Search Results for author:
Found 11 papers, 10 papers with code
UniSparse: An Intermediate Language for General Sparse Format Customization
The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound.
3D Reconstruction and New View Synthesis of Indoor Environments based on a Dual Neural Radiance Field
One of the innovative features of Du-NeRF is that it decouples a view-independent component from the density field and uses it as a label to supervise the learning process of the SDF field.
PSAvatar: A Point-based Morphable Shape Model for Real-Time Head Avatar Animation with 3D Gaussian Splatting
In this paper, we introduce PSAvatar, a novel framework for animatable head avatar creation that utilizes discrete geometric primitive to create a parametric morphable shape model and employs 3D Gaussian for fine detail representation and high fidelity rendering.
Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks
Computational offloading has become an enabling component for edge intelligence in mobile and smart devices.
Delay-aware Backpressure Routing Using Graph Neural Networks
In this work, we improve upon the widely-used metric of hop distance (and its variants) for the shortest path bias by introducing a bias based on the link duty cycle, which we predict using a graph convolutional neural network.
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks
Distributed scheduling algorithms for throughput or utility maximization in dense wireless multi-hop networks can have overwhelmingly high overhead, causing increased congestion, energy consumption, radio footprint, and security vulnerability.
Delay-Oriented Distributed Scheduling Using Graph Neural Networks
In wireless multi-hop networks, delay is an important metric for many applications.
Link Scheduling using Graph Neural Networks
Test results on medium-sized wireless networks show that our centralized heuristic can reach a near-optimal solution quickly, and our distributed heuristic based on a shallow GCN can reduce by nearly half the suboptimality gap of the distributed greedy solver with minimal increase in complexity.
Distributed Scheduling using Graph Neural Networks
In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.
COMO: A Pipeline for Multi-Omics Data Integration in Metabolic Modeling and Drug Discovery
Identifying potential drug targets using metabolic modeling requires integrating multiple modeling methods and heterogenous biological datasets, which can be challenging without sophisticated tools.
Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-valued Convolutional Networks
The (inverse) discrete Fourier transform (DFT/IDFT) is often perceived as essential to orthogonal frequency-division multiplexing (OFDM) systems.
