Search Results for author: Takuya Akiba

Found 12 papers, 6 papers with code

Evolutionary Optimization of Model Merging Recipes

1 code implementation19 Mar 2024 Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha

Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks.

Evolutionary Algorithms Math

Team PFDet's Methods for Open Images Challenge 2019

no code implementations25 Oct 2019 Yusuke Niitani, Toru Ogawa, Shuji Suzuki, Takuya Akiba, Tommi Kerola, Kohei Ozaki, Shotaro Sano

Using this method, the team PFDet achieved 3rd and 4th place in the instance segmentation and the object detection track, respectively.

Instance Segmentation Object +4

Optuna: A Next-generation Hyperparameter Optimization Framework

11 code implementations25 Jul 2019 Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama

We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications.

Distributed Computing Hyperparameter Optimization

A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation

3 code implementations NeurIPS 2019 Mitsuru Kusumoto, Takuya Inoue, Gentaro Watanabe, Takuya Akiba, Masanori Koyama

Recomputation algorithms collectively refer to a family of methods that aims to reduce the memory consumption of the backpropagation by selectively discarding the intermediate results of the forward propagation and recomputing the discarded results as needed.

Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects

no code implementations CVPR 2019 Yusuke Niitani, Takuya Akiba, Tommi Kerola, Toru Ogawa, Shotaro Sano, Shuji Suzuki

However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector.

object-detection Object Detection

Adversarial Attacks and Defences Competition

1 code implementation31 Mar 2018 Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jian-Yu Wang, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov, Takuya Akiba, Seiya Tokui, Motoki Abe

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.

BIG-bench Machine Learning

Variance-based Gradient Compression for Efficient Distributed Deep Learning

no code implementations ICLR 2018 Yusuke Tsuzuku, Hiroto Imachi, Takuya Akiba

We also analyze the efficiency using computation and communication cost models and provide the evidence that this method enables distributed deep learning for many scenarios with commodity environments.

ShakeDrop Regularization for Deep Residual Learning

5 code implementations7 Feb 2018 Yoshihiro Yamada, Masakazu Iwamura, Takuya Akiba, Koichi Kise

In this paper, to relieve the overfitting effect of ResNet and its improvements (i. e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization.

Extremely Large Minibatch SGD: Training ResNet-50 on ImageNet in 15 Minutes

no code implementations12 Nov 2017 Takuya Akiba, Shuji Suzuki, Keisuke Fukuda

We demonstrate that training ResNet-50 on ImageNet for 90 epochs can be achieved in 15 minutes with 1024 Tesla P100 GPUs.

ChainerMN: Scalable Distributed Deep Learning Framework

1 code implementation31 Oct 2017 Takuya Akiba, Keisuke Fukuda, Shuji Suzuki

One of the keys for deep learning to have made a breakthrough in various fields was to utilize high computing powers centering around GPUs.

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