AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates on Deep Learning, Machine Learning, and Computer Vision (with Papers).

Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
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Our lecture about Big Data and Artificial Intelligence at TIM WCAP, Milan: https://www.linkedin.com/feed/update/urn:li:activity:6364544686064955392

Soon, the full slideshow available.
How we are changing the client's pipeline with #ArtificialIntelligence

- LEFT: fully human | human release, 2016-2017
- CENTER: #AI + human | alpha release, Q1 2018
- RIGHT: #AI + human | beta release, est. Q3 2018

#DeepLearning #MachineLearning #convergeAI #VR
The Random Network Distillation (RND) is a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity. No extrinsic reward is needed but the network will learn it by itself.

Tested across 50+ different games, it worked pretty well. Some of the agent learnt the game's objective even though the objective was not set through an extrinsic reward.

Article: https://lnkd.in/dUtf2JZ
Paper: https://lnkd.in/dRpZXu4
Code: https://lnkd.in/d5B__cE
AI with Papers - Artificial Intelligence & Deep Learning pinned «Open Position | full-stack developer for ML,CV, AI | More: https://www.linkedin.com/feed/update/urn:li:activity:6491988838486020096/ #artificialIntelligence #computervision #machinelearning #ai #javascript #java #linux»
Leaving Milan to attend C1A0_expo in Genoa! Anyone there?

Ready for my talk about computer vision and neural evolution? Topic: object detection, semantic segmentation, unconventional adversarial attacks & neural parking.

More: https://www.linkedin.com/posts/argo-vision_artificialintelligence-deeplearning-smartparking-activity-6601033667391430657-NpN3
⚡️ The best #AI of the week: #deeplearning, #computervision & #NLP free resources from across the internet in one place, every week. ⚡️

💡 Google | Face and Hand tracking in the browser with MediaPipe and TensorFlow: https://lnkd.in/dQRSB7U

💡 Berkeley | Speeding Up Transformer Training and Inference By Increasing Model Size: https://lnkd.in/dV5qZ4S

💡 CVPR-2020 | Zero-shot video classification by end-to-end training of 3D convolutional neural networks: https://lnkd.in/djs98Ew

💡 Google | Connecting Vision and Language with Localized Narratives: https://lnkd.in/dD6hjUq

More: https://www.linkedin.com/feed/update/urn:li:activity:6643790787312140288