198 skills found · Page 5 of 7
mlvlab / DDMIOfficial Implementation (Pytorch) of "DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations", ICLR 2024
irfancn / Abaqus UMAT ViscoelasticAbaqus UMAT subroutine for viscoelastic model (Voigt) for implicit analysis.
jenovencio / Fortran Library For Material Constitutive ModelsThis library has a lot of Fortran Routines for model implicit material constitutive behavior. There are implementation of Neo-Hooken, Odgen and Hencky potential for hyperelastic behavior.
LordLiang / GA Sketching(PG2023/CGF) This is the official PyTorch implementation of PG2023/CGF paper: GA-Sketching: Shape Modeling from Multi-View Sketching with Geometry-Aligned Deep Implicit Functions
cleverhans-lab / Capc IclrCaPC is a method that enables collaborating parties to improve their own local heterogeneous machine learning models in a setting where both confidentiality and privacy need to be preserved to prevent explicit and implicit sharing of private data.
knowitall / ImplieImplicit relation extractor using a natural language model.
jamenlong / ALS Expected Percent Rank CvAlternate cross validation approach for ALS models with implicit ratings utilizing an expected percent ranking metric for model performance evaluation.
aidinattar / Volatility Carry Trading StrategyModelling the implicit volatility, using multi-factor statistical models.
VisionXLab / DVGBench[ISPRS2026] DVGBench: Implicit-to-Explicit Visual Grounding Benchmark in UAV Imagery with Large Vision-Language Models
ideas4u / Trading PlatformThis project is the most awaited project in open source community where every user who belongs to Stock Trading always wanted to develop its own software. This project has been developed specifically for Indian Market Stock Trading. It encompasses end to end trading cycle for intraday trading but the design would be such that it can be easily extended for delivery trading. During the lifecycle of this project we will be using most advance technologies but the base code will always be C/C++. Development Methodology: ======================== We use "Incremental Life Cycle Model" along with Cross-Platform Development (Portable). Project Priorities and Assumptions: =================================== 1) Low Latency, High Performance all the time. 2) Wherever choice has to be made between memory and execution speed, we give preference to speed. 3) Every module devloped will be exhaustively tested. How the work Proceed: ===================== Before the beginning of any new project, we should know the "PROBLEM STATEMENT", so here it is "Problem Statement" ------------------- To Build a high performance, low latency, end to end Trading Platform for Indian Stock Market but not limited to which home users should be able use for trading which guarantees (99% of the times) the profit but does not guarantees maximized profit for intraday trading. First Step: ----------- To provide the optimal solution to any problem is "UNDERSTAING THE PROBLEM". To understand the above problem statement you need to really extract the explicit and implcit requirements from the statement. Here is the List of requirements: Explicit: --------- 1) High Performance 2) Low-Latency 3) End-to-End Trading Platform 4) Focus on Indian Stock Market but not limited to it. 5) Guarantees (99% of the times) the profit but does not guarantees maximized profit. 6) Only for Intraday Trading. Implicit: --------- 1) Book Keeping of the order and trade (Order Management System). 2) Availability of Market Data to End-Users on Demand for identifying the stock and placing the order. 3) User Account Management. Might be I missed something please suggest and after reveiw we add it here. Second Step: ------------ To understand the above Explicit/Implicit requirements, you should have the "KNOWLEDGE OF VARIOUS TECHNOLOGIES" and indepth undertstanding of the "PROBLEM DOMAIN" i.e. Stock Market. Once this is achieved we need to architect the solution in terms of Software and Hardware nodes and their integration. Third Step: ----------- To solve the problem statement, the above requirements should be "DECOMPOSED IN MODULES" and map to them with technolgoies/software/hardware used. Below is the list of modules we are able to identify: Modules Included: ================= Core Modules: -------------- 1) Core Libraries 2) Manual Order Entry System 3) Auto Order Entry System 4) Artificial Exchange 5) Algorithmic Trading Platform 6) Smart Order Router 7) Direct Trading Platform (Ooptional) Utility