1 citations · 1 across the 9 of their papers we have counts for
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Virtual Width Networks
Seed, Baisheng Li, Banggu Wu +115
We introduce Virtual Width Networks (VWN), a framework that delivers the benefits of wider representations without incurring the quadratic cost of increasing the hidden size. VWN d…
SeeDNorm: Self-Rescaled Dynamic Normalization
Wenrui Cai, Defa Zhu, Qingjie Liu +1
Normalization layer constitutes an essential component in neural networks. In transformers, the predominantly used RMSNorm constrains vectors to a unit hypersphere, followed by dim…
UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning
Zihao Huang, Yu Bao, Qiyang Min +8
While Mixture of Experts (MoE) models achieve remarkable efficiency by activating only subsets of parameters, they suffer from high memory access costs during inference. Memory-lay…
Frac-Connections: Fractional Extension of Hyper-Connections
Defa Zhu, Hongzhi Huang, Jundong Zhou +5
Residual connections are central to modern deep learning architectures, enabling the training of very deep networks by mitigating gradient vanishing. Hyper-Connections recently gen…
Ultra-Sparse Memory Network
Zihao Huang, Qiyang Min, Hongzhi Huang +4
It is widely acknowledged that the performance of Transformer models is logarithmically related to their number of parameters and computational complexity. While approaches like Mi…
Hyper-Connections
Defa Zhu, Hongzhi Huang, Zihao Huang +5
We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed…