102 citations · 331 across the 58 of their papers we have counts for
13 papers · 1 filter
Distributed Coding of Quantized Random Projections
Maxim Goukhshtein, Petros T. Boufounos, Toshiaki Koike-Akino +1
In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of…
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Mo Han, Ozan Ozdenizci, Toshiaki Koike-Akino +2
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological st…
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Mo Han, Ozan Ozdenizci, Ye Wang +2
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner. One major challenge of…
Huffman-Coded Sphere Shaping for Extended-Reach Single-Span Links
Pavel Skvortcov, Ian Phillips, Wladek Forysiak +4
Huffman-coded sphere shaping (HCSS) is an algorithm for finite-length probabilistic constellation shaping, which provides nearly optimal energy efficiency at low implementation com…
Robust Machine Learning via Privacy/Rate-Distortion Theory
Ye Wang, Shuchin Aeron, Adnan Siraj Rakin +2
Robust machine learning formulations have emerged to address the prevalent vulnerability of deep neural networks to adversarial examples. Our work draws the connection between opti…
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Andac Demir, Toshiaki Koike-Akino, Ye Wang +1
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning. We introduce an automated Ba…
Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
Takuya Fujihashi, Toshiaki Koike-Akino, Siheng Chen +1
In typical point cloud delivery, a sender uses octree-based digital video compression to send three-dimensional (3D) points and color attributes over band-limited links. However, t…
Huffman-coded Sphere Shaping and Distribution Matching Algorithms via Lookup Tables
Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino +3
In this paper, we study amplitude shaping schemes for the probabilistic amplitude shaping (PAS) framework as well as algorithms for constant-composition distribution matching (CCDM…
Analysis of Nonlinear Fiber Interactions for Finite-Length Constant-Composition Sequences
Tobias Fehenberger, David S. Millar, Toshiaki Koike-Akino +3
In order to realize probabilistically shaped signaling within the probabilistic amplitude shaping (PAS) framework, a shaping device outputs sequences that follow a certain nonunifo…
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction
Toshiaki Koike-Akino, Ye Wang
We propose a new concept of rateless auto-encoders (RL-AEs) that enable a flexible latent dimensionality, which can be seamlessly adjusted for varying distortion and dimensionality…
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Mo Han, Ozan Ozdenizci, Ye Wang +2
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological st…
LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
Abhinav Kumar, Tim K. Marks, Wenxuan Mou +6
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted loca…
Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter
Yingheng Tang, Keisuke Kojima, Toshiaki Koike-Akino +6
We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm…