Search Results for author:
Found 10 papers, 2 papers with code
Compressive Neural Representations of Volumetric Scalar Fields
We present an approach for compressing volumetric scalar fields using implicit neural representations.
Visually Analyzing and Steering Zero Shot Learning
We propose a visual analytics system to help a user analyze and steer zero-shot learning models.
Visually Analyzing Contextualized Embeddings
In this paper we introduce a method for visually analyzing contextualized embeddings produced by deep neural network-based language models.
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models
We use Attention Flows to study attention mechanisms in various sentence understanding tasks and highlight how attention evolves to address the nuances of solving these tasks.
Domain Adaptation for Ultrasound Beamforming
We address this challenge by extending cycle-consistent generative adversarial networks, where we leverage maps between synthetic simulation and real in vivo domains to ensure that the learned beamformers capture the distribution of both noisy and clean in vivo data.
Visual Supervision in Bootstrapped Information Extraction
We challenge a common assumption in active learning, that a list-based interface populated by informative samples provides for efficient and effective data annotation.
A Generative Model for Volume Rendering
Additionally, the generative model transforms transfer functions into a view-invariant latent space specifically designed to synthesize volume-rendered images.
Active Perceptual Similarity Modeling with Auxiliary Information
A common way to learn such a model is from relative comparisons in the form of triplets: responses to queries of the form "Is object a more similar to b than it is to c?".
Efficient Online Relative Comparison Kernel Learning
Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search.
Subspace Tracking under Dynamic Dimensionality for Online Background Subtraction
Long-term modeling of background motion in videos is an important and challenging problem used in numerous applications such as segmentation and event recognition.
