🖥 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

🔥 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

🏓 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

🖥 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

💻 A Closer Look at Hardware-Friendly Weight Quantization

⚙️Github: https://github.com/google/qkeras

📄Paper: https://arxiv.org/abs/2210.03671v1

🗒Training: https://github.com/BertMoons/QuantizedNeuralNetworks-Keras-Tensorflow

@ArtificialIntelligencedl

🗄 NerfAcc: A General NeRF Acceleration Toolbox

pip install nerfacc

⚙️Github: https://github.com/kair-bair/nerfacc

📄Paper: https://arxiv.org/abs/2210.04847v1

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

@ArtificialIntelligencedl

👣 OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds

⚙️Github: https://github.com/vlar-group/ogc

📄Paper: https://arxiv.org/abs/2210.04458v1

↪️ Demo: https://www.youtube.com/watch?v=dZBjvKWJ4K0

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

@ArtificialIntelligencedl

🛠 Understanding Embodied Reference with Touch-Line Transformer

conda create --name nvvc python=3.8
conda activate nvvc
pip install -r requirements.txt
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

⚙️Github: https://github.com/yang-li-2000/understanding-embodied-reference-with-touch-line-transformer

📄Paper: https://arxiv.org/abs/2210.05668v2

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

@ArtificialIntelligencedl

🖥 Exploring Long-Sequence Masked Autoencoders

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

📄Paper: https://arxiv.org/abs/2210.07224v1

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

@ArtificialIntelligencedl