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  • Left: Performance of the “CLIP” model on accurately providing labels for images, dramatically outperforming previous work. Image from https://arxiv.org/pdf/2103.00020.pdf. Right: Summarizing a model’s performance by a single number is only one piece of information. Once this information is actually used to make a decision, we will also need to understand the different ways the model can fail. Image: own work.
    Reasoning About Uncertainty using Markov Chains
    Formal methods to tackle “Trial-and-Error” problems The ability to deal with unseen objects in a zero-shot manner makes machine learning models very attractive for applications in robotics, allowing robots to enter previously unseen
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  • Principal Component Analysis of a random 2D point cloud using PyTorch’s built-in function. Image by the author.
    Understanding Principal Component Analysis in PyTorch
    Built-in function vs. numerical methods PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine learning
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