13 citations · 19 across the 8 of their papers we have counts for
8 papers
Lookahead Drifting Model
Guoqiang Zhang, Kenta Niwa, W. Bastiaan Kleijn
Recently, a new paradigm named \emph{drifting model} has been proposed for mapping distributions, which achieves the SOTA image generation performance over ImageNet via one-step ne…
FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters
Hiro Ishii, Kenta Niwa, Hiroshi Sawada +3
We propose Federated Preconditioned Mixing (FedPM), a novel Federated Learning (FL) method that leverages second-order optimization. Prior methods--such as LocalNewton, LTDA, and F…
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
Parameter-free Clipped Gradient Descent Meets Polyak
Yuki Takezawa, Han Bao, Ryoma Sato +2
Gradient descent and its variants are de facto standard algorithms for training machine learning models. As gradient descent is sensitive to its hyperparameters, we need to tune th…
Optimal Transport with Cyclic Symmetry
Shoichiro Takeda, Yasunori Akagi, Naoki Marumo +1
We propose novel fast algorithms for optimal transport (OT) utilizing a cyclic symmetry structure of input data. Such OT with cyclic symmetry appears universally in various real-wo…
Embarrassingly Simple Text Watermarks
Ryoma Sato, Yuki Takezawa, Han Bao +2
We propose Easymark, a family of embarrassingly simple yet effective watermarks. Text watermarking is becoming increasingly important with the advent of Large Language Models (LLM)…
Necessary and Sufficient Watermark for Large Language Models
Yuki Takezawa, Ryoma Sato, Han Bao +2
In recent years, large language models (LLMs) have achieved remarkable performances in various NLP tasks. They can generate texts that are indistinguishable from those written by h…
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks
Haimin Zhang, Min Xu, Guoqiang Zhang +1
Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborh…