Artificial Intelligence – Telegram
Artificial Intelligence
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Artificial Intelligence

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⭐️ RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning


🖥 Github: https://github.com/tencent-ailab/rlogist

🗒 Paper: https://arxiv.org/abs/2212.01737v1

➡️ Dataset: https://paperswithcode.com/dataset/nus-wide

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🔼 IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation

🖥 Github: https://github.com/shendu0321/incepformer

✔️ Project: https://github.com/shendu0321/IncepFormer

🗒 Paper: https://arxiv.org/abs/2212.03035v1

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

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🔥 C-VTON: Context-Driven Image-Based Virtual Try-On Network

🖥 Github: https://github.com/benquick123/c-vton

🗒 Paper: https://arxiv.org/abs/2212.04437v1

📍 Dataset: https://paperswithcode.com/dataset/viton-hd

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🛠 Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks

git clone https://github.com/Graph-Learning-Benchmarks/gli.git
cd gli
pip install -e .


🖥 Github: https://github.com/graph-learning-benchmarks/gli

Paper: https://arxiv.org/abs/2212.04537v1

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

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➡️ Reinforcement Learning and Tree Search Methods for the Unit Commitment Problem

pip install rl4uc

🖥 Github: https://github.com/pwdemars/rl4uc

Paper: https://arxiv.org/abs/2212.06001v1

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

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✔️ MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

🖥 Github: https://github.com/pwdemars/rl4uc

Paper: https://github.com/facebookresearch/modem

➡️ Website: https://nicklashansen.github.io/modemrl

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🖥 Nerf-Art: Text-Driven Neural Radiance Fields Stylization

git clone https://github.com/cassiePython/NeRF-Art

🖥 Github: https://github.com/cassiePython/NeRF-Art

✔️ Project: https://cassiepython.github.io/nerfart/index.html

Paper: https://arxiv.org/abs/2212.08070

➡️ Dataset: https://drive.google.com/drive/folders/12zOhjv4CrUC-z3n4uF-qHNrT7doZcsVA

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MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers

🖥 Github: https://github.com/microsoft/simxns

Paper: https://arxiv.org/abs/2212.07841v1

➡️ Dataset: https://paperswithcode.com/dataset/natural-questions

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💨 Medfusion - Medical Denoising Diffusion Probabilistic Model

python -m venv venv
source venv/bin/activate
pip install -e .


🖥 Github: https://github.com/mueller-franzes/medfusion

Paper: https://arxiv.org/abs/2212.07501v1

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

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🍪 Azimuth

Azimuth, an open-source dataset and error analysis tool for text classification, with love from ServiceNow.

pip install gdown
make download_demo
make CFG_PATH=/config/development/clinc/conf.json launch

🖥 Github: https://github.com/servicenow/azimuth

Paper: https://arxiv.org/abs/2212.08216v1

➡️ Docs: https://servicenow.github.io/azimuth

🔧 Demo: https://azimuth-demo.net/

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⭐️ Position-guided Text Prompt for Vision-Language Pre-training

🖥 Github: https://github.com/sail-sg/ptp

Paper: https://arxiv.org/abs/2212.09737v1

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

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✔️ Query-as-context Pre-training for Dense Passage Retrieval

🖥 Github: https://github.com/caskcsg/ir

Paper: Query-as-context Pre-training for Dense Passage Retrieval

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

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📎 Open-Domain Multi-Document Summarization

pip install "git+https://github.com/allenai/open-mds.git"

🖥 Github: https://github.com/allenai/open-mds

Paper: https://arxiv.org/abs/2212.10526v1

➡️ Dataset: https://paperswithcode.com/dataset/multi-news

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🌐 SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Esimation

$git clone git@github.com:Yang-Kaixing/SHLE.git

$cd SHLE

$pip install -r requirements.txt


🖥 Github: https://github.com/yang-kaixing/shle

Paper: https://arxiv.org/abs/2212.11538v1

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⭐️ TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers

🖥 Github: https://github.com/airi-institute/transpath

Paper: https://arxiv.org/abs/2212.11730v1

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Scalable Adaptive Computation for Iterative Generation

$ pip install isab-pytorch

🖥 Github: https://github.com/lucidrains/isab-pytorch

Paper: https://arxiv.org/abs/2212.11972v1

➡️ Dataset: https://paperswithcode.com/dataset/kinetics-600

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⭐️ Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective

🖥 Github: https://github.com/zhichao-lu/robust-residual-network

Paper: https://arxiv.org/abs/2212.11005v1

➡️ Сheckpoints: https://github.com/zhichao-lu/robust-residual-network/blob/main

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