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Found 9 papers, 3 papers with code
Optimizing Electric Vehicle Efficiency with Real-Time Telemetry using Machine Learning
In the contemporary world with degrading natural resources, the urgency of energy efficiency has become imperative due to the conservation and environmental safeguarding.
Reconfigurable Inspection in Manufacturing: State of the Art and Taxonomy
Different Reconfigurable Inspection Machines (RIMs) and their arrangement in an assembly line as an inspection system have been carefully studied and the modern inspection system equipped in RMS has been compared to the traditional techniques commonly used in inspection of product quality.
Neural models for Factual Inconsistency Classification with Explanations
The proposed system first predicts inconsistent spans from claim and context; and then uses them to predict inconsistency types and inconsistent entity types (when inconsistency is due to entities).
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates considerable computational time.
End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data
Cryogenic electron microscopy (cryo-EM) provides images from different copies of the same biomolecule in arbitrary orientations.
Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant
To that end, we first establish a global bound for the Lipschitz constant of neural networks.
Improving Document Classification with Multi-Sense Embeddings
Through extensive experiments on multiple real-world datasets, we show that SCDV-MS embeddings outperform previous state-of-the-art embeddings on multi-class and multi-label text categorization tasks.
Ranked #5 on
Document Classification
on Reuters-21578
(F1 metric)
Time-Dependent Deep Image Prior for Dynamic MRI
The key ingredients of our method are threefold: 1) a fixed low-dimensional manifold that encodes the temporal variations of images; 2) a network that maps the manifold into a more expressive latent space; and 3) a convolutional neural network that generates a dynamic series of MRI images from the latent variables and that favors their consistency with the measurements in k-space.
CNN-Based Projected Gradient Descent for Consistent Image Reconstruction
When the projector is replaced with a CNN, we propose a relaxed PGD, which always converges.
