Artificial Intelligence
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Artificial Intelligence

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@ai_machinelearning_big_data - Machine learning channel

@pythonl - Our Python channel

@pythonlbooks- python книги📚

@datascienceiot - ml 📚

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🎼 A Framework for Benchmarking Clustering Algorithms

BEVStereo is a new multi-view 3D object detector using temporal stereo to enhance depth estimation.

⚙️Github: https://github.com/megvii-basedetection/bevstereo

📄Paper: https://arxiv.org/abs/2209.10248v1

🗒Dataset: https://paperswithcode.com/dataset/nuscenes

@ArtificialIntelligencedl
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Forwarded from Machinelearning
🗣 Robust Speech Recognition via Large-Scale Weak Supervision

Whisper is a general-purpose speech recognition model by Open AI.

pip install git+https://github.com/openai/whisper.git

⚙️ Github
💡 Colab
💻 Model
🗒 Paper
🦾 Dataset
✴️ HABR

@ai_machinelearning_big_data
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🦾 Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

A novel personalization pipeline to calibrate the intrinsic shape parameters using only a few unlabeled RGB images of the subject.

conda create -n IdHandMesh python=3.8
conda activate IdHandMesh


⚙️Github: https://github.com/deyingk/personalizedhandmeshestimation

📄Paper: https://arxiv.org/abs/2209.10840v1

🗒Dataset: https://paperswithcode.com/dataset/dexycb

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MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline

# Clone the repo
git clone https://github.com/walker-hyf/MnTTS.git
cd $PROJECT_ROOT_DIR

⚙️Github: https://github.com/walker-hyf/mntts

📄Paper: https://arxiv.org/abs/2209.10848v1

🗒Dataset: https://paperswithcode.com/dataset/ljspeech

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🔸 Poisson Flow Generative Models

A new Poisson flow generative model (PFGM) that maps a uniform distribution on a high-dimensional hemisphere into any data distribution.

⚙️Github: https://github.com/newbeeer/poisson_flow

📄Paper: https://arxiv.org/abs/2209.11178v1

🗒Dataset: https://paperswithcode.com/dataset/lsun

@ArtificialIntelligencedl
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🚀 On Efficient Reinforcement Learning for Full-length Game of StarCraft II

In this work, we investigate a set of RL techniques for the full-length game of StarCraft II

⚙️Github: https://github.com/liuruoze/mini-AlphaStar

📄Paper: https://arxiv.org/abs/2209.11553v1

🗒HierNet-SC2: https://github.com/liuruoze/hiernet-sc2

@ArtificialIntelligencedl
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🦾 EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems


EasyRec implements state of the art deep learning models used in common recommendation tasks: candidate generation(matching), scoring(ranking), and multi-task learning.

⚙️Github: https://github.com/alibaba/easyrec

📄Paper: https://arxiv.org/abs/2209.12766v1

🗒Dataset: https://paperswithcode.com/dataset/criteo

@ArtificialIntelligencedl
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News Summarization and Evaluation in the Era of GPT-3

Corpus of 10K generated summaries from fine-tuned and zero-shot models across 4 standard summarization benchmarks.


⚙️Github: https://github.com/tagoyal/factuality-datasets

📄Paper: https://arxiv.org/abs/2209.12356v1

🗒Dataset: https://paperswithcode.com/dataset/cnn-daily-mail-1

@ArtificialIntelligencedl
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🦾 Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks

An object-centric vision framework, Obj2Seq. Obj2Seq takes objects as basic units, and regards most object-level visual tasks as sequence generation problems of objects.

⚙️Github: https://github.com/casia-iva-lab/obj2seq

📄Paper: https://arxiv.org/abs/2209.13948

🗒Dataset: https://paperswithcode.com/dataset/coco

@ArtificialIntelligencedl
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📰 A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection

a novel Coarse-to-fine Cascaded Evidence-Distillation (CofCED) neural network for explainable fake news detection based on such raw reports, alleviating the dependency on fact-checked ones.

⚙️Github: https://github.com/nicozwy/cofced

📄Paper: https://arxiv.org/abs/2209.14642v1

🗒Dataset: https://paperswithcode.com/dataset/fever

@ArtificialIntelligencedl
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📌 Denoising MCMC for Accelerating Diffusion-Based Generative Models

a general sampling framework, Denoising MCMC (DMCMC), that combines Markov chain Monte Carlo (MCMC) with reverse-SDE/ODE integrators / diffusion models to accelerate score-based sampling.

