354 citations · 1.1k across the 23 of their papers we have counts for
6 papers · 1 filter
GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction
Pengyuan Lyu, Weihong Ma, Hongyi Wang +5
All tables can be represented as grids. Based on this observation, we propose GridFormer, a novel approach for interpreting unconstrained table structures by predicting the vertex…
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
Samuel Horvath, Stefanos Laskaridis, Shashank Rajput +1
Deep Neural Networks (DNNs) have been a large driver for AI breakthroughs in recent years. However, these models have been getting increasingly large as they become more accurate a…
Cuttlefish: Low-Rank Model Training without All the Tuning
Hongyi Wang, Saurabh Agarwal, Pongsakorn U-chupala +3
Recent research has shown that training low-rank neural networks can effectively reduce the total number of trainable parameters without sacrificing predictive accuracy, resulting…
Memory-adaptive Depth-wise Heterogeneous Federated Learning
Kai Zhang, Yutong Dai, Hongyi Wang +3
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data. However, the presence of heterogeneous devi…
Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Han Guo, Philip Greengard, Hongyi Wang +3
The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decompo…
Does compressing activations help model parallel training?
Song Bian, Dacheng Li, Hongyi Wang +2
Large-scale Transformer models are known for their exceptional performance in a range of tasks, but training them can be difficult due to the requirement for communication-intensiv…