72 skills found · Page 1 of 3
NVlabs / Intrinsic3dIntrinsic3D - High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (ICCV 2017)
pysal / SpoptSpatial Optimization
Robotics-STAR-Lab / REMANI Planner[ICRA'24] Real-time Whole-body Motion Planning for Mobile Manipulators Using Environment-adaptive Search and Spatial-temporal Optimization
davyneven / SpatialEmbeddingsInstance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
Mati365 / Micro Racing🚗 🏎️ 🎮 A browser-based isometric racing game built with a custom WebGL 1.x engine. Features real-time multiplayer with client-side prediction, spatial optimizations via quad trees, and AI opponents powered by neural networks trained through genetic algorithms.
WHUzxp / Reprinted Applied Energy复刻论文Applied Energy的论文A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles，包含考虑电动汽车有序充放电的机组组合和最优潮流
tylfin / GeosPyHighly optimized geolocation inference package for spatial approximation
TUW-GEO / Equi7GridDefinition of the Equi7Grid - a spatial reference optimized for global high-resolution raster data.
facebookresearch / BinauralSDMThis repository contains a set of tools to render Binaural Room Impulse Responses (BRIR) using the Spatial Decomposition Method (SDM).The implementation features a series of improvements presented in Amengual et al. 2020, such as quantization of the direction of arrival (DOA) estimates to improve the spectral properties of the rendered BRIRs, or RTMod and RTMod+AP equalization for the late reverberation.The repository also contains the necessary files to 3D print an array holder of optimized topology for the estimation of DOA information.
YihongT / ITINERA[EMNLP 2024 Industry Track & KDD UrbComp 2024 Best Paper Award] ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning
apulverizer / PyspatialoptA library to create, solve, and analyze spatial optimization problems
AliAmini93 / ADHDeepNetADHDeepNet is a model that integrates temporal and spatial characterization, attention modules, and explainability techniques, optimized for EEG data ADAD diagnosis. Neural Architecture Search (NAS), Hyper-parameter optimization, and data augmentation are also incorporated to enhance the model's performance and accuracy.
loicland / Point Cloud RegularizationA structured optimization framework for spatially regularizing point clouds classification
walkerke / Spopt RSpatial Optimization for R
cubedhuang / BoidsAn interactive 2D bird flocking simulation using a spatial subdivision method to optimize performance for thousands of agents.
HaroldChen19 / VistaDPO[ICML 2025] VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models
bio-mlhui / MedGround R1Offical Code of MICCAI'25 Best-Paper-Shortlist paper "MedGround-R1: Advancing Medical Image Grounding via Spatial-Semantic Rewarded Group Relative Policy Optimization"
antonson-j1 / SHA256 Accelerator HardwareThis project aims at implementing an hardware accelerator peripheral for SHA256 hashing algorithm with AXI4 interfacing with PicoRV32 CPU. The project focuses on multiple implementations of the accelerator with gradual improvements through spatial pre-computation techniques and pipelining. The SHA256 accelerators are implemented using Verilog and synthesized using Yosys Open Synthesis Suite. The optimized designs are then compared with a base-line C implementation in software. Hash functions are used to securely store passwords, to quickly store and retrive data, and also to check if a file/message is corrupted.
GeoSOSDevelopers / GeoSOSforArcGISThis is the repository of GeoSOS for ArcGIS, running as an add-in in ArcGIS desktop software ArcMap, provides spatial simulation and optimization functions, under open-source license GPLv3.
sayantann11 / Clustering Modelsfor MLlustering in Machine Learning Introduction to Clustering It is basically a type of unsupervised learning method . An unsupervised learning method is a method in which we draw references from datasets consisting of input data without labelled responses. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples. Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. For ex– The data points in the graph below clustered together can be classified into one single group. We can distinguish the clusters, and we can identify that there are 3 clusters in the below picture. It is not necessary for clusters to be a spherical. Such as : DBSCAN: Density-based Spatial Clustering of Applications with Noise These data points are clustered by using the basic concept that the data point lies within the given constraint from the cluster centre. Various distance methods and techniques are used for calculation of the outliers. Why Clustering ? Clustering is very much important as it determines the intrinsic grouping among the unlabeled data present. There are no criteria for a good clustering. It depends on the user, what is the criteria they may use which satisfy their need. For instance, we could be interested in finding representatives for homogeneous groups (data reduction), in finding “natural clusters” and describe their unknown properties (“natural” data types), in finding useful and suitable groupings (“useful” data classes) or in finding unusual data objects (outlier detection). This algorithm must make some assumptions which constitute the similarity of points and each assumption make different and equally valid clusters. Clustering Methods : Density-Based Methods : These methods consider the clusters as the dense region having some similarity and different from the lower dense region of the space. These methods have good accuracy and ability to merge two clusters.Example DBSCAN (Density-Based Spatial Clustering of Applications with Noise) , OPTICS (Ordering Points to Identify Clustering Structure) etc. Hierarchical Based Methods : The clusters formed in this method forms a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one. It is divided into two category Agglomerative (bottom up approach) Divisive (top down approach) examples CURE (Clustering Using Representatives), BIRCH (Balanced Iterative Reducing Clustering and using Hierarchies) etc. Partitioning Methods : These methods partition the objects into k clusters and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter example K-means, CLARANS (Clustering Large Applications based upon Randomized Search) etc. Grid-based Methods : In this method the data space is formulated into a finite number of cells that form a grid-like structure. All the clustering operation done on these grids are fast and independent of the number of data objects example STING (Statistical Information Grid), wave cluster, CLIQUE (CLustering In Quest) etc. Clustering Algorithms : K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problem.K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster . Applications of Clustering in different fields Marketing : It can be used to characterize & discover customer segments for marketing purposes. Biology : It can be used for classification among different species of plants and animals. Libraries : It is used in clustering different books on the basis of topics and information. Insurance : It is used to acknowledge the customers, their policies and identifying the frauds. City Planning: It is used to make groups of houses and to study their values based on their geographical locations and other factors present. Earthquake studies: By learning the earthquake-affected areas we can determine the dangerous zones. References : Wiki Hierarchical clustering Ijarcs matteucc analyticsvidhya knowm
