March 2026

Python is an excellent choice for working with Application Programming Interfaces (APIs), allowing you to efficiently consume and interact with them. By using the Requests library, you can easily fetch data from APIs that communicate using HTTP, such as REST, SOAP, or GraphQL APIs. This video course covers the essentials of consuming REST APIs with Python, including authentication and handling responses. By the end of this video course, you’ll understand that: An API is an interface that allows […]

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Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottlenecked by the high cost of acquiring preference data, especially in low-resource and expert domains. To address this, we introduce ACTIVEULTRAFEEDBACK, a modular active learning pipeline that leverages uncertainty estimates to dynamically identify the most informative responses for annotation. Our pipeline facilitates the systematic evaluation of standard response selection methods alongside DOUBLE REVERSE THOMPSON SAMPLING (DRTS) and DELTAUCB, […]

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I’m lately noticing at start-ups people don’t hire someone only for knowing rl but they want me to know the full robotics stack like Ros 2, Linux, slam etc so they both go hand in hand..? I’m someone who is having 0 experience in robotics and only know rl, so is it true or what? I’m a physics major I’m learning stm 32 rn and the startup is an autonomous vehicle start-up.. So looking forward for help and […]

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Photonic Quantum Machine Learning (PQML) is an emerging approach that integrates photonic quantum computing technologies with machine learning techniques to enable scalable and energy-efficient quantum information processing. Photonic systems offer advantages such as room-temperature operation, high-speed signal processing, and the ability to represent information in high-dimensional Hilbert spaces. However, noise remains a major challenge affecting the performance, reliability, and scalability of PQML implementations. This review provides a systematic analysis of noise sources in photonic quantum machine learning systems. […]

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Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically rely on predefined functional sub-networks to construct sub-network associations. However, we identified many cross-network interaction patterns with high Pearson correlations that this strict, prior-based organization fails to capture. To overcome this limitation, we propose the Brain Hierarchical Organization Learning (BrainHO) to learn inherently hierarchical brain network dependencies based on their intrinsic features rather than predefined sub-network labels. Specifically, we […]

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Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concepts as flat and independent. To circumvent this, recent work has introduced Hierarchical Concept Embedding Models (HiCEMs) to explicitly model concept relationships, and Concept Splitting to discover sub-concepts using only coarse annotations. However, both HiCEMs and Concept Splitting are restricted to shallow hierarchies. We overcome this limitation with Multi-Level Concept Splitting (MLCS), which discovers multi-level concept hierarchies from only […]

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