The Next Input updates The Next Input-updates
Browse every published The Next Input update in a calm card overview with images, dates, and direct access to each article. Bekijk alle gepubliceerde The Next Input-updates in een rustig kaartenoverzicht met beelden, datums en directe toegang tot elk artikel.
The Next Input update The Next Input-update
Prediction and control with temporal segment models Prediction and control with temporal segment models
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The Next Input update The Next Input-update
Third-person imitation learning Third-person imitation learning
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Attacking machine learning with adversarial examples Attacking machine learning with adversarial examples
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult. Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult.
The Next Input update The Next Input-update
Adversarial attacks on neural network policies Adversarial attacks on neural network policies
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Team update Team update
Title: Team update Title: Team update
The Next Input update The Next Input-update
PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
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Faulty reward functions in the wild Faulty reward functions in the wild
Reinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function. Reinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function.
The Next Input update The Next Input-update
Universe Universe
We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications. We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.
The Next Input update The Next Input-update
OpenAI and Microsoft OpenAI and Microsoft
We’re working with Microsoft to start running most of our large-scale experiments on Azure. We’re working with Microsoft to start running most of our large-scale experiments on Azure.
The Next Input update The Next Input-update
#Exploration: A study of count-based exploration for deep reinforcement learning #Exploration: A study of count-based exploration for deep reinforcement learning
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On the quantitative analysis of decoder-based generative models On the quantitative analysis of decoder-based generative models
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A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models
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Showing 937 to 948 of 993 updates. Je bekijkt 937 tot 948 van 993 updates.