TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions
Summary
TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions is a scholarly article[1].
Key Facts
TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions. Retrieved May 24, 2026, from https://4ort.xyz/entity/ternarynet-faster-deep-model-inference-without-gpus-for-medical-3d-segmentation-using-sparse-and-binary-convolutions
MLA“TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/ternarynet-faster-deep-model-inference-without-gpus-for-medical-3d-segmentation-using-sparse-and-binary-convolutions.
BibTeX@misc{4ortxyz_ternarynet-faster-deep-model-inference-without-gpus-for-medical-3d-segmentation-using-sparse-and-binary-convolutions_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions}}, year = {2026}, url = {https://4ort.xyz/entity/ternarynet-faster-deep-model-inference-without-gpus-for-medical-3d-segmentation-using-sparse-and-binary-convolutions}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): TernaryNet: faster deep model inference without GPUs for medical 3D segmentation using sparse and binary convolutions — https://4ort.xyz/entity/ternarynet-faster-deep-model-inference-without-gpus-for-medical-3d-segmentation-using-sparse-and-binary-convolutions (retrieved 2026-05-24)
