By 冯利芳. 2020
529 citations recorded by the source corpus.
As preservers of history and the objects that others leave behind, it is easy to get distracted by a desire for those objects to be “perfect.” We worry about every little tear, stain or blemish, and we want nothing more than to return objects to their "original state." However, so many times those rough spots are part of the item’s history. It is important to understand the difference between the types of condition issues on objects that cause structural issues versus those that are purely cosmetic. Knowing and identifying these differences can help when determining whether or not an item requires conservation or deciding simple factors like the best way to store or exhibit an item.
Subfields: Social Sciences & Humanities
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Citations in the corpus
- Extracting Training Data from Large Language Models (2020, background): “Private machine learning models can be trained with variants of the differentially private stochastic gradient descent (DP-SGD) algorithm [1] which is widely implemented [17,25].”
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- Data Movement Is All You Need: A Case Study on Optimizing Transformers (2020, methodology): “Frameworks for inference, including TensorRT (NVIDIA, 2020b), Caffe2 (Facebook, 2020), and the ONNX Run-time (Microsoft, 2020), all help to enable a suite of optimizations primarily applicable during inference.”
- Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019, background): “3 Optimized implementations for the allreduce op. include Nvidia’s NCCL [16] and Facebook’s gloo [7].”
- Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “Facebook conducts extensive deep learning research in their Facebook AI Research (FAIR) [153] lab for image recognition and natural language understanding.”
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