1 citations · 1 across the 5 of their papers we have counts for
5 papers
Dataset Distillation Efficiently Encodes Low-Dimensional Representations from Gradient-Based Learning of Non-Linear Tasks
Yuri Kinoshita, Naoki Nishikawa, Taro Toyoizumi
Dataset distillation, a training-aware data compression technique, has recently attracted increasing attention as an effective tool for mitigating costs of optimization and data st…
Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
Ryotaro Kawata, Kohsei Matsutani, Yuri Kinoshita +2
Mixture of Experts (MoE), an ensemble of specialized models equipped with a router that dynamically distributes each input to appropriate experts, has achieved successful results i…
A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness
Yuri Kinoshita, Taro Toyoizumi
While neural networks can enjoy an outstanding flexibility and exhibit unprecedented performance, the mechanism behind their behavior is still not well-understood. To tackle this f…
Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network
Yuri Kinoshita, Kenta Oono, Kenji Fukumizu +2
Variational autoencoders (VAEs) are one of the deep generative models that have experienced enormous success over the past decades. However, in practice, they suffer from a problem…
Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
Yuri Kinoshita, Taiji Suzuki
The stochastic gradient Langevin Dynamics is one of the most fundamental algorithms to solve sampling problems and non-convex optimization appearing in several machine learning app…