11 skills found
andi-jo / ML Projection ToolboxSimple wrapper for machine learning models in the context of lead-lag projection modelling.
KRYMZ0N / SimpleExploitFixerA Simple Exploit Fixer plugin that aims to fix most lag machines
Nelvinebi / Ocean Current Forecasting With Time Series ML ModelsThis project forecasts ocean current speeds using synthetic time series data and machine learning models. It demonstrates how lag-based features and supervised learning (Linear Regression, Random Forest) can be applied to environmental data for predictive analysis and educational purposes.
HLVprob / Minecraft Server CrasherAdvanced Minecraft server stress testing tool built with Node.js. Features high-performance bot flooding, packet spam (lag machine), and AI movement.
Eric02851 / Valorant Lag MachineLags valorant servers by rapidly picking up and throwing shorties.
achahalrsh / Global Power Consumption Time Series Forecast Developed a multivariate multistep time series forecasting model using lagged variables to predict the electricity power consumption for next 7 days. Cleaned the data and performed data exploration and visualisation using matplotlib, seaborn, ggplot2 and created new variables for the date time indexed dataset based on seasonality, time of the day and day of the week etc. Trained the model on various machine learning algorithms including SVR, LR , Random Forest , XGBoost and got a RMSE of approximately 280.
CambridgeIIS / Gesture SpotterIn this paper we examine the task of key gesture spotting: accurate and timely online recognition of hand gestures. We specifically seek to address two key challenges faced by developers when integrating key gesture spotting functionality into their applications. These are: i) achieving high accuracy and zero or negative activation lag with single-time activation; and ii) avoiding the requirement for deep domain expertise in machine learning. We address the first challenge by proposing a key gesture spotting architecture consisting of a novel gesture classifier model and a novel single-time activation algorithm. This key gesture spotting architecture was evaluated on four separate hand skeleton gesture datasets, and achieved high recognition accuracy with early detection. We address the second challenge by encapsulating different data processing and augmentation strategies, as well as the proposed key gesture spotting architecture, into a graphical user interface and an application programming interface. Two user studies demonstrate that developers are able to efficiently construct custom recognizers using both the graphical user interface and the application programming interface.
amberwalker-ds / DML For Panel DataThis project uses Double Machine Learning (DML) to estimate the causal effect of peace agreements on reducing violence intensity. Key components include Random Forest models, cross-fitting, and panel data with fixed effects to handle lagged data and confounders.
timkiely / Spatially Conscious Ml ModelDemonstrates the use of spatial lag features to boost the accuracy of supervised machine learning models predicting real estate values. Published in SADA November 2019. Awarded 2018-19 Distinguished Thesis Award from Northwestern Masters of Data Science program
benrayfield / QuineforgeA quine is a software which outputs its own source code. Quineforge is a very experimental data format for the lossless compression of quines and for translating all possible non-quines (such as pictures of cats, videos, games, GPU matrix multiply algorithms, or nearly anything) into quine form. It uses the (wikipedia page) Chain_rule_for_Kolmogorov_complexity and a 5-way gametree (like a chess or go gametree) to navigate the space of all possible lambda functions. Its security level is planned to be, eventually after the bugs are worked out, enough for the operation of industrial machines and low lag enough to satisfy hardcore gamers. TODO I should copy some of the "fntape" (5 way tree) theory from occamsfuncer readme and various parts of kolmogorov theory. Basically, for example, if we are using sha3_256 (with some pre and post processing of a merkle forest node (or its faster lazy merkle form), then at some few points in a sequence of bits will occur those 256 bits, and the 256 bit ids of other functions, sparsely, and between those are 1 bit at a time opcodes (or 3 or 4 bit opcodes, or something like that), with some opcodes being to say that what follows is a variable size number of 1 bits followed by a 0 bit, then a powOf2 number of bits is a complete binary tree of bits (cbt) which is a lambda function of Lx.Ly.Lz.zxy aka the church encoding of the pair function whose "leafs" are Lx.Ly.x (true) and Lx.L.y.y (false), which goes into the "register" of "fntape" which is basically a lambda datastruct of 2 linkedlists with 5 possible actions from each possible state: move turingtape left (whose cell contents are each a function), move it right, copy register to center of tape, copy center of tape to register, or (heres the only turing complete part) call register on whats at center of turing tape and replace register with what that returns (and using various statistical methods if there is an infinite loop or other nonhalting lambda call it will be given up on quickly before that happens, within some approx specified low lag limits, but compressed forms are expected not to have nonhalters or overly expensive operations etc else they are not shared in the network as often as cheaper faster more useful data structures). So basically theres a bunch of functions, in the space of all possible lambda functions sparsely explored among many computers and people (some of which may be cat memes, minigames, compressed random bitstrings, or whatever) and fntape kind of opcodes aka small bitstrings from one id256 to another id256 such as to say its left child (a few bits of fntape) or its right child etc, or various combinator on eachother, leads to what else. Its a space where, as the name quineforge implies, the distance of bitstring from any function, or from any small set of functions, to any other function, has bigO that is certainly within the distance predicted by kolmogorov complexity theory and which in practice may be able to compete with zip files, 7z files, wavelet sound compression, neuralnet video compression, AIXI compression, andOr any other imaginable kind of compression, and trading compression efficiency for low lag and scalability etc, you might build low lag high voxel count massively multiplayer games with it, or various experiments somewhere within that.
SurabhiShah / LKMR SimulationsSimulation code for lagged kernel machine regression
