4 citations · 22 across the 33 of their papers we have counts for
13 papers · 1 filter
Explicit Dropout: Deterministic Regularization for Transformer Architectures
Vidhi Agrawal, Illia Oleksiienko, Alexandros Iosifidis
Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explicit optimization objectives.…
DeepCoT: Deep Continual Transformers for Real-Time Inference on Data Streams
Ginés Carreto Picón, Peng Yuan Zhou, Qi Zhang +1
Transformer-based models have dramatically increased their size and parameter count to tackle increasingly complex tasks. At the same time, there is a growing demand for high perfo…
InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation
Sanyam Jain, Khuram Naveed, Illia Oleksiienko +2
This work introduces InJecteD, a framework for interpreting Denoising Diffusion Probabilistic Models (DDPMs) by analyzing sample trajectories during the denoising process of 2D poi…
PRISM: Distributed Inference for Foundation Models at Edge
Muhammad Azlan Qazi, Alexandros Iosifidis, Qi Zhang
Foundation models (FMs) have achieved remarkable success across a wide range of applications, from image classification to natural langurage processing, but pose significant challe…
Variational Graph Convolutional Neural Networks
Illia Oleksiienko, Juho Kanniainen, Alexandros Iosifidis
Estimation of model uncertainty can help improve the explainability of Graph Convolutional Networks and the accuracy of the models at the same time. Uncertainty can also be used in…
Cryptocurrency Portfolio Optimization by Neural Networks
Quoc Minh Nguyen, Dat Thanh Tran, Juho Kanniainen +2
Many cryptocurrency brokers nowadays offer a variety of derivative assets that allow traders to perform hedging or speculation. This paper proposes an effective algorithm based on…