activity
20222025
most citedDirected Graph Auto-Encoders

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

eess.SY2025

Cross-Process Defect Attribution using Potential Loss Analysis

Tsuyoshi Idé, Kohei Miyaguchi

Cross-process root-cause analysis of wafer defects is among the most critical yet challenging tasks in semiconductor manufacturing due to the heterogeneity and combinatorial nature…

cs.LG2024

Improving Transformers using Faithful Positional Encoding

Tsuyoshi Idé, Jokin Labaien, Pin-Yu Chen

We propose a new positional encoding method for a neural network architecture called the Transformer. Unlike the standard sinusoidal positional encoding, our approach is based on s…

cs.LG2024

Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis

Tsuyoshi Idé, Dzung T. Phan, Rudy Raymond

This paper presents two methodological advancements in decentralized multi-task learning under privacy constraints, aiming to pave the way for future developments in next-generatio…

cs.LG2024

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

Dongxia Wu, Tsuyoshi Idé, Aurélie Lozano +5

We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level c…

cs.LG20221 cited

Directed Graph Auto-Encoders

Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé +2

We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns…