An AI made using artificial intelligence (AI) and machine learning algorithms (ML) .

Overview

DTech.AIML

An AI made using artificial intelligence (AI) and machine learning algorithms (ML) . This is created by help of some members in my team and they have a vast big contribution in this project.

Any way welcome to the DTech which stands for Darshan Technologies here we are creating projects using AI artificial intelligence and ML that is machine learning algorithms. Recently we have made a stock market value predicting model and this very profitable in all the case .

Any way hope that if you are reading this documentation to the end then we hope that you will be Stay forever with us helping in builting new project and to get new project ideas.

Team Members :::--->>> ______________________: 1)Darshan 2)Shubham 3)Arijit

This is the repository of shape matching algorithm Iterative Rotations and Assignments (IRA)

Description This is the repository of shape matching algorithm Iterative Rotations and Assignments (IRA), described in the publication [1]. Directory

MAMMASMIAS Consortium 6 Nov 14, 2022
Data pipelines for both TensorFlow and PyTorch!

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1 Dec 08, 2021
Encoding Causal Macrovariables

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For the paper entitled ''A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion Mining''

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VOS: Learning What You Don’t Know by Virtual Outlier Synthesis

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ALFRED - A Benchmark for Interpreting Grounded Instructions for Everyday Tasks

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Face Mask Detector by live camera using tensorflow-keras, openCV and Python

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SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model

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MODNet: Trimap-Free Portrait Matting in Real Time

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Official Code Release for Container : Context Aggregation Network

Container: Context Aggregation Network Official Code Release for Container : Context Aggregation Network Comparion between CNN, MLP-Mixer and Transfor

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Pytorch implementation of NeurIPS 2021 paper: Geometry Processing with Neural Fields.

Geometry Processing with Neural Fields Pytorch implementation for the NeurIPS 2021 paper: Geometry Processing with Neural Fields Guandao Yang, Serge B

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Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.

CSE-Autoloss Designing proper loss functions for vision tasks has been a long-standing research direction to advance the capability of existing models

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Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

Official implementation of NeurIPS 2021 paper "One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective"

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Temporally Efficient Vision Transformer for Video Instance Segmentation, CVPR 2022, Oral

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Calibrate your listeners! Robust communication-based training for pragmatic speakers. Findings of EMNLP 2021.

Calibrate your listeners! Robust communication-based training for pragmatic speakers Rose E. Wang, Julia White, Jesse Mu, Noah D. Goodman Findings of

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