🔌 HiPart: Hierarchical divisive clustering toolbox
It is a package with similar execution principles as the scikit-learn package. It also provides two types of static visualizations for all the algorithms executed in the package, with the addition of linkage generation for the divisive hierarchical clustering structure.
⚙️Github: https://github.com/panagiotisanagnostou/hipart
📄Paper: https://arxiv.org/abs/2209.08680v1
📎Dataset: https://paperswithcode.com/dataset/usps
@ArtificialIntelligencedl
It is a package with similar execution principles as the scikit-learn package. It also provides two types of static visualizations for all the algorithms executed in the package, with the addition of linkage generation for the divisive hierarchical clustering structure.
pip install HiPart⚙️Github: https://github.com/panagiotisanagnostou/hipart
📄Paper: https://arxiv.org/abs/2209.08680v1
📎Dataset: https://paperswithcode.com/dataset/usps
@ArtificialIntelligencedl
👍4👎2❤1🔥1
A Framework for Benchmarking Clustering Algorithms
⚙️Github: https://github.com/gagolews/clustering-benchmarks
📄Paper: https://arxiv.org/abs/2209.09493v1
📎Results: https://github.com/gagolews/clustering-results-v1
@ArtificialIntelligencedl
⚙️Github: https://github.com/gagolews/clustering-benchmarks
📄Paper: https://arxiv.org/abs/2209.09493v1
📎Results: https://github.com/gagolews/clustering-results-v1
@ArtificialIntelligencedl
👍8❤1🔥1
🎼 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
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
👍6❤1🔥1
Forwarded from Machinelearning
🗣 Robust Speech Recognition via Large-Scale Weak Supervision
Whisper is a general-purpose speech recognition model by Open AI.
⚙️ Github
💡 Colab
💻 Model
🗒 Paper
🦾 Dataset
✴️ HABR
@ai_machinelearning_big_data
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
👍5🔥2🥰1
🦾 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.
⚙️Github: https://github.com/deyingk/personalizedhandmeshestimation
📄Paper: https://arxiv.org/abs/2209.10840v1
🗒Dataset: https://paperswithcode.com/dataset/dexycb
@ArtificialIntelligencedl
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
@ArtificialIntelligencedl
👍4❤1🔥1
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and Accompanied Baseline
⚙️Github: https://github.com/walker-hyf/mntts
📄Paper: https://arxiv.org/abs/2209.10848v1
🗒Dataset: https://paperswithcode.com/dataset/ljspeech
@ArtificialIntelligencedl
# 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
@ArtificialIntelligencedl
👍6❤1🔥1
🔸 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
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
👍7❤1🔥1
This media is not supported in your browser
VIEW IN TELEGRAM
🚀 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
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
👍7❤1🔥1
🦾 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
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
👍6❤1🔥1😁1
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
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
👍3❤1🔥1
🦾 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
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
👍4❤1🔥1
📰 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
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
👍8❤1🔥1
📌 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
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
👍8❤2🥰1
✔️ 4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation
⚙️Github: https://github.com/larskreuzberg/4d-stop
📄Paper: https://arxiv.org/abs/2209.14858v1
🗒Dataset: https://paperswithcode.com/dataset/semantickitti
@ArtificialIntelligencedl
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
👍6🤔2❤1🔥1
📌 ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing
An unsupervised end-to-end network for discovering sketch and extrude from point clouds.
⚙️Github: https://github.com/kimren227/extrudenet
📄Paper: https://arxiv.org/abs/2209.15632v1
@ArtificialIntelligencedl
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
👍5❤1
✔️ From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
⚙️Github: https://github.com/csxmli2016/redegnet
📄Paper: https://arxiv.org/abs/2210.00752v1
🗒Dataset: https://paperswithcode.com/dataset/realsrset
@ArtificialIntelligencedl
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
👍4❤1🔥1
✔️ Real-Time Monitoring of User Stress, Heart Rate and Heart Rate Variability on Mobile Devices
⚙️Github: https://github.com/beamai/beamaisdk-ios
📄Paper: https://arxiv.org/abs/2210.01791v1
🗒Dataset: https://paperswithcode.com/dataset/ubfc-rppg
@ArtificialIntelligencedl
⚙️Github: https://github.com/beamai/beamaisdk-ios
📄Paper: https://arxiv.org/abs/2210.01791v1
🗒Dataset: https://paperswithcode.com/dataset/ubfc-rppg
@ArtificialIntelligencedl
GitHub
GitHub - beamai/BeamAISDK-iOS: Monitor user stress, heart rate and heart rate variability through the selfie camera in real-time.…
Monitor user stress, heart rate and heart rate variability through the selfie camera in real-time. Use the Beam AI SDK inside your iOS apps today! - beamai/BeamAISDK-iOS
👍5❤2🔥1
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
Please open Telegram to view this post
VIEW IN TELEGRAM
👍6❤1🔥1
🎞 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
⚙️Github: https://github.com/gitcvfb/rssr
📄Paper: https://arxiv.org/abs/2210.03040v1
🗒Dataset: https://paperswithcode.com/dataset/carla
@ArtificialIntelligencedl
👍4🔥1🥰1
🔥 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
@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
@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
@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
@javascriptv - javascript изучение
@about_javascript - javascript продвинутый
@JavaScript_testit -тесты JS
@htmlcssjavas - web
@hashdev - web разработка
@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_kal - чат kali linux
@linuxkalii - linux kali
@linux_read - книги linux
👷♂️ IT работа
@hr_itwork - ит-ваканнсии
@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_forprogrammers - Английский для программистов
@github_code
Please open Telegram to view this post
VIEW IN TELEGRAM
👍6👎2❤1🔥1