23 skills found
fitzgen / MachA rust interface to the Mach 3.0 kernel that underlies OSX.
Darm64 / XNUResearch into porting the XNU kernel to ARM devices.
JustPingo / KernelTimeMachine OldInitial version of KTM, still there for the sake of Drama History™. A safe 3DS downgrader using TTP files.
Piker-Alpha / DebugMachKernel.shScript to enable debug output in the mach_kernel
cocoahuke / IosdumpkernelfixThis tool will help to fix the Mach-O header of iOS kernel which dump from the memory. So that IDA or function symbol-related tools can loaded function symbols of ios kernel correctly
KitsuneBSD / FKernel Old V2A small hybrid kernel for x86_64 with a freestanding, bare-metal focus. It is inspired by MINIX, seL4, Mach, the BSDs, and SerenityOS.
neozeed / Mach86Mach kernel from 1986
xinwangliu / Multi Kernel Extreme Learning MachineNo description available
cocoahuke / UniversalmigparserExtract and generate code based on name and type for mig func/arg/request&reply member etc, ideal helper for creating monitor, tracker, fuzzer etc for Mach Remote Procedure Calls.
wangsiwei2010 / Multi Kernel Extreme Learning MachineMatlab code for "Multiple kernel extreme learning machine"
Xiangyan93 / Chem Graph Kernel MachineMachine Learning using marginalized graph kernel for chemical molecules.
attilathedud / Mem ScanPOC memory editor that uses the mach_vm kernel calls to scan, read, and write integer memory regions.
kennedyCzar / ADVANCE MACHINE LEARNING KERNEL METHODAdvance machine Learning: Kernel methods implemented for PCA, KMeans, Logistic Regression, Support Vector Machine (SVM) and Support Vector Data Description (SVDD)
tilkb / Siamese Kernel MachineCombine SVM with deep learning for one-shot learning
rsln-s / Importance Of Kernel Bandwidth In Quantum Machine LearningData and code for the paper "Importance of Kernel Bandwidth in Quantum Machine Learning"
FormMe / MultipleKernel LeastSquares SupportVectorMachineMultiple Kernel Least Squares Suport Vector Machine provide classification model.
axelexic / MachManiaMach IPC Playground for kernel and DTrace purposes
amd64pager / ChronosA hybrid kernel with inspiration from XNU / Mach and BSD and *nix
MrPandey01 / Stiefel Restricted Kernel MachinePython code for the model St-RKM
ljinstat / Structured Data Random Features For Large Scale Kernel MachinesKernel machines such as the Support Vector Machine are widely used in solving machine learning problem, since they can approximate any function or decision boundary arbitrary well with enough training data. However, those methods applied on the kernel matrix (Gram matrix) of the data scale poorly with the size of the training dataset. The kernel trick may become intractable to compute as the computation and storage requirements for the kernel trick are exponentially proportional to the number of samples in the dataset. It takes a long time to train a model when training examples have big volume. For some specialized algorithms for linear Support Vector Machines, they operate much more quickly when the dimensionality of data is small because they operate on the covariance matrix rather than the kernel matrix of the training data. This paper we’ve chosen proposes a way to combine the advantages of the linear and nonlinear approaches. This method transformed the training and evaluation of any kernel machine by mapping the input data to a randomized low-dimensional feature space in order to create corresponding opera- tions of a linear machine. Those randomized features are designed to ensure that the inner products of the transformed data are nearly equal to those in the feature space of a user specific shift-invariant kernel. This method gives competitive results with state-of-the-art kernel-based classification and re- gression algorithms. What’s more, random features fix the problem of large scale of training data when computing the kernel matrix. The results have similar or even better testing error.
