Training data attribution (TDA) methods offer to trace a model's prediction on any given example back to specific influential training examples. Existing approaches do so by assigning a scalar influence score to each training example, under a simplifying assumption that influence is additive. But in reality, we observe that training examples interact in highly non-additive ways due to factors such as inter-example redundancy, training order, and curriculum learning effects. To study such interactions, we propose Simfluence, a new paradigm for TDA where the goal is not to produce a single influence score per example, but instead a training run simulator: the user asks, ``If my model had trained on example $z_1$, then $z_2$, ..., then $z_n$, how would it behave on $z_{test}$?''; the simulator should then output a simulated training run, which is a time series predicting the loss on $z_{test}$ at every step of the simulated run. This enables users to answer counterfactual questions about what their model would have learned under different training curricula, and to directly see where in training that learning would occur. We present a simulator, Simfluence-Linear, that captures non-additive interactions and is often able to predict the spiky trajectory of individual example losses with surprising fidelity. Furthermore, we show that existing TDA methods such as TracIn and influence functions can be viewed as special cases of Simfluence-Linear. This enables us to directly compare methods in terms of their simulation accuracy, subsuming several prior TDA approaches to evaluation. In experiments on large language model (LLM) fine-tuning, we show that our method predicts loss trajectories with much higher accuracy than existing TDA methods (doubling Spearman's correlation and reducing mean-squared error by 75%) across several tasks, models, and training methods.

The surging demand for high-definition video streaming services and large neural network models (e.g., Generative Pre-trained Transformer, GPT) implies a tremendous explosion of Internet traffic. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular contents at devices in closer proximity to users. Correspondingly, in order to maximize caching utilization, it becomes essential to devise an effective popularity prediction method. In that regard, predicting popularity with dynamic graph neural network (DGNN) models achieve remarkable performance. However, DGNN models still suffer from tackling sparse datasets where most users are inactive. Therefore, we propose a reformative temporal graph network, named semantics-enhanced temporal graph network (STGN), which attaches extra semantic information into the user-content bipartite graph and could better leverage implicit relationships behind the superficial topology structure. On top of that, we customize its temporal and structural learning modules to further boost the prediction performance. Specifically, in order to efficiently aggregate the diversified semantics that a content might possess, we design a user-specific attention (UsAttn) mechanism for temporal learning module. Unlike the attention mechanism that only analyzes the influence of genres on content, UsAttn also considers the attraction of semantic information to a specific user. Meanwhile, as for the structural learning, we introduce the concept of positional encoding into our attention-based graph learning and adopt a semantic positional encoding (SPE) function to facilitate the analysis of content-oriented user-association analysis. Finally, extensive simulations verify the superiority of our STGN models and demonstrate the effectiveness in content caching.

Artificial Intelligence is being used to generate videos pretending to be step-by-step tutorials on how to access programs like Photoshop, Premiere Pro, Autodesk 3ds Max, AutoCAD, and others without a license. Instead, the videos are loaded with infostealer malware that scrapes the viewer's sensitive personal data stored on the device. Researchers with CloudSEK measured a month-over-month increase of 200% to 300% since November 2022 of AI-created YouTube videos with links to infostealer malware, including Vidar, RedLine, and Raccoon. Making the video lures more compelling to its targets, the CloudSEK security team added, AI video tools such as Synthesia and D-ID are being used to generate personas intended to exude trustworthiness across multiple languages and social media platforms, supercharging threat actors' ability to deliver infostealer malware. "It is well known that videos featuring humans, especially those certain facial features, appear more familiar and trustworthy," the CloudSEK report explained.

Operational planning and long-term planning for companies are more complex in recent years. Information changes fast, and the decision making is a hard task. Therefore, optimization algorithms (operations research) are used to find optimal solutions for these problems. Professionals in this field are one of the most valued in the market. The classes use examples that are created step by step, so we will create the algorithms together. Besides this Optimization with Python: Solve Operations Research Problems Course is more focused in mathematical approaches, you will also learn how to solve problems using artificial intelligence (AI), genetic algorithm, and particle swarm.

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.

Nowadays, gathering high-quality training data from multiple data sources with privacy preservation is a crucial challenge to training high-performance machine learning models. The potential solutions could break the barriers among isolated data corpus, and consequently enlarge the range of data available for processing. To this end, both academic researchers and industrial vendors are recently strongly motivated to propose two main-stream folders of solutions mainly based on software constructions: 1) Secure Multi-party Learning (MPL for short); and 2) Federated Learning (FL for short). The above two technical folders have their advantages and limitations when we evaluate them according to the following five criteria: security, efficiency, data distribution, the accuracy of trained models, and application scenarios. Motivated to demonstrate the research progress and discuss the insights on the future directions, we thoroughly investigate these protocols and frameworks of both MPL and FL. At first, we define the problem of Training machine learning Models over Multiple data sources with Privacy Preservation (TMMPP for short). Then, we compare the recent studies of TMMPP from the aspects of the technical routes, the number of parties supported, data partitioning, threat model, and machine learning models supported, to show their advantages and limitations. Next, we investigate and evaluate five popular FL platforms. Finally, we discuss the potential directions to resolve the problem of TMMPP in the future.

Machine learning has become one of the most popular fields of study in recent years, and it's not hard to see why. With the rise of big data and the increasing importance of artificial intelligence in various industries, machine learning is a valuable skill set to possess. However, it can be overwhelming to know where to start and how to progress in this field. In this blog, we will provide you with a comprehensive roadmap to machine learning, complete with step-by-step guidance and valuable resources to help you along the way. Before diving into machine learning, it is crucial to understand the fundamentals of data science.

AlphaZero is a game-playing algorithm that uses artificial intelligence and machine learning techniques to learn how to play board games at a superhuman level. We just published a machine learning course on the freeCodeCamp.org Robert Förster created this course. He is a student from Germany who is focused on machine learning. The video course teaches how to code an AlphaZero algorithm from scratch to play Tic Tac Toe and Connect Four.

Welcome to the "Artificial Intelligence with Machine Learning, Deep Learning " course. It's hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon's Alexa and the iPhone's Siri, are all technologies that function based on machine learning algorithms and mathematical models. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, my course on Udemy is here to help you apply machine learning to your work. Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. So data science careers are in high demand. Udemy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies. Whether you're interested in machine learning, data mining, or data analysis, Udemy has a course for you. If you want to learn one of the employer's most requested skills?

This repository contains all of the code, notebooks, images and other materials related to the Zero to Mastery Machine Learning Course on Udemy and zerotomastery.io. If you'd like to see anything in particular, please send me an email: daniel@mrdbourke.com Some students have taken and shared extensive notes on this course, see them below. If you'd like to submit yours, leave a pull request.