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Found 6 papers, 3 papers with code
A Lightweight Inception Boosted U-Net Neural Network for Routability Prediction
As the modern CPU, GPU, and NPU chip design complexity and transistor counts keep increasing, and with the relentless shrinking of semiconductor technology nodes to nearly 1 nanometer, the placement and routing have gradually become the two most pivotal processes in modern very-large-scale-integrated (VLSI) circuit back-end design.
An Augmented Transformer Architecture for Natural Language Generation Tasks
The Transformer based neural networks have been showing significant advantages on most evaluations of various natural language processing and other sequence-to-sequence tasks due to its inherent architecture based superiorities.
Image Super-resolution via Feature-augmented Random Forest
In this paper, we present a novel feature-augmented random forest (FARF) for image super-resolution, where the conventional gradient-based features are augmented with gradient magnitudes and different feature recipes are formulated on different stages in an RF.
Ranked #48 on
Image Super-Resolution
on BSD100 - 4x upscaling
Joint Maximum Purity Forest with Application to Image Super-Resolution
In the JMPF scheme, the original feature space is transformed into a compactly pre-clustered feature space, via a trained rotation matrix.
Ranked #50 on
Image Super-Resolution
on BSD100 - 4x upscaling
Efficient Likelihood Bayesian Constrained Local Model
The constrained local model (CLM) proposes a paradigm that the locations of a set of local landmark detectors are constrained to lie in a subspace, spanned by a shape point distribution model (PDM).
Cascaded Face Alignment via Intimacy Definition Feature
In this paper, we present a random-forest based fast cascaded regression model for face alignment, via a novel local feature.
