🔥 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

🗄 SUPERB-prosody: On The Utility of Self-supervised Models for Prosody-related Tasks

⚙️Github: https://github.com/jsalt-2022-ssl/superb-prosody

📄Paper: https://arxiv.org/abs/2210.07185v1

🗒Tasks: https://paperswithcode.com/task/prosody-prediction

@ArtificialIntelligencedl

🖥 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: https://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