Transformer

By Mukund R. Patel. 2020

Absorption Index (AI) remains valid for old-aged unsealed transformers as a simple and effective method of non-destructive control insulation. The reasons for AI decrease within transformer operation are insulation moistening and contamination. Seven gradation levels of the insulation condition and algorithm of the operating procedures are proposed depending on the value of the measured AI and its variation in time. Along with AI, it is recommended to measure the polarisation index (PI) and the PI-2 (R 600 /R 15 ratio).

Subfields: Model Architectures

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In Model Architectures

1. Jianfeng Gao
2. Jianwei Yang
3. Lu Yuan
4. Chunyuan Li
5. Bin Xiao
6. Xipeng Qiu
7. Pengchuan Zhang
8. Xiyang Dai

Citations in the corpus

• Generating Diverse High-Fidelity Images with VQ-VAE-2 (2019, methodology): “Deep autoregressive models are common probabilistic models that achieve state of the art results in density estimation across several data modalities [24, 6, 23, 22].”
• How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers (2021, background): “In (26, 17, 41, 43) the authors propose hierarchical versions of ViT.”
• Audiobox: Unified Audio Generation with Natural Language Prompts (2023, neutral): “…and music generation: Text-to-sound (Kreuk et al., 2022) and text-to-music (Schneider et al., 2023) are the emerging paradigms for general-domain sound and music generation, in contrast to earlier studies that generate finite sound effects (Donahue et al., 2018) or instruments (Huang et al., 2018).”
• Focal Self-attention for Local-Global Interactions in Vision Transformers (2021, methodology): “On one hand, a number of works in both NLP and CV resort to coarse-grained global self-attention by attending the downsampled/summarized tokens, while preserving the long-range interactions [50, 47, 63, 67, 32].”
• General-purpose, long-context autoregressive modeling with Perceiver AR (2022, neutral): “(Wang, 2021).”
• Focal Attention for Long-Range Interactions in Vision Transformers (2021, background): “There are also increasing number of studies for improving ViT via data-efficient training [55], improved patch embedding/encoding [16, 71, 31], integrating convolutional projections into transformers [64, 70], and using multi-scale architectures and efficient self-attention mechanisms for…”
• A Survey of Transformers (2021, methodology): “To address this issue, Transformer in Transformer (TNT) [48] uses at each layer an inner Transformer block that transforms pixel representations and an outer Transformer block that takes fused vectors of patch representations and pixel representations as input.”
• PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer (2021, unknown): “Many other ViT variants [8, 13, 21, 22, 25, 37, 53, 59, 69] are proposed from then, which achieve promising performance compared with its counterpart CNNs for image analysis tasks [6, 23, 73].”