compared to the baseline. We validated our findings on ImageNet and other datasets using ResNet architecture.

    Comment: 21 pages, 14 figures
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-01-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Continual Few-Shot Learning Using HyperTransformers

    Vladymyrov, Max / Zhmoginov, Andrey / Sandler, Mark

    2023  

    Abstract: We focus on the problem of learning without forgetting from multiple tasks arriving sequentially, where each task is defined using a few-shot episode of novel or already seen classes. We approach this problem using the recently published HyperTransformer ...

    Abstract We focus on the problem of learning without forgetting from multiple tasks arriving sequentially, where each task is defined using a few-shot episode of novel or already seen classes. We approach this problem using the recently published HyperTransformer (HT), a Transformer-based hypernetwork that generates specialized task-specific CNN weights directly from the support set. In order to learn from a continual sequence of tasks, we propose to recursively re-use the generated weights as input to the HT for the next task. This way, the generated CNN weights themselves act as a representation of previously learned tasks, and the HT is trained to update these weights so that the new task can be learned without forgetting past tasks. This approach is different from most continual learning algorithms that typically rely on using replay buffers, weight regularization or task-dependent architectural changes. We demonstrate that our proposed Continual HyperTransformer method equipped with a prototypical loss is capable of learning and retaining knowledge about past tasks for a variety of scenarios, including learning from mini-batches, and task-incremental and class-incremental learning scenarios.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004 ; 006
    Publishing date 2023-01-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Fine-tuning Image Transformers using Learnable Memory

    Sandler, Mark / Zhmoginov, Andrey / Vladymyrov, Max / Jackson, Andrew

    2022  

    Abstract: In this paper we propose augmenting Vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optionally preserving its capabilities on previously learned tasks. At each layer ...

    Abstract In this paper we propose augmenting Vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optionally preserving its capabilities on previously learned tasks. At each layer we introduce a set of learnable embedding vectors that provide contextual information useful for specific datasets. We call these "memory tokens". We show that augmenting a model with just a handful of such tokens per layer significantly improves accuracy when compared to conventional head-only fine-tuning, and performs only slightly below the significantly more expensive full fine-tuning. We then propose an attention-masking approach that enables extension to new downstream tasks, with a computation reuse. In this setup in addition to being parameters efficient, models can execute both old and new tasks as a part of single inference at a small incremental cost.

    Comment: CVPR 2022, to appear
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004 ; 006
    Publishing date 2022-03-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: HyperTransformer

    Zhmoginov, Andrey / Sandler, Mark / Vladymyrov, Max

    Model Generation for Supervised and Semi-Supervised Few-Shot Learning

    2022  

    Abstract: In this work we propose a HyperTransformer, a Transformer-based model for supervised and semi-supervised few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. Since the dependence of a small ... ...

    Abstract In this work we propose a HyperTransformer, a Transformer-based model for supervised and semi-supervised few-shot learning that generates weights of a convolutional neural network (CNN) directly from support samples. Since the dependence of a small generated CNN model on a specific task is encoded by a high-capacity Transformer model, we effectively decouple the complexity of the large task space from the complexity of individual tasks. Our method is particularly effective for small target CNN architectures where learning a fixed universal task-independent embedding is not optimal and better performance is attained when the information about the task can modulate all model parameters. For larger models we discover that generating the last layer alone allows us to produce competitive or better results than those obtained with state-of-the-art methods while being end-to-end differentiable.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2022-01-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Multiphoton In Vivo Microscopy of Embryonic Thrombopoiesis Reveals the Generation of Platelets through Budding.

    Liu, Huan / Ishikawa-Ankerhold, Hellen / Winterhalter, Julia / Lorenz, Michael / Vladymyrov, Mykhailo / Massberg, Steffen / Schulz, Christian / Orban, Mathias

    Cells

    2023  Volume 12, Issue 19

    Abstract: Platelets are generated by specialized cells called megakaryocytes (MKs). However, MK's origin and platelet release mode have remained incompletely understood. Here, we established direct visualization of embryonic thrombopoiesis in vivo by combining ... ...

    Abstract Platelets are generated by specialized cells called megakaryocytes (MKs). However, MK's origin and platelet release mode have remained incompletely understood. Here, we established direct visualization of embryonic thrombopoiesis in vivo by combining multiphoton intravital microscopy (MP-IVM) with a fluorescence switch reporter mouse model under control of the platelet factor 4 promoter (
    MeSH term(s) Mice ; Animals ; Thrombopoiesis ; Microscopy ; Blood Platelets ; Megakaryocytes ; Platelet Count
    Language English
    Publishing date 2023-10-06
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2661518-6
    ISSN 2073-4409 ; 2073-4409
    ISSN (online) 2073-4409
    ISSN 2073-4409
    DOI 10.3390/cells12192411
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: High levels of endothelial ICAM-1 prohibit natalizumab mediated abrogation of CD4

    Soldati, Sasha / Bär, Alexander / Vladymyrov, Mykhailo / Glavin, Dale / McGrath, James L / Gosselet, Fabien / Nishihara, Hideaki / Goelz, Susan / Engelhardt, Britta

    Journal of neuroinflammation

    2023  Volume 20, Issue 1, Page(s) 123

    Abstract: Introduction: The humanized anti-α4 integrin blocking antibody natalizumab (NTZ) is an effective treatment for relapsing-remitting multiple sclerosis (RRMS) that is associated with the risk of progressive multifocal leukoencephalopathy (PML). While ... ...