⚙️Github: https://github.com/1202kbs/dmcmc

📄Paper: https://arxiv.org/abs/2209.14593v1

🗒Dataset: https://paperswithcode.com/dataset/celeba-hq

@ArtificialIntelligencedl
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✔️ 4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation

conda create --name <env> --file requirements.txt

cd cpp_wrappers
sh compile_wrappers.sh

cd pointnet2
python setup.py install

⚙️Github: https://github.com/larskreuzberg/4d-stop

📄Paper: https://arxiv.org/abs/2209.14858v1

🗒Dataset: https://paperswithcode.com/dataset/semantickitti

@ArtificialIntelligencedl
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📌 ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing

An unsupervised end-to-end network for discovering sketch and extrude from point clouds.

conda create --name ExtrudeNet python=3.7
conda activate ExtrudeNet
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch
conda install -c open3d-admin open3d
conda install numpy
conda install pymcubes
conda install tensorboard
conda install scipy
pip install tqdm

⚙️Github: https://github.com/kimren227/extrudenet

📄Paper: https://arxiv.org/abs/2209.15632v1

@ArtificialIntelligencedl
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✔️ From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution

git clone https://github.com/csxmli2016/ReDegNet
cd ReDegNet
conda create -n redeg python=3.8 -y
conda activate redeg
python setup.py develop


⚙️Github: https://github.com/csxmli2016/redegnet

📄Paper: https://arxiv.org/abs/2210.00752v1

🗒Dataset: https://paperswithcode.com/dataset/realsrset

@ArtificialIntelligencedl
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🖥 The Vendi Score: A Diversity Evaluation Metric for Machine Learning

pip install vendi_score

⚙️Github: https://github.com/vertaix/vendi-score

📄Paper: https://arxiv.org/abs/2210.02410v1

🗒Dataset: https://paperswithcode.com/dataset/multinli

@ArtificialIntelligencedl
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🎞 Inverting a Rolling Shutter Camera: Bring Rolling Shutter Images to High Framerate Global Shutter Video

⚙️Github: https://github.com/gitcvfb/rssr

📄Paper: https://arxiv.org/abs/2210.03040v1

🗒Dataset: https://paperswithcode.com/dataset/carla

@ArtificialIntelligencedl
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🔥 Полезнейшая Подборка каналов

🖥 Machine learning
@ai_machinelearning_big_data – все о машинном обучении
@data_analysis_ml – все о анализе данных.
@machinelearning_ru – машинное обучении на русском от новичка до профессионала.
@machinelearning_interview – подготовка к собеседования Data Science
@datascienceiot – бесплатные книги Machine learning
@ArtificialIntelligencedl – канал о искусственном интеллекте
@neural – все о нейронных сетях
@machinee_learning – чат о машинном обучении
@datascienceml_jobs - работа ds, ml

🖥 Python

@pro_python_code – погружение в python
@python_job_interview – подготовка к Python собеседованию
@python_testit тесты на python
@pythonlbooks - книги Python
@Django_pythonl django
@python_djangojobs - работа Python

🖥 Java
@javatg - Java для програмистов
@javachats Java чат
@java_library - книги Java
@android_its Android разработка
@java_quizes - тесты Java
@Java_workit - работа Java
@progersit - шпаргалки ит

🖥 Javascript / front
@javascriptv - javascript изучение
@about_javascript - javascript продвинутый
@JavaScript_testit -тесты JS
@htmlcssjavas - web
@hashdev - web разработка

👣 Golang
@golang_interview - вопросы и ответы с собеседований по Go. Для всех уровней разработчиков.
@Golang_google - go для разработчиков
@golangtests - тесты и задачи GO
@golangl - чат Golang
@GolangJobsit - вакансии и работа GO
@golang_jobsgo - чат вакансий
@golang_books - книги Golang
@golang_speak - обсуждение задач Go

🖥 Linux
@linux_kal - чат kali linux
@linuxkalii - linux kali
@linux_read - книги linux

👷‍♂️ IT работа

@hr_itwork - ит-ваканнсии

🖥 SQL
@sqlhub - базы данных
@chat_sql - базы данных чат

🤡It memes
@memes_prog - ит-мемы

⚙️ Rust
@rust_code - язык программирования rust
@rust_chats - чат rust

#️⃣ c# c++
@csharp_ci - c# c++кодинг
@csharp_cplus чат

📓 Книги

@programming_books_it
@datascienceiot
@pythonlbooks
@golang_books
@frontendbooksit
@progersit
@linux_read
@java_library
@frontendbooksit

📢 English for coders

@english_forprogrammers - Английский для программистов

🖥 Github
@github_code
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🏓 Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence

git clone https://github.com/SunghwanHong/CATs
cd CATs

conda create -n CATs python=3.6
conda activate CATs

pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -U scikit-image
pip install git+https://github.com/albumentations-team/albumentations
pip install tensorboardX termcolor timm tqdm requests pandas


⚙️Github: https://github.com/SunghwanHong/Cost-Aggregation-transformers

📄Paper: https://arxiv.org/abs/2210.02689v1

🗒Dataset: https://paperswithcode.com/dataset/nerf

@ArtificialIntelligencedl
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🖥 CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning

A novel method, CANIFE, that uses canaries - carefully crafted samples by a strong adversary to evaluate the empirical privacy of a training round.

conda create -n "canife" python=3.9
conda activate canife
pip install -r ./requirements.txt


⚙️Github: https://github.com/facebookresearch/canife

📄Paper: https://arxiv.org/abs/2210.02912v1

🗒Dataset: https://paperswithcode.com/dataset/cifar-10

@ArtificialIntelligencedl
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