🖥 Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief

⚙️Github: https://github.com/huawei-noah/HEBO/tree/master/PMDB

📄Paper: https://arxiv.org/abs/2210.06692v1

↪️ Project: https://research.nianticlabs.com/mapfree-reloc-benchmark

🗒Dataset: https://paperswithcode.com/dataset/d4rl

@ai_machinelearning_big_data

🔩 SubeventWriter: Iterative Sub-event Sequence Generation with Coherence Controller

⚙️Github: https://github.com/hkust-knowcomp/subeventwriter

📄Paper: https://arxiv.org/abs/2210.06694v1

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

@ArtificialIntelligencedl

🖥 Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation

⚙️Github: https://github.com/law-ai/summarization

📄Paper: https://arxiv.org/pdf/2210.07544v1.pdf

🗒Dataset: http://www.liiofindia.org/in/cases/cen/INSC/

@ArtificialIntelligencedl

✅ GNN-Motion-Planning

⚙️Github: https://github.com/rainorangelemon/gnn-motion-planning

📄Paper: https://arxiv.org/abs/2210.08864v1

🗒Project: https://rainorangelemon.github.io/NeurIPS2021/

@ArtificialIntelligencedl

🖥 RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

⚙️Github: https://github.com/m3dv/ribseg

📄Paper: https://arxiv.org/abs/2210.09309v1

🗒Dataset: https://doi.org/10.5281/zenodo.5336592

@ArtificialIntelligencedl

🖥 HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks (NeurIPS 2022)

🖥 Github: https://github.com/macderru/hyperdomainnet

📄Paper: https://arxiv.org/abs/2210.08884v2

🔩 Colab: https://colab.research.google.com/drive/1QMylWjzPxvHtxm74U4lWRQXwquw5AaFL#scrollTo=si2tLKYLT-kV

@ArtificialIntelligencedl

↪️ Targeted Adversarial Self-Supervised Learning

🖥 Github: https://github.com/Kim-Minseon/RoCL

📄Paper: https://arxiv.org/abs/2210.10482v1

🔩 Adversarial Self-Supervised Contrastive Learning: https://sites.google.com/view/rocl2020

@ArtificialIntelligencedl

⏩ Learning to Discover and Detect Objects

🖥 Github: https://github.com/vlfom/rncdl

➡️ Project: https://vlfom.github.io/RNCDL/

📄Paper: https://arxiv.org/abs/2210.10774v1

🔩 Dataset: https://paperswithcode.com/dataset/visual-genome

@ArtificialIntelligencedl

⭐️ Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions

🖥 Github: https://github.com/sinzlab/cgnf

➡️ Model: https://github.com/sinzlab/propose/tree/0.2.0/propose/models/flows

📄Paper: https://arxiv.org/abs/2210.11179v1

🔩 Dataset: https://paperswithcode.com/dataset/human3-6m

@ArtificialIntelligencedl

🖥 TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation

🖥 Github: https://github.com/air-discover/toist

📄Paper: https://arxiv.org/abs/2210.10775v1

🔩 Dataset: https://github.com/coco-tasks/dataset

@ArtificialIntelligencedl

Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

🖥 Github: https://github.com/mv-lab/AISP

📄Paper: https://arxiv.org/abs/2210.11153v1

🔩 Starter guide: https://github.com/mv-lab/AISP/blob/main/aim22-reverseisp/official-starter-code.ipynb

@ArtificialIntelligencedl

🖥 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 git@github.com: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