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cotillon    
n. 一种活泼轻快的舞

一种活泼轻快的舞

Cotillon \Co`til`lon"\ (k[-o]`t[-e]`y[^o]N" or k[-o]`t[-e]l`-;
277), Cotillion \Co*til"lion\ (k[-o]*t[i^]l"y[u^]n), n. [F.
cotillon, fr. OF. cote coat, LL. cotta tunic. See {Coat}.]
1. A brisk dance, performed by eight persons; a quadrille.
[1913 Webster]

2. A tune which regulates the dance.
[1913 Webster]

3. A kind of woolen material for women's skirts.

4. A formal ball, especially one at which debutantes are
first presented to society.
[1913 Webster PJC]


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  • Kimi新作《Attention Residuals》：对Transformer中残差 . . .
    二、 核心技术 深度方向的伪查询注意力 （Depth-Wise Attention with Pseudo-Query） 在具体的技术设计上，AttnRes 并没有使用极其复杂的注意力计算公式，而是为每一层 l 引入了一个 单一的可学习伪查询向量（pseudo-query w_l \in \mathbb {R}^d）。 该查询向量 w_l 会与前面层的输出计算点积注意力，生成注意力权重
  • Attention Residuals - arXiv. org
    Through a unified structured-matrix analysis, we show that standard residuals and prior recurrence-based variants correspond to depth-wise linear attention, while AttnRes performs depth-wise softmax attention
  • Attention Residuals - ArXivIQ
    By reframing depth-wise aggregation as an attention problem, they have engineered a scalable, drop-in replacement that bounds state growth, harmonizes gradient flow, and wrings superior performance out of identical parameter counts
  • GitHub - MoonshotAI Attention-Residuals
    This is the official repository for Attention Residuals (AttnRes), a drop-in replacement for standard residual connections in Transformers that enables each layer to selectively aggregate earlier representations via learned, input-dependent attention over depth
  • Attention Residuals in Deep Language Models
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  • Attention Residuals: The Long-Overdue Upgrade to How Neural Networks . . .
    Instead of adding layer outputs together with unit weights, Attention Residuals (AttnRes) use a learned softmax attention mechanism over the depth dimension The hidden state entering layer l is computed as: A few design choices deserve attention: Pseudo-query, not full query
  • Attention Residuals: What If Your Network Could Choose Which Layer to . . .
    The depth-sequence duality framing is the paper's most intellectually satisfying contribution The history of deep learning is full of insights migrating from one dimension to another and this one has a clean story: RNNs over time became transformers, and now uniform residuals over depth are becoming depth-wise attention
  • Attention Residuals: Fixing Signal Dilution in the Depth Dimension of . . .
    All pseudo-query vectors are initialized to zero, so at the start of training, attention weights are uniform, equivalent to standard residual connections, avoiding early training instability The parameter overhead is minimal: only one additional learned vector and one normalization per layer
  • Kimi. ai: Rewiring Deep Learning with Attention Residuals
    Rethinking depth-wise aggregation to solve hidden-state dilution and unlock smarter, more efficient scaling in large language models The Problem: Standard residual connections blindly accumulate information with fixed, uniform weights, causing hidden-state growth that progressively dilutes the contributions of early layers in deep networks The Innovation: Attention Residuals (AttnRes) treats
  • Attention Residuals Explained: Rethinking Transformer Depth
    Learn how Attention Residuals rethink depth in Transformers by replacing uniform residual accumulation with selective, attention-based aggregation





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