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Advanced Privacy-Preserving Federated Learning (APPFL): An AI Framework to Address Data Heterogeneity, Computational Disparities, and Security Challenges in Decentralized Machine Learning
Federated learning (FL) is a powerful ML paradigm that enables multiple data owners to train models without centralizing their data collaboratively. This approach is particularly valuable in domains where data privacy is critical, such as healthcare, finance, and the energy sector. The core of federated learning lies in training models on decentralized data stored on…
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Google AI Introduces the Open Buildings 2.5D Temporal Dataset that Tracks Building Changes Across the Global South
Governments and humanitarian organizations need reliable data on building and infrastructure changes over time to manage urbanization, allocate resources, and respond to crises. However, many regions across the Global South need more access to timely and accurate data on buildings, making it difficult to track urban growth and infrastructure development. The absence of this data…
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This Research Paper Discusses Space-Efficient Algorithms for Integer Programming with Few Constraints
Integer Linear Programming (ILP) is the foundation of combinatorial optimization, which is extensively applied across numerous industries to resolve challenging decision-making issues. Under a set of linear equality constraints, an ILP aims to minimize or maximize a linear objective function, with the important condition that all variables must be integers. Even while ILP is an…
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HARP (Human-Assisted Regrouping with Permutation Invariant Critic): A Multi-Agent Reinforcement Learning Framework for Improving Dynamic Grouping and Performance with Minimal Human Intervention
Multi-agent reinforcement learning (MARL) is a field focused on developing systems where multiple agents cooperate to solve tasks that exceed the capabilities of individual agents. This area has garnered significant attention due to its relevance in autonomous vehicles, robotics, and complex gaming environments. The aim is to enable agents to work together efficiently, adapt to…
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Exploring Input Space Mode Connectivity: Insights into Adversarial Detection and Deep Neural Network Interpretability
Input space mode connectivity in deep neural networks builds upon research on excessive input invariance, blind spots, and connectivity between inputs yielding similar outputs. The phenomenon exists generally, even in untrained networks, as evidenced by empirical and theoretical findings. This research expands the scope of input space connectivity beyond out-of-distribution samples, considering all possible inputs.…
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From Single Trees to Forests: Enhancing Real Estate Predictions with Ensembles
This post dives into the application of tree-based models, particularly focusing on decision trees, bagging, and random forests within the Ames Housing dataset. It begins by emphasizing the critical role of preprocessing, a fundamental step that ensures our data is optimally configured for the requirements of these models. The path from a single decision tree…
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Why prompt engineering is one of the most valuable skills today
Prompt engineering is shaping how we interact with and benefit from AI. Here’s how to get prompt engineering right.Read More
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The best robot vacuums for 2024: Expert tested and reviewed
We’ve tested nearly every popular robot vacuum on the market from brands like Roomba, Dreame, Narwal, Roborock, and more. These are our favorite models.
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18 Best Fitness Trackers (2024), Tested and Reviewed
Whether you’re skiing in the backcountry or trampolining in the backyard, we have an activity tracker for you.
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4 Useful Slack Features You May not Be Using Yet
From Canvases to Lists, learn how to use some of these recently added—but still not widely known—Slack features.