Search Results for author:
Found 9 papers, 4 papers with code
Weakly Supervised Multi-Task Representation Learning for Human Activity Analysis Using Wearables
Sensor data streams from wearable devices and smart environments are widely studied in areas like human activity recognition (HAR), person identification, or health monitoring.
Unsupervised Embedding Learning for Human Activity Recognition Using Wearable Sensor Data
The embedded sensors in widely used smartphones and other wearable devices make the data of human activities more accessible.
Siamese Networks for Weakly Supervised Human Activity Recognition
Deep learning has been successfully applied to human activity recognition.
Histopathology Slide Indexing and Search: Are We There Yet?
The search and retrieval of digital histopathology slides is an important task that has yet to be solved.
Clinically Relevant Latent Space Embedding of Cancer Histopathology Slides through Variational Autoencoder Based Image Compression
We generate and visualize embeddings from the compressed latent space and demonstrate how they are useful for clinical interpretation of data, and how in the future such latent embeddings can be used to accelerate search of clinical imaging data.
Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts
Learning a control policy capable of adapting to time-varying and potentially evolving system dynamics has been a great challenge to the mainstream reinforcement learning (RL).
A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels
We validate these claims by generating a new experimental spatial proteomics data set from human lung adenocarcinoma tissue sections and show that a model trained on HuBMAP can accurately synthesize channels from our new data set.
Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization
We introduce a family of fairness enhancing regularization components that we use in conjunction with the traditional binary-cross-entropy based accuracy loss.
Learning the Next Best View for 3D Point Clouds via Topological Features
In this paper, we introduce a reinforcement learning approach utilizing a novel topology-based information gain metric for directing the next best view of a noisy 3D sensor.
