A Deep Variational Approach to Clustering Survival Data

Github: https://github.com/i6092467/vadesc

Paper: https://arxiv.org/abs/2106.05763v1

@ArtificialIntelligencedl

Part-aware Panoptic Segmentation

Github: https://github.com/pmeletis/panoptic_parts

Paper: https://arxiv.org/abs/2106.06351v1

Docs: https://panoptic-parts.readthedocs.io/en/stable

@ArtificialIntelligencedl

Optimal transport in multilayer networks

Github: https://github.com/cdebacco/MultiOT

Paper: https://arxiv.org/abs/2106.07202v1


@ArtificialIntelligencedl

☑️ Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

Github: https://github.com/cf020031308/LinkDist

Paper: https://arxiv.org/abs/2106.08541v1

@ArtificialIntelligencedl

Deep Transfer Learning on PyTorch

Github: https://github.com/easezyc/deep-transfer-learning

Paper: https://arxiv.org/abs/2106.09388v1

@ArtificialIntelligencedl

XCiT: Cross-Covariance Image Transformers

Github: https://github.com/facebookresearch/xcit

Paper: https://arxiv.org/abs/2106.09681v1

@ArtificialIntelligencedl

LMs for biomedical KG completion

Github: https://github.com/rahuln/lm-bio-kgc

Paper: https://arxiv.org/abs/2106.09700

@ArtificialIntelligencedl

♠️ RLCard: A Toolkit for Reinforcement Learning in Card Games

Github: https://github.com/datamllab/rlcard

DouZero: https://github.com/kwai/DouZero

Paper: https://arxiv.org/abs/2106.06135v1

@ArtificialIntelligencedl

Adversarial poison generation and evaluation

Github: https://github.com/lhfowl/adversarial_poisons

Paper: https://arxiv.org/abs/2106.10807

@ArtificialIntelligencedl

🧠 Tiny CUDA Neural Networks

Github: https://github.com/nvlabs/tiny-cuda-nn

Paper: https://arxiv.org/abs/2106.12372v1

@ArtificialIntelligencedl

Cartoon-StyleGan2 🙃 : Fine-tuning StyleGAN2 for Cartoon Face Generation

Github: https://github.com/happy-jihye/Cartoon-StyleGan2

Paper: https://arxiv.org/abs/2106.12445

Colab: https://colab.research.google.com/github/happy-jihye/Cartoon-StyleGan2/blob/main/Cartoon_StyleGAN2.ipynb

@ai_machinelearning_big_data

VOLO: Vision Outlooker for Visual Recognition

Github: https://github.com/sail-sg/volo

Paper: https://arxiv.org/abs/2106.13112

@ai_machinelearning_big_data

Neural Network Pruning

Github: https://github.com/lucaslie/torchprune

Paper: https://arxiv.org/abs/2106.12718v1

@ArtificialIntelligencedl

CAMS: Color-Aware Multi-Style Transfer

Github: https://github.com/mahmoudnafifi/color-aware-style-transfer

Paper: https://arxiv.org/abs/2106.13920v1

@ArtificialIntelligencedl

AI Workshop: Bring Your Use Case Ideas to Life

In this workshop, we present how you can use AI services and start prototyping in minutes.
We cover some of the most popular AI verticals such as Computer Vision and Natural Language Processing.

After this workshop, you will know:
1. What is AI as a Service, and how does it work?
2. What are Computer Vision, NLP, and Speech?
3. How can you prototype your AI products before actual implementation?

Requirements:

✅ Free Registration
✅ Basic Python knowledge

https://www.linkedin.com/events/6814928657631473664/

Deep Equilibrium Models

Github: https://github.com/locuslab/deq

Paper: https://arxiv.org/abs/2106.14342v1

@ArtificialIntelligencedl

Multilayer Networks for Text Analysis with Multiple Data Types

Github: https://github.com/martingerlach/hSBM_Topicmodel

Paper: https://arxiv.org/pdf/2106.15821v1.pdf

@ArtificialIntelligencedl

PlanSys2: A Planning System Framework for ROS2

Github: https://github.com/IntelligentRoboticsLabs/ros2_planning_system

Paper: https://arxiv.org/abs/2107.00376v1

@ArtificialIntelligencedl

Deep Transfer Learning Baselines for Sentiment Analysis in Russian

Github: https://github.com/sismetanin/sentiment-analysis-in-russian

Paper: https://www.sciencedirect.com/science/article/abs/pii/S0306457320309730?dgcid=author

@ArtificialIntelligencedl

Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets

Github: https://github.com/HayeonLee/MetaD2A

Paper: https://arxiv.org/abs/2107.00860v1

@ArtificialIntelligencedl

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds (CVPR 2020)

Github: https://github.com/QingyongHu/RandLA-Net

Paper: https://arxiv.org/abs/1911.11236

Project: http://randla-net.cs.ox.ac.uk/

@ArtificialIntelligencedl