84 skills found · Page 1 of 3
zh217 / Torch DctDCT (discrete cosine transform) functions for pytorch
PiMaker / LtcgiOptimized plug-and-play realtime area lighting using the linearly transformed cosine algorithm for Unity/VRChat.
selfshadow / Ltc CodeCode for "Real-Time Polygonal-Light Shading with Linearly Transformed Cosines"
Zhaozixiang1228 / GDSR DCTNet[CVPR 2022 Oral] Official implementation for "Discrete Cosine Transform Network for Guided Depth Map Super-Resolution."
tizian / Ltc SheenCode for "Practical Multiple-Scattering Sheen Using Linearly Transformed Cosines" (SIGGRAPH 2022 Talk) by Tizian Zeltner, Brent Burley, and Matt Jen-Yuan Chiang
ejmahler / Rust DctRust library to compute the main four discrete cosine transforms
MasonEdgar / DCT Image SteganographyA small python app to embed "secret" user data into a carrier image by manipulation of the Discrete Cosine Transform (DCT) AC coefficients. This application was developed for a graduate-level university project.
Paul180297 / BezierLightLTCAn official implementation of the paper "Real-Time Shading of Free-Form Area Lights using Linearly Transformed Cosines". Please refer to README for the setup instruction.
Kinyugo / Torch MdctA PyTorch implementation of the Modified Discrete Cosine Transform (MDCT) and its inverse for audio processing.
vinuni-vishc / Few Shot Cosine TransformerFew shot learning for human pose estimation
jonashaag / PydctShort-Time Discrete Cosine Transform (DCT) for Python. SciPy, TensorFlow and PyTorch implementations.
mathworks / Fast Poisson Equation Solver Using DCTFast Poisson Equation Solver using Discrete Cosine Transform
guiqi134 / LTC Area LightsImplementation of the paper "Real-time polygonal-light shading with linearly transformed cosines"
viralgokani / 8PointDCT VerilogDiscrete Cosine Transform (DCT) is one of the important image compression algorithms used in image processing applications. Several algorithms have been proposed over the last couple of decades to reduce the number of computations and memory requirements involved in the DCT computation algorithm. One of the algorithms is implemented here using Verilog HDL.
pkmital / PkmFFTpkmFFT provide simple interfaces to the Accelerate.framework for performing vectorized FFT. pkmSTFT builds on pkmFFT to perform Short Time Fourier Transform efficiently using vectorized ops. Also handles options for windowing. pkmDCT provides a simple discrete cosine transform using Accelerate and pkmMatrix.
AbraaoHonorio / DCT Discrete Cosine TransformIdentify the frequency domain of an image. Aplly Discrete Cosine Transform(DCT ) and Inverse Discrete Cosine Transform(IDCT) in image
tistatos / Supersonic SunshineImplementation of Real-Time Polygonal-Light Shading with Linearly Transformed Cosines by Eric Heitz et al for the course TSBK03 Advanced Game Programming
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
BYRTIMO / END TO END SPEECH ENHANCEMENT BASED ON DISCRETE COSINE TRANSFORMNo description available
cscribano / DCT Former PublicPublic repository for "DCT-Former: Efficient Self-Attention withDiscrete Cosine Transform"
