Summary of Mm1: Methods, Analysis & Insights From Multimodal Llm Pre-training, by Brandon Mckinzie et al.
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-trainingby Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier,…
Summary of Unleashing the Power Of Meta-tuning For Few-shot Generalization Through Sparse Interpolated Experts, by Shengzhuang Chen et al.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Expertsby Shengzhuang Chen, Jihoon…
Summary of Scattered Mixture-of-experts Implementation, by Shawn Tan et al.
Scattered Mixture-of-Experts Implementationby Shawn Tan, Yikang Shen, Rameswar Panda, Aaron CourvilleFirst submitted to arxiv on:…
Summary of Conditional Computation in Neural Networks: Principles and Research Trends, by Simone Scardapane et al.
Conditional computation in neural networks: principles and research trendsby Simone Scardapane, Alessandro Baiocchi, Alessio Devoto,…
Summary of Harder Tasks Need More Experts: Dynamic Routing in Moe Models, by Quzhe Huang et al.
Harder Tasks Need More Experts: Dynamic Routing in MoE Modelsby Quzhe Huang, Zhenwei An, Nan…
Summary of Acquiring Diverse Skills Using Curriculum Reinforcement Learning with Mixture Of Experts, by Onur Celik et al.
Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Expertsby Onur Celik, Aleksandar Taranovic,…
Summary of Video Relationship Detection Using Mixture Of Experts, by Ala Shaabana and Zahra Gharaee and Paul Fieguth
Video Relationship Detection Using Mixture of Expertsby Ala Shaabana, Zahra Gharaee, Paul FieguthFirst submitted to…
Summary of Testam: a Time-enhanced Spatio-temporal Attention Model with Mixture Of Experts, by Hyunwook Lee et al.
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Expertsby Hyunwook Lee, Sungahn KoFirst submitted…
Summary of Enhancing the “immunity” Of Mixture-of-experts Networks For Adversarial Defense, by Qiao Han et al.
Enhancing the “Immunity” of Mixture-of-Experts Networks for Adversarial Defenseby Qiao Han, yong huang, xinling Guo,…
Summary of M2mkd: Module-to-module Knowledge Distillation For Modular Transformers, by Ka Man Lo et al.
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformersby Ka Man Lo, Yiming Liang, Wenyu Du, Yuantao…