Learning Sparse Mixture of Experts for Visual Question Answering There has been a rapid progress in the task of Visual Question Answering with improved model architectures. Unfortunately, these models are usually computationally intensive due to their sheer size which poses a serious challenge for ...
We conclude by scaling a sparse model to 269B parameters, with a computational cost comparable to a 32B dense encoder-decoder Transformer (Stable and Transferable Mixture-of-Experts or ST-MoE-32B). For the first time, a sparse model achieves state-of-the-art performance in transfer learning,...
巴比龙 北京邮电大学 计算机科学技术博士 Papers | MoE-LLaVA: Mixture of Experts for Large Vision-Language ModelsFor Large Vision-Language Models (LVLMs), scaling the model can effectively improve performance. However, expanding model parameters significantly increases the training and inferring ...
The data was generated from a 100-dimensional mixture of two Gaussian distribution that satisfies ‖μ1−μ2‖2=3 (with identity covariance matrices). The dictionary size was fixed at 1024. The number of data point was 1000. We compare the proposed smooth sparse coding algorithm, standard ...
Real-world datasets were used to evaluate the presented approach, which confirmed that the model achieved higher diversity than the conventional approaches for a given loss of accuracy. Yan and Tang [32] use a Gaussian mixture model to cluster users and items and extracts new item features to ...