🖥 NVIDIA Federated Learning Application Runtime Environment

NVIDIA FLARE enables researchers to collaborate and build AI models without sharing private data.

pip install nvflare

🖥 Github: https://github.com/nvidia/nvflare

📄Paper: https://arxiv.org/abs/2210.13291v1

🔩 Starter guide: https://nvflare.readthedocs.io/en/main/getting_started.html

@ArtificialIntelligencedl

🖥 Evaluating Long-Term Memory in 3D Mazes

pip install memory-maze

🖥 Github: https://github.com/jurgisp/memory-maze

📄Paper: https://arxiv.org/abs/2210.13383v1

🔩 Starter guide: https://www.dropbox.com/sh/c38sc5h7ltgyyzc/AAARVeKgnyaoBLGdYYVABh_Ja

@ArtificialIntelligencedl

⭐️DiffusionDB

DiffusionDB is the first large-scale text-to-image prompt dataset.

🖥 Github: https://github.com/poloclub/diffusiondb

🗒 Paper: https://arxiv.org/abs/2210.14896v1

➡️ Dataset: https://huggingface.co/datasets/poloclub/diffusiondb

@ArtificialIntelligencedl

⭐️ Streaming Radiance Fields for 3D Video Synthesis

🖥 Github: https://github.com/algohunt/streamrf

🗒 Paper: https://arxiv.org/abs/2210.14831v1

➡️ Dataset: https://github.com/facebookresearch/Neural_3D_Video

@ArtificialIntelligencedl

🖥 MSMC-TTS: Multi-Stage Multi-Codebook TTS

🖥 Github: https://github.com/hhguo/msmc-tts

🗒 Paper: https://arxiv.org/abs/2210.15131v1

➡️ FastSpeech Implementation: https://github.com/NVIDIA/NeMo

@ArtificialIntelligencedl

🔥 Best it channels

🖥 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
@machinelearning_books - ml notes
@bigdatai - big data
@vistehno - ai news

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

📢 English for coders

@english_forprogrammers - Английский для программистов

🖥 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
@react_tg - react js
@javascriptv - js канал
@about_javascript - javascript продвинутый
@JavaScript_testit -тесты JS
@htmlcssjavas - web
@hashdev - web разработка

👣 Golang
@Golang_google - go для разработчиков
@golang_interview - вопросы и ответы с собеседований по Go.
@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-вакансии
@GolangJobsit
@python_djangojobs - python jobs
@javascriptjobjs - js jobs
@Java_workit - java jobs

🖥 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

🖥 Github
@github_code

🔑 What Language Model to Train if You Have One Million GPU Hours?

🖥 Github: https://github.com/bigscience-workshop/Megatron-DeepSpeed

🗒 Paper: https://arxiv.org/abs/2210.15424v1

➡️ Dataset: https://paperswithcode.com/dataset/the-pile

@ArtificialIntelligencedl

⭐️ Masked Vision-Language Transformer in Fashion

🖥 Github: https://github.com/gewelsji/mvlt

🗒 Paper: https://arxiv.org/abs/2210.15110v1

➡️ Dataset: https://paperswithcode.com/dataset/fashion-gen

@ArtificialIntelligencedl

⭐️ Differentiable Analog Quantum Computing for Learning and Control

git clone [email protected]:YilingQiao/diffquantum.git
cd diffquantum
pip install -r requirements.txt
git submodule init
git submodule update
python setup.py install

🖥 Github: https://github.com/yilingqiao/diffquantum

🗒 Paper: https://arxiv.org/abs/2210.15812v1

@ArtificialIntelligencedl

⏩ Open MatSci ML Toolkit : A Flexible Framework for Deep Learning on the OpenCatalyst Dataset

🖥 Github: https://github.com/intellabs/matsciml

🗒 Paper: https://arxiv.org/abs/2210.17484v1

➡️ Dataset: https://paperswithcode.com/dataset/cluster

@ArtificialIntelligencedl

🖥 Dataset Distillation via Factorization

git clone https://github.com/Huage001/DatasetFactorization.git
cd DatasetFactorization
pip install -r requirements.txt

🖥 Github: https://github.com/huage001/datasetfactorization

🗒 Paper: https://arxiv.org/abs/2210.16774v1

➡️ Dataset: https://paperswithcode.com/dataset/svhn

@ArtificialIntelligencedl

💻 Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

🖥 Github: https://github.com/redwoodresearch/easy-transformer

🗒 Paper: https://arxiv.org/abs/2211.00593v1

➡️ Colab: https://colab.research.google.com/drive/1UfT8HQxHpmSKD1HjWcoUYETiYlL8_XB8?usp=sharing .

