26 citations · 50 across the 18 of their papers we have counts for
6 papers · 1 filter
The Implicit Bias of Hyperbolic Representation Learning for Multiclass Data: A Busemann Risk Perspective
Xingrun Li, Sho Kuno, Yusuke Mukuta +2
We study the implicit bias of Riemannian gradient flow for hyperbolic multiclass classification with fixed class prototypes in hyperbolic space . Our framework accomm…
Representation Learning: An Intrinsic Mixed-Curvature Space with Higher Curvature Capacities and Deeper Order-Aware Composition
Xingrun Li, Yusuke Mukuta, Xin Yang +2
Mixed-curvature representation learning seeks to capture rich geometric structures that cannot be adequately modeled by a single curvature regime. Existing approaches largely rely…
HiPerformer: Hierarchically Permutation-Equivariant Transformer for Time Series Forecasting
Ryo Umagami, Yu Ono, Yusuke Mukuta +1
It is imperative to discern the relationships between multiple time series for accurate forecasting. In particular, for stock prices, components are often divided into groups with…
Grouped self-attention mechanism for a memory-efficient Transformer
Bumjun Jung, Yusuke Mukuta, Tatsuya Harada
Time-series data analysis is important because numerous real-world tasks such as forecasting weather, electricity consumption, and stock market involve predicting data that vary ov…
Learning from Label Proportions with Instance-wise Consistency
Ryoma Kobayashi, Yusuke Mukuta, Tatsuya Harada
Learning from Label Proportions (LLP) is a weakly supervised learning method that aims to perform instance classification from training data consisting of pairs of bags containing…
Hyperbolic Neural Networks++
Ryohei Shimizu, Yusuke Mukuta, Tatsuya Harada
Hyperbolic spaces, which have the capacity to embed tree structures without distortion owing to their exponential volume growth, have recently been applied to machine learning to b…