UC Berkeley Reward-Free RL Beats SOTA Reward-Based RL
Augmented Temporal Contrast (ATC), a new unsupervised learning (UL) task for learning visual representations agnostic to rewards and without degrading the control policy.
Tag: Unsupervised Learning
Geoffrey Hinton & Google Brain Unsupervised Learning Algorithm Improves SOTA Accuracy on ImageNet by 7%
Researchers have proposed a simple but powerful “SimCLR” framework for contrastive learning of visual representations.
Unsupervised Approach for GAN Interpretability Through Semantic Direction Discovery
Researchers have introduced the first unsupervised learning approach for identifying interpretable semantic directions in the latent space of generative adversarial network (GAN) models.
Geoffrey Hinton’s Unsupervised Capsule Networks Achieve SOTA Results on SVHN
In 2017 the “Godfather of Deep Learning” Geoffrey Hinton and his students Sara Sabour and Nicholas Frosst proposed the discrimininatively trained, multi-layer capsule system “CapsNet” in their paper Dynamic Routing Between Capsules.
Big Announcements from Facebook F8
Synced Global AI Weekly May 5th
Yann LeCun Cake Analogy 2.0
Facebook AI Chief Yann LeCun introduced his now-famous “cake analogy” at NIPS 2016: “If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning (RL).”
DataVisor Uses Unsupervised Learning to Combat Online Fraud
DataVisor is a Mountain View, California based anti-fraud startup that uses AI to detect fake accounts, prevent money laundering, and protect financial institutions from credit scams. The company offers clients its DataVisor APIs for real-time data connection, and a specialized UI for direct results.
Unsupervised Image-to-Image Translation with Generative Adversarial Networks
This paper proposes a general approach to achieve “Image-to-Image Translation” by using deep convolutional and conditional generative adversarial networks (GANs).
Deep Patient: Improving Prognosis with Electronic Health Records by Deep Learning
This paper applies deep learning to a large-scale EHR dataset to extract robust patient descriptors that can be used to predict future patient diseases.
Yann Le Cun: Predicting under Uncertainty, the Next Frontier in AI
professor Yann LeCun discussed about “predicting under uncertainty: the next frontier in AI” during the lecture at the University of Edinburgh