
ProtographBased Design for QC Polar Codes
We propose a new family of polar coding which realizes high coding gain,...
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Distributed Coding of Quantized Random Projections
In this paper we propose a new framework for distributed source coding o...
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Universal Physiological Representation Learning with SoftDisentangled Rateless Autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of ...
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Disentangled Adversarial Autoencoder for SubjectInvariant Physiological Feature Extraction
Recent developments in biosignal processing have enabled users to exploi...
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Robust Machine Learning via Privacy/RateDistortion Theory
Robust machine learning formulations have emerged to address the prevale...
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AutoBayes: Automated Inference via Bayesian Graph Exploration for NuisanceRobust Biosignal Analysis
Learning data representations that capture taskrelated features, but ar...
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Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
In typical point cloud delivery, a sender uses octreebased digital vide...
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Huffmancoded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables
In this paper, we study amplitude shaping schemes for the probabilistic ...
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Analysis of Nonlinear Fiber Interactions for FiniteLength ConstantComposition Sequences
In order to realize probabilistically shaped signaling within the probab...
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Stochastic Bottleneck: Rateless AutoEncoder for Flexible Dimensionality Reduction
We propose a new concept of rateless autoencoders (RLAEs) that enable ...
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Disentangled Adversarial Transfer Learning for Physiological Biosignals
Recent developments in wearable sensors demonstrate promising results fo...
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LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Modern face alignment methods have become quite accurate at predicting t...
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Neural Turbo Equalization: Deep Learning for FiberOptic Nonlinearity Compensation
Recently, datadriven approaches motivated by modern deep learning have ...
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Adversarial Deep Learning in EEG Biometrics
Deep learning methods for person identification based on electroencephal...
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Deep LearningBased Constellation Optimization for Physical Network Coding in TwoWay Relay Networks
This paper studies a new application of deep learning (DL) for optimizin...
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Learning to Modulate for Noncoherent MIMO
The deep learning trend has recently impacted a variety of fields, inclu...
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Polar Coding with Chemical Reaction Networks
In this paper, we propose a new polar coding scheme with molecular progr...
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HoloCast: Graph Signal Processing for Graceful Point Cloud Delivery
In conventional point cloud delivery, a sender uses octreebased digital...
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Channel Decoding with Quantum Approximate Optimization Algorithm
Motivated by the recent advancement of quantum processors, we investigat...
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Transfer Learning in BrainComputer Interfaces with Adversarial Variational Autoencoders
We introduce adversarial neural networks for representation learning as ...
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Invariant Representations from Adversarially Censored Autoencoders
We combine conditional variational autoencoders (VAE) with adversarial c...
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Toshiaki KoikeAkino
verfied profileToshiaki KoikeAkino received the B.S. degree in electrical and electronics engineering, M.S. and Ph.D. degrees in communications and computer engineering from Kyoto University, Japan, in 2002, 2003, and 2005, respectively. During 2006–2010, he was a Postdoctoral Research Fellow at Harvard University, and joined Mitsubishi Electric Research Laboratories, Cambridge, MA, USA, in 2010. His research interest includes digital signal processing for data communications and sensing.