GTC 2019 | Highlights & Disappointments at NVIDIA’s Annual Conference
NVIDIA’s annual GPU Technology Conference (GTC) attracted some 9,000 developers, buyers and innovators to San Jose, California this week. CEO and Co-Founder Jensen Huang’s two-and-a-half hour keynote speech fused GPU-based innovations in domains ranging from graphic design to autonomous driving.
GTC 2019 | Huang Kicks Off GTC, Focuses on NVIDIA Data Center Momentum, Blue Chip Partners
NVIDIA’s message was unmistakable as it kicked off the 10th annual GPU Technology Conference: it’s doubling-down on the data center. Founder and CEO Jensen Huang delivered a sweeping opening keynote at San Jose State University, describing the company’s progress accelerating the sprawling data centers that power the world’s most dynamic industries.
(NVIDIA) / (GTC 2019 Keynote)
GTC 2019 | NVIDIA’s New GauGAN Transforms Sketches Into Realistic Images
GTC 2019 | New NVIDIA One-Stop AI Framework Accelerates Workflows by 50x
GTC 2019 | NVIDIA CEO Says No Rush on 7nm GPU; Company Clearing Its Crypto Chip Inventory
GTC 2019 | Toyota doubles down on Nvidia tech for self-driving cars
GTC 2019 | Nvidia’s T4 GPUs are coming to the AWS cloud
Coconet: The ML Model Behind Today’s Bach Doodle
Google celebrated J.S. Bach’s 334th birthday with the first AI-powered Google Doodle. They introduce Coconet, the machine learning model behind the Doodle. People can create their own melody, and the machine learning model will harmonize it in Bach’s style.
Reducing The Need for Labeled Data in Generative Adversarial Networks
Generative adversarial networks (GANs) are a powerful class of deep generative models.The main idea behind GANs is to train two neural networks: the generator, which learns how to synthesise data (such as an image), and the discriminator, which learns how to distinguish real data from the ones synthesised by the generator.
Implicit Generation And Generalization in Energy-Based Models
In this work, researchers advocate for using continuous energy-based models (EBMs), represented as neural networks, for generative tasks and as a means for generalizable models. These models aim to learn an energy function E(x) that assigns low energy values to inputs x in the data distribution and high energy values to other inputs.
(MIT & OpenAI)
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