Search Results for author:
Found 9 papers, 5 papers with code
Utilizing Large Language Models for Natural Interface to Pharmacology Databases
The drug development process necessitates that pharmacologists undertake various tasks, such as reviewing literature, formulating hypotheses, designing experiments, and interpreting results.
Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector
As the size of a table increases with detector volume for a fixed resolution, this method scales poorly for future larger detectors.
clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP
We introduce a new method to efficiently create text-to-image models from a pre-trained CLIP and StyleGAN.
Multiple Pairwise Ranking Networks for Personalized Video Summarization
In this paper, we investigate video summarization in the supervised setting.
NPRportrait 1.0: A Three-Level Benchmark for Non-Photorealistic Rendering of Portraits
Despite the recent upsurge of activity in image-based non-photorealistic rendering (NPR), and in particular portrait image stylisation, due to the advent of neural style transfer, the state of performance evaluation in this field is limited, especially compared to the norms in the computer vision and machine learning communities.
HoloGAN: Unsupervised learning of 3D representations from natural images
This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes.
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis.
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images.