    Abstract Introduction: The humanized anti-α4 integrin blocking antibody natalizumab (NTZ) is an effective treatment for relapsing-remitting multiple sclerosis (RRMS) that is associated with the risk of progressive multifocal leukoencephalopathy (PML). While extended interval dosing (EID) of NTZ reduces the risk for PML, the minimal dose of NTZ required to maintain its therapeutic efficacy remains unknown.
    Objective: Here we aimed to identify the minimal NTZ concentration required to inhibit the arrest of human effector/memory CD4
    Results: Making use of three different human in vitro BBB models and in vitro live-cell imaging we observed that NTZ mediated inhibition of α4-integrins failed to abrogate T cell arrest to the inflamed BBB under physiological flow. Complete inhibition of shear resistant T cell arrest required additional inhibition of β2-integrins, which correlated with a strong upregulation of endothelial intercellular adhesion molecule (ICAM)-1 on the respective BBB models investigated. Indeed, NTZ mediated inhibition of shear resistant T cell arrest to combinations of immobilized recombinant vascular cell adhesion molecule (VCAM)-1 and ICAM-1 was abrogated in the presence of tenfold higher molar concentrations of ICAM-1 over VCAM-1. Also, monovalent NTZ was less potent than bivalent NTZ in inhibiting T cell arrest to VCAM-1 under physiological flow. In accordance with our previous observations ICAM-1 but not VCAM-1 mediated T cell crawling against the direction of flow.
    Conclusion: Taken together, our in vitro observations show that high levels of endothelial ICAM-1 abrogate NTZ mediated inhibition of T cell interaction with the BBB. EID of NTZ in MS patients may thus require consideration of the inflammatory status of the BBB as high levels of ICAM-1 may provide an alternative molecular cue allowing for pathogenic T cell entry into the CNS in the presence of NTZ.
    MeSH term(s) Humans ; Natalizumab ; T-Lymphocytes ; Blood-Brain Barrier ; Intercellular Adhesion Molecule-1 ; Integrin alpha4 ; CD4-Positive T-Lymphocytes
    Chemical Substances Natalizumab ; nitazoxanide (SOA12P041N) ; Intercellular Adhesion Molecule-1 (126547-89-5) ; Integrin alpha4 (143198-26-9)
    Language English
    Publishing date 2023-05-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 2156455-3
    ISSN 1742-2094 ; 1742-2094
    ISSN (online) 1742-2094
    ISSN 1742-2094
    DOI 10.1186/s12974-023-02797-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Decentralized Learning with Multi-Headed Distillation

    Zhmoginov, Andrey / Sandler, Mark / Miller, Nolan / Kristiansen, Gus / Vladymyrov, Max

    2022  

    Abstract: Decentralized learning with private data is a central problem in machine learning. We propose a novel distillation-based decentralized learning technique that allows multiple agents with private non-iid data to learn from each other, without having to ... ...

    Abstract Decentralized learning with private data is a central problem in machine learning. We propose a novel distillation-based decentralized learning technique that allows multiple agents with private non-iid data to learn from each other, without having to share their data, weights or weight updates. Our approach is communication efficient, utilizes an unlabeled public dataset and uses multiple auxiliary heads for each client, greatly improving training efficiency in the case of heterogeneous data. This approach allows individual models to preserve and enhance performance on their private tasks while also dramatically improving their performance on the global aggregated data distribution. We study the effects of data and model architecture heterogeneity and the impact of the underlying communication graph topology on learning efficiency and show that our agents can significantly improve their performance compared to learning in isolation.
    Keywords Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2022-11-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

    Vladymyrov, Mykhailo / Ariga, Akitaka

    2019  

    Abstract: Efficient tracking algorithm is a crucial part of particle tracking detectors. While big work was done in designing plethora of various algorithms, they usually require tedious tuning for each use case. (Weakly) supervised Machine Learning-based ... ...

    Abstract Efficient tracking algorithm is a crucial part of particle tracking detectors. While big work was done in designing plethora of various algorithms, they usually require tedious tuning for each use case. (Weakly) supervised Machine Learning-based approaches can leverage the actual raw data for maximal performance. Yet in realistic scenarios sufficient high-quality labeled data is not available. While sometimes training can be performed on simulated data, often appropriate simulation of detector noise is impossible, compromising this approach. Here we propose a novel fully unsupervised approach to track reconstruction. The introduced model for learning to disentangle the factors of variation in a geometrically meaningful way employs geometrical space invariances. We train it through constraints on the equivariance between the image space and the latent representation in a Deep Convolutional Autoencoder. Using experimental results on synthetic data we show requirement of the variety of the space transformations for meaningful disentanglement of factors of variation. We also demonstrate performance of our model on real data from tracking detectors.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; High Energy Physics - Experiment
    Subject code 006
    Publishing date 2019-09-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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