Business-to-business (B2B) transfer learning is a method of using machine learning algorithms to transfer knowledge from one building to predict the energy consumption of another building. This is particularly useful when one building has scarce data available for analysis.
What is Transfer Learning?
Transfer learning is a machine learning technique in which a model trained on one task is used to make predictions on a different task. The idea is to use the knowledge gained from one task to he
Overview of CLIPort
CLIPort is a unique artificial intelligence (AI) technology that uses the combined power of two pre-existing models to create a novel type of AI agent. This particular agent combines the strengths of two previously separate AI models known as CLIP and Transporter.
Both of these AI models were created to learn and understand different things about the visual world around them. CLIP specializes in semantic understanding, or the ability to recognize and interpret the meanings
Are you interested in machine learning, but intimidated by complex algorithms and coding? IQ-Learn is here to simplify the process of imitation learning. It is a simple, stable, and data-efficient framework that directly learns soft Q-functions from expert data. With IQ-Learn, you can perform non-adversarial imitation learning on both offline and online settings, even with sparse expert data. Plus, it scales well in image-based environments, surpassing prior methods by more than three times.
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Parrot: An Imitation Learning Approach to Cache Access Patterns
Parrot is an imitation learning approach that automates the process of learning cache access patterns. This process is achieved by leveraging Belady's optimal policy, an oracle policy that computes the ideally optimum cache eviction decision based on the knowledge of the future cache accesses. Parrot approximates this process by conditioning on the past accesses, defining a policy that efficiently enhances the performance of cache
Primal Wasserstein Imitation Learning (PWIL)
Primal Wasserstein Imitation Learning (PWIL) is an approach to machine learning that employs the Wasserstein Distance to teach machines how to imitate or learn from expert behavior. It pertains to the primal form of the Wasserstein distance between the expert and agent state-action distributions. This means that it is more efficient, requires less fine-tuning, and is generally more effective than recent adversarial IL algorithms, which learn a reward