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Latest CMU Research Improves Reinforcement Learning With Lookahead Policy: Learning Off-Policy...

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Reinforcement learning (RL) is a technique that allows artificial agents to learn new tasks by interacting with their surroundings. Because of their capacity to...

CMU Researchers Propose A Computer Vision-Based Approach With Data-Frugal Deep Learning...

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Materials processing is the process of turning raw materials into final items through a sequence of phases or "unit operations." The activities entail a...

Meta AI and CMU Researchers Present ‘BANMo’: A New Neural Network-Based...

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Previous work on articulated 3D shape reconstruction has frequently relied on specialized sensors (e.g., synchronized multi-camera systems) or pre-built 3D deformable models (e.g., SMAL...

AI Researchers Propose ‘GANgealing’: A GAN-Supervised Algorithm That Learns Transformations of...

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The correspondence problem of visual alignment is one that computer vision algorithms must solve for many different applications.It's considered a critical element in Optical...

Researchers Develop A Unified Framework For Evaluating Natural Language Generation (NLG)

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Natural language generation (NLG) is a broad term that encompasses a variety of tasks that generate fluent text from input data and other contextual...

CMU AI Researchers Present A New Study To Achieve Fairness and...

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The rapid rise in machine learning applications in criminal justice, hiring, healthcare, and social service intentions substantially impacts society. These wide applications have heightened...

CMU Researchers Introduce ‘CatGym’, A Deep Reinforcement Learning (DRL) Environment For...

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It isn't an easy task to design efficient new catalysts. In the case of multiple element mixtures, for example - researchers must take into...

CMU and MIT AI Researchers Present A New Method To Sketch...

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Sketching is the most universally accessible way to convey a visual concept. In contrast, creating GAN models has traditionally required knowledge in deep learning...

Researchers at Facebook AI, UC Berkeley, and Carnegie Mellon University Announced...

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To achieve success in the real world, walking robots must adapt to whatever surfaces they encounter, objects they carry, and conditions they are in,...

Researchers from UC Berkeley and CMU Introduce a Task-Agnostic Reinforcement Learning...

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Applying Deep Learning techniques to complex control tasks depends on simulations before transferring models to the real world. However, there is a challenging “reality...
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