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Found 6 papers, 2 papers with code
EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Prior
This limitation leads to the Janus problem, where multi-faced 3D models are generated under the guidance of such diffusion models.
Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs
Neural Radiance Fields from Sparse input} (NeRF-S) have shown great potential in synthesizing novel views with a limited number of observed viewpoints.
Use of Deterministic Transforms to Design Weight Matrices of a Neural Network
For the existing SSFN, a part of each weight matrix is trained using a layer-wise convex optimization approach (a supervised training), while the other part is chosen as a random matrix instance (an unsupervised training).
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning
We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers).
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University.
Asynchronous Decentralized Learning of a Neural Network
In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network (SSFN) in a decentralized scenario.
