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