Letao Wang

Yanqing Lu, Letao Wang, Jinbo Liu

Shampoo with Adam in the Preconditioner's eigenbasis (SOAP) has recently emerged as a promising optimization algorithm for neural network training, achieving superior training efficiency over both Adam and Shampoo in language modeling tasks. In this work, we analyze Adam, Shampoo, and SOAP from the perspective of gradient whitening, interpreting their preconditioners as approximations to the whitening matrix, which captures second-order curvature information. We further establish a theoretical equivalence between idealized versions of SOAP and Shampoo under the Kronecker product assumption. To empirically evaluate these insights, we reproduce the language modeling experiments using nanoGPT and grayscale image colorization. Our results show that SOAP exhibits similar convergence rate as Shampoo, and no significant advantage over both Adam and Shampoo in the final loss achieved, which aligns with their equivalence in theory.

Paola Cascante-Bonilla, Hui Wu, Letao Wang et al.

Existing work on VQA explores data augmentation to achieve better generalization by perturbing the images in the dataset or modifying the existing questions and answers. While these methods exhibit good performance, the diversity of the questions and answers are constrained by the available image set. In this work we explore using synthetic computer-generated data to fully control the visual and language space, allowing us to provide more diverse scenarios. We quantify the effect of synthetic data in real-world VQA benchmarks and to which extent it produces results that generalize to real data. By exploiting 3D and physics simulation platforms, we provide a pipeline to generate synthetic data to expand and replace type-specific questions and answers without risking the exposure of sensitive or personal data that might be present in real images. We offer a comprehensive analysis while expanding existing hyper-realistic datasets to be used for VQA. We also propose Feature Swapping (F-SWAP) -- where we randomly switch object-level features during training to make a VQA model more domain invariant. We show that F-SWAP is effective for enhancing a currently existing VQA dataset of real images without compromising on the accuracy to answer existing questions in the dataset.