98 skills found · Page 1 of 4
hofstadter-io / HofA developer experience centered on CUE. Unifies schemas, data models, deterministic and agentic code generation, workflow and task engine, dagger powered environments, coding assistant, and vscode extension; woven together on the CUE lattice. Squint harder if you can't see the cube :]
USTCPCS / CVPR2018 AttentionContext Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark
PorousMediaSimulation / OpenLBMPMopenLBMPM is an open source lattice Boltzmann method (LBM) package for multicomponent and multiphase (MCMP) flow and transport in porous media. Currently, it includes Shan-Chen method and color gradient method for MCMP system. There are two options for Shan-Chen method: (1) Original Shan-Chen method, which integrates the force term to the equilibrium velocity and cannot reach high viscosity ratio; (2) Explicit forcing model developed by M.Porter et al (2012). For color gradient model, the methods developed by Liu et.al (2014), Huang et al (2014) and Takashi et al (2018) are included. For running the codes, CUDA and numba (from Anaconda) are required
issp-center-dev / HPhiQuantum Lattice Model Simulator Package
jurajHasik / Peps TorchSolving two-dimensional spin models with tensor networks (powered by PyTorch)
issp-center-dev / MVMCA numerical solver package for a wide range of quantum lattice models based on many-variable Variational Monte Carlo method
mhoffman / KmoskMC on steroids: A vigorous attempt to make lattice kinetic Monte Carlo modelling easier
ziatdinovmax / GPimGaussian processes and Bayesian optimization for images and hyperspectral data
google-research / LastA JAX library for building lattice-based speech transducer models
jbuckman / Neural Lattice Language ModelsCode for upcoming TACL paper w/ Graham Neubig, "Neural Lattice Language Models".
neubig / LatticelmSoftware for unsupervised word segmentation and language model learning using lattices
dylanljones / LattpySimple and efficient Python package for modeling d-dimensional Bravais lattices in solid state physics.
llnl / MultiscaleTopOptA 3D multsicale topology optimization code using surrogate models of lattice microscale response
cmendl / LBM ClassicC implementation of the classical lattice Boltzmann method (LBM) using the D2Q9 and D3Q19 models
chahuja / LruLattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling
aromanro / IsingMonteCarloA program implementing Metropolis Monte Carlo for the 2D square-lattice Ising model and the spin block renormalization
luchko / Latticegraph DesignerPyQt based GUI tool which allows to visualize, design and export the lattice graph models.
jalovisko / LatticeQueryModeling of heterogeneous lattice structures with Open CASCADE
becarioprecario / INLAMSMMultivariate spatial models for lattice data with INLA
kattemolle / HVQEUser-friendly but high-performance code for simulating Variational Quantum Eigensolvers for the Heisenberg model on any lattice. It is designed to support a computer-cluster workflow.