@ArtificialIntelligencedl

🌃 Big Data - действительно полезный блог для всех, кто работает с данными.

- огромный набор бесплатных датасетов
- инструменты для работы с данными
- обучающие материалы для датасаентистов
- перевод книг и статей

🔥 Смотреть

👣 Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models

🖥 Github: https://github.com/lmxyy/sige

📍 Project: https://www.cs.cmu.edu/~sige/

🗒 Paper: https://arxiv.org/abs/2211.02048v1

➡️ Dataset: https://paperswithcode.com/dataset/cityscapes

@ArtificialIntelligencedl

⭐️ Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulation

$ git clone [email protected]:rpmsnu/PrimA6D.git

🖥 Github: https://github.com/rpmsnu/prima6d

🗒 Paper: https://arxiv.org/abs/2211.00960v1

➡️ Dataset: https://paperswithcode.com/dataset/ycb-video

@ArtificialIntelligencedl

✅ Large Language Models Are Human-Level Prompt Engineers

Automatic Prompt Engineer (APE) for automatic instruction generation and selection.

pip install -e .
🖥 Github: https://github.com/keirp/automatic_prompt_engineer

👣 Colab: https://colab.research.google.com/drive/1Hrz6Q7GFdH5OVg3Dis86f5OqiGdkDfRP?usp=sharing

🖥 Project: https://sites.google.com/view/automatic-prompt-engineer

🗒 Paper: https://arxiv.org/abs/2211.01910v1

➡️ Demo: https://colab.research.google.com/drive/1oL1CcvzRybAbmeqs--2csaIvSOpjH072?usp=sharing

@ArtificialIntelligencedl

💫 Galaxy Image Deconvolution for Weak Gravitational Lensing with Physics-informed Deep Learning

git clone https://github.com/Lukeli0425/Galaxy-Deconv.git

🖥 Github: https://github.com/Lukeli0425/Galaxy-Deconv

🗒 Paper: https://arxiv.org/abs/2211.01567v1

➡️ Dataset: https://drive.google.com/drive/folders/1IwgvbetMDpLK2skRalYWmth2J1gvF-qm

@ArtificialIntelligencedl

WEBVICOB 🕸 : WEB based VIsual COrpus Builder

🖥 Github: https://github.com/clovaai/webvicob

🗒 Paper: https://arxiv.org/abs/2211.03256v1

➡️ Dataset: https://dumps.wikimedia.org/other/enterprise_html/runs

@ArtificialIntelligencedl

Robust Reflection Removal with Reflection-free Flash-only Cues (RFC)

conda env create -f environment.yml
conda activate flashrr-rfc
bash download.sh
python test.py

🖥 Github: https://github.com/ChenyangLEI/flash-reflection-removal

🗒 Paper: https://arxiv.org/abs/2211.02914v1

⭐️ Project: https://chenyanglei.github.io/flashrr_rfc/index.html

➡️ Dataset: https://hkustconnect-my.sharepoint.com/:u:/g/personal/cleiaa_connect_ust_hk/EWv1afaxrhFKlbT7iX0b8FMB8R1ZeNyUWRQM__A_SPkVGQ?e=8IbhE6

@ArtificialIntelligencedl

💬 MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation

🖥 Github: https://github.com/victorsungo/mmdialog

🗒 Paper: https://arxiv.org/abs/2211.05719v1

➡️ Dataset: https://paperswithcode.com/dataset/openvidial

@ArtificialIntelligencedl