Search Results for "data processing"
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Showing 3 open source projects for "data processing"
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Skillfully - The future of skills based hiringSkillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
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One Unified Time Tracking Software For Projects, Billing, Pay and ComplianceReplicon's time-tracking platform is scalable and configurable to support the diverse needs of small, mid & large businesses with a remote and globally distributed workforce. Replicon’s Time Tracking is a cloud-based, enterprise-grade solution that tracks employee time across projects, tasks, presence, and absence to facilitate client billing, project costing, and compliant payroll processing. The scalable and configurable platform offers seamless integration with common business technology stacks, such as ERP, CRM, Accounting, and payroll solutions. With AI-powered time capture, mobile apps, and labor compliance as a service, Replicon makes time tracking hassle-free.
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Databend
Cloud-native open source data warehouse for analytics and AI queries
Databend is an open source cloud-native data warehouse designed for large-scale analytics and modern data workloads. Built in Rust, the system focuses on high performance, scalability, and efficient data processing for analytical queries. It is designed with a separation of compute and storage, allowing compute nodes to scale independently while storing data in object storage systems. -
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CycleGAN
Software that can generate photos from paintings
CycleGAN — in its original form — is a landmark in deep learning for image-to-image translation without paired data. Rather than requiring matching image pairs between source and target domains (which are often hard or impossible to obtain), CycleGAN learns two mappings — one from domain A to B, and another back from B to A — along with a cycle-consistency loss that encourages the round-trip to reconstruct the original image. This innovation lets the model learn domain-to-domain translations... -
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MultiPathNet
A Torch implementation of the object detection network
MultiPathNet is a Torch-7 implementation of the “A MultiPath Network for Object Detection” paper (BMVC 2016), developed by Facebook AI Research. It extends the Fast R-CNN framework by introducing multiple network “paths” to enhance feature extraction and object recognition robustness. The MultiPath architecture incorporates skip connections and multi-scale processing to capture both fine-grained details and high-level context within a single detection pipeline. This results in improved...
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