Search Results for author:
Found 18 papers, 6 papers with code
Infographics Wizard: Flexible Infographics Authoring and Design Exploration
With the help of designers, we propose a semi-automated infographic framework for general structured and flow-based infographic design generation.
Cascaded Debiasing : Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions
Lastly, we offer a list of combinations of interventions that perform best for different fairness and utility metrics to aid the design of fair ML pipelines.
User-Centric Semi-Automated Infographics Authoring and Recommendation
Based on the framework, we also propose an interactive tool, \name{}, for assisting novice designers with creating high-quality infographics from an input in a markdown format by offering recommendations of different design components of infographics.
Fluent: An AI Augmented Writing Tool for People who Stutter
On hovering over any such word, Fluent presents a set of alternative words which have similar meaning but are easier to speak.
Transforming the Latent Space of StyleGAN for Real Face Editing
But more importantly, the proposed $W$++ space achieves superior performance in both reconstruction quality and editing quality.
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning
Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics.
WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings
In this work, we present WordBias, an interactive visual tool designed to explore biases against intersectional groups encoded in static word embeddings.
Noise Entangled GAN For Low-Dose CT Simulation
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image.
Visual Steering for One-Shot Deep Neural Network Synthesis
To deal with the problem, we formulate the task of neural network architecture optimization as a graph space exploration, based on the one-shot architecture search technique.
Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation
The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model.
Measuring Social Biases of Crowd Workers using Counterfactual Queries
Social biases based on gender, race, etc.
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience
We conducted an empirical study comparing the model learning outcomes, feedback content and experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).
Interpreting Galaxy Deblender GAN from the Discriminator's Perspective
Using the Galaxy Zoo dataset we demonstrate that our method clearly reveals attention areas of the Discriminator when differentiating generated galaxy images from ground truth images.
Does Speech enhancement of publicly available data help build robust Speech Recognition Systems?
Thereafter, we tried to investigate if we can use publicly available noisy data to train robust ASR systems.
Task Classification Model for Visual Fixation, Exploration, and Search
Yarbus' claim to decode the observer's task from eye movements has received mixed reactions.
Metal Artifact Reduction in Cone-Beam X-Ray CT via Ray Profile Correction
We tested the proposed method with two clinical datasets that were both obtained during spine surgery.
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation
In this paper, we propose a novel two-step understanding method, namely Salient Relevance (SR) map, which aims to shed light on how deep CNNs recognize images and learn features from areas, referred to as attention areas, therein.
Visualizing Linguistic Shift
Neural network based models are a very powerful tool for creating word embeddings, the objective of these models is to group similar words together.
