62 citations · 104 across the 17 of their papers we have counts for
18 papers
Response of Confined Mesoscale Flames to Acoustic Forcing
Akhil Aravind, Balasundaram Mohan, Saptarshi Basu
Mesoscale combustion plays a vital role in applications ranging from portable power generation to micro-propulsion systems. This study investigates the response of mesoscale flames…
Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers for Text-to-Image Generation
Yuxuan Yao, Yuxuan Chen, Hui Li +6
Multimodal Diffusion Transformers (MMDiTs) for text-to-image generation maintain separate text and image branches, with bidirectional information flow between text tokens and visua…
Detection of Global Anomalies on Distributed IoT Edges with Device-to-Device Communication
Hideya Ochiai, Riku Nishihata, Eisuke Tomiyama +2
Anomaly detection is an important function in IoT applications for finding outliers caused by abnormal events. Anomaly detection sometimes comes with high-frequency data sampling w…
Remembering Transformer for Continual Learning
Yuwei Sun, Ippei Fujisawa, Arthur Juliani +2
Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task learning interferes with previously learned knowledge. Existing data f…
Associative Transformer
Yuwei Sun, Hideya Ochiai, Zhirong Wu +2
Emerging from the pairwise attention in conventional Transformers, there is a growing interest in sparse attention mechanisms that align more closely with localized, contextual lea…
Meta Neural Coordination
Yuwei Sun
Meta-learning aims to develop algorithms that can learn from other learning algorithms to adapt to new and changing environments. This requires a model of how other learning algori…
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space
Yuwei Sun, Hideya Ochiai, Jun Sakuma
Trojan attacks embed perturbations in input data leading to malicious behavior in neural network models. A combination of various Trojans in different modalities enables an adversa…
Meta Learning in Decentralized Neural Networks: Towards More General AI
Yuwei Sun
Meta-learning usually refers to a learning algorithm that learns from other learning algorithms. The problem of uncertainty in the predictions of neural networks shows that the wor…
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks
Naoya Tezuka, Hideya Ochiai, Yuwei Sun +1
Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to con…
Bidirectional Contrastive Split Learning for Visual Question Answering
Yuwei Sun, Hideya Ochiai
Visual Question Answering (VQA) based on multi-modal data facilitates real-life applications such as home robots and medical diagnoses. One significant challenge is to devise a rob…
Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning
Hideya Ochiai, Yuwei Sun, Qingzhe Jin +2
Privacy-sensitive data is stored in autonomous vehicles, smart devices, or sensor nodes that can move around with making opportunistic contact with each other. Federation among suc…
Feature Distribution Matching for Federated Domain Generalization
Yuwei Sun, Ng Chong, Hideya Ochiai
Multi-source domain adaptation has been intensively studied. The distribution shift in features inherent to specific domains causes the negative transfer problem, degrading a model…
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis
Yuwei Sun, Hideya Ochiai, Jun Sakuma
Model poisoning attacks on federated learning (FL) intrude in the entire system via compromising an edge model, resulting in malfunctioning of machine learning models. Such comprom…
Homogeneous Learning: Self-Attention Decentralized Deep Learning
Yuwei Sun, Hideya Ochiai
Federated learning (FL) has been facilitating privacy-preserving deep learning in many walks of life such as medical image classification, network intrusion detection, and so forth…
Federated Phish Bowl: LSTM-Based Decentralized Phishing Email Detection
Yuwei Sun, Ng Chong, Hideya Ochiai
With increasingly more sophisticated phishing campaigns in recent years, phishing emails lure people using more legitimate-looking personal contexts. To tackle this problem, instea…
Suspicious ARP Activity Detection and Clustering Based on Autoencoder Neural Networks
Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
The rapidly increasing number of smart devices on the Internet necessitates an efficient inspection system for safeguarding our networks from suspicious activities such as Address…
Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks
Yuwei Sun, Ng Chong, Hideya Ochiai
An attack on deep learning systems where intelligent machines collaborate to solve problems could cause a node in the network to make a mistake on a critical judgment. At the same…
Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness
Yuwei Sun, Hideya Ochiai, Hiroshi Esaki
Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology. Decentralized deep learning (DDL) s…