3 projects for "phpldapadmin-1.2.3-php5.5.patch" with 2 filters applied:

  • Failed Payment Recovery for Subscription Businesses Icon
    Failed Payment Recovery for Subscription Businesses

    For subscription companies searching for a failed payment recovery solution to grow revenue, and retain customers.

    FlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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  • Simplify Purchasing For Your Business Icon
    Simplify Purchasing For Your Business

    Manage what you buy and how you buy it with Order.co, so you have control over your time and money spent.

    Simplify every aspect of buying for your business in Order.co. From sourcing products to scaling purchasing across locations to automating your AP and approvals workstreams, Order.co is the platform of choice for growing businesses.
    Learn More
  • 1
    MiniMax-01

    MiniMax-01

    Large-language-model & vision-language-model based on Linear Attention

    MiniMax-01 is the official repository for two flagship models: MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model built on top of it. MiniMax-Text-01 uses a hybrid attention architecture that blends Lightning Attention, standard softmax attention, and Mixture-of-Experts (MoE) routing to achieve both high throughput and long-context reasoning. It has 456 billion total parameters with 45.9 billion activated per token and is trained with advanced parallel...
    Downloads: 0 This Week
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  • 2
    MAE (Masked Autoencoders)

    MAE (Masked Autoencoders)

    PyTorch implementation of MAE

    MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the...
    Downloads: 0 This Week
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  • 3
    TimeSformer

    TimeSformer

    The official pytorch implementation of our paper

    TimeSformer is a vision transformer architecture for video that extends the standard attention mechanism into spatiotemporal attention. The model alternates attention along spatial and temporal dimensions (or designs variants like divided attention) so that it can capture both appearance and motion cues in video. Because the attention is global across frames, TimeSformer can reason about dependencies across long time spans, not just local neighborhoods. The official implementation in PyTorch...
    Downloads: 1 This Week
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