Modules: ---------------- 8) Logger Server 9) HeartBeat Server Technologies Used: ================= Software: --------- We always use freeware, Open Source Softwares or APIs which are the part of GPL, LGPL.xx licence. Any special requirement for building/using the modules will be detailed in specific module. For development, we generally use: ---------------------------------- Windows-7 for Operating System but any other OS ca be used. Our Code is Platform Indepandant. Visual Studio 2013 in built compiler for build or Intel@ Compilers which can be easily integrated with Visual Studio IDE. For real time, we generally use: -------------------------------- Linux-susse 10 or above with real time extensions. gcc 4.4.1 for build. vi editor Hardware: --------- No special requirement for development purpose. For real time use, it depands how much Stock you are interested in and the various configuration of modules. We prefer generally the below configuration for any number of Stock Trading: 256 GB RAM 16 core processor 1 TB of HDD/SDD Programming Languages and other Technologies: --------------------------------------------- C, C++99/c++11, Lua, ZeroMq, nanodbc, Lock-Free Data Structures, Intel TBB, Boost, Google Protobuf, MySql, Python. Fourth Step: ------------ Dcompose each module till it becomes entity to provide the useful functionality. We are going to explain this in each module detailed section. Fifth Step: ------------ We do design/develop/benchmark/unit test/integration testing of the above modules. Sixth Step: ------------ We deploy the delivered software on various hardware nodes as per the deployment architecture and integrate them. Seventh Step: ------------ Observe the behaviour of deployed software on live traffic and cut two branches at this level : 1st branch continue to do incremental development and 2nd branch fix the issues reported which can be later merged with 1st branch for another release. Any suggestions for improvement are most welcome.
cyz-ai / Neural Approx Ss LfiCodes for ICLR 21 paper: Neural Approximate Sufficient Statistics for Implicit Models
daigo0927 / Jax DdimJax/Flax implementation of Denoising Diffusion Implicit Models
osmhpi / Federated Learning DagImplicit Model Specialization through DAG-based Decentralized Federated Learning
Ali-Meh619 / FKANThis repository hosts the code for the FKAN model, proposed in our IEEE ICASSP paper "Implicit Neural Representations with Fourier Kolmogorov-Arnold Networks".
guglielmocamporese / Learning Invariances In Speech RecognitionIn this work I investigate the speech command task developing and analyzing deep learning models. The state of the art technology uses convolutional neural networks (CNN) because of their intrinsic nature of learning correlated represen- tations as is the speech. In particular I develop different CNNs trained on the Google Speech Command Dataset and tested on different scenarios. A main problem on speech recognition consists in the differences on pronunciations of words among different people: one way of building an invariant model to variability is to augment the dataset perturbing the input. In this work I study two kind of augmentations: the Vocal Tract Length Perturbation (VTLP) and the Synchronous Overlap and Add (SOLA) that locally perturb the input in frequency and time respectively. The models trained on augmented data outperforms in accuracy, precision and recall all the models trained on the normal dataset. Also the design of CNNs has impact on learning invariances: the inception CNN architecture in fact helps on learning features that are invariant to speech variability using different kind of kernel sizes for convolution. Intuitively this is because of the implicit capability of the model on detecting different speech pattern lengths in the audio feature.
tranquyenbk173 / BERT ITEOfficial implementation of "From Implicit to Explicit Feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users" (Neurocomputing 2022)
suddhu / Gpis Touch Public(In progress: see roadmap) Gaussian process implicit surface generation from manipulator contact measurements, for object modeling
Jung-Su / Deep Visual ConstraintsPytorch implimentation of the paper: "Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input"
aykutonol / Ilqr CitoMatlab implementation of contact-implicit trajectory optimization based on a variable smooth contact model and iLQR
bachzz / UW DiffPhysUnderwater Image Enhancement with Physical-based Denoising Diffusion Implicit Models
