Existing frameworks for image stitching often provide visually reasonabl...
Learning implicit templates as neural fields has recently shown impressi...
Despite decades of efforts to resolve, memory safety violations are stil...
The naphtha cracking process heavily relies on the composition of naphth...
We present a learning framework for reconstructing neural scene
represen...
Multi-resolution hash encoding has recently been proposed to reduce the
...
Recent studies have proven that DNNs, unlike human vision, tend to explo...
Image warping aims to reshape images defined on rectangular grids into
a...
We devise a performance model for GPU training of Deep Learning
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As CUDA programs become the de facto program among data parallel applica...
Recent works with an implicit neural function shed light on representing...
Despite the extensive usage of point clouds in 3D vision, relatively lim...
With the rapid development of scientific computation, more and more
rese...
In this paper, we provide a deep dive into the deployment of inference
a...
Deep learning (DL) relies on massive amounts of labeled data, and improv...
Recurrent Neural Network Language Models (RNNLMs) have started to be use...
This paper presents methods to accelerate recurrent neural network based...