Marktechpost Media Inc.’s Post

CMU Researchers Propose a Distributed Data Scoping Method: Revealing the Incompatibility between the Deep Learning Architecture and the Generic Transport PDEs https://lnkd.in/d95nXu6G Practical AI Solutions for Generic Transport Equations Physics-Informed Neural Networks (PINNs) Physics-Informed Neural Networks (PINNs) utilize PDE residuals in training to learn smooth solutions of known nonlinear PDEs, proving valuable in solving inverse problems. Data-Driven Models Data-driven models offer promise in overcoming computation bottlenecks, but their architecture’s compatibility with generic transport PDEs’ local dependency poses challenges to generalization. Data Scoping Technique Researchers from Carnegie Mellon University present a data scoping technique to augment the generalizability of data-driven models forecasting time-dependent physics properties in generic transport issues by disentangling the expressiveness and local dependency of the neural operator. Validation and Results By validating R2, they confirmed the geometric generalizability of the models. The data scoping method significantly enhances accuracy across all validation data, with CNNs improving by 21.7% on average and FNOs by 38.5%. Conclusion and Future Applications In conclusion, this paper reveals the incompatibility between deep learning architecture and generic transport problems, demonstrating how the local-dependent region expands with layer increase. This leads to input complexity and noise, impacting model convergence and generalizability. Researchers proposed a data-scoping method to address this issue efficiently. While this method is currently applied to structured data, the approach shows promise for extension to unstructured data like graphs, potentially benefiting from parallel computation to expedite prediction integration. AI for Business Transformation Identify Automation Opportunities Locate key customer interaction points that can benefit from AI. Define KPIs Ensure your AI endeavors have measurable impacts on business outcomes. Select an AI Solution Choose tools that align with your needs and provide customization. Implement Gradually Start with a pilot, gather data, and expand AI usage judiciously. Spotlight on a Practical AI Solution: AI Sales Bot Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom #artificialintelligence #ai #machinelearning #technology #datascience #python #deeplearning #programming #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #business #engineering #robot #datascientist #art #software #auto...

Reliable results with limited training data - **Technique**: Deep learning is a powerful AI technique with applications in various fields like speech recognition and computer vision.  - **Data Requirement**: Traditional deep learning models require large amounts of annotated data for training, which is time-consuming and labor-intensive. - **Research Focus**: Researchers are exploring ways to train machine-learning models on less data, especially for understanding complex equations in real-world situations. - **Notable Finding**: A study by Cornell University and the University of Cambridge showed that machine learning models can accurately solve partial differential equations (PDEs) with minimal data. - **Methodology**: The researchers used randomized numerical linear algebra and PDE theory to create an efficient algorithm for solving 3D uniformly elliptic PDEs. - **PDEs in Physics**: PDEs are crucial in physics for describing natural phenomena, and the researchers believe their AI models can help understand why AI is effective in physics. - **Data Experiment**: The team tested their AI models with varying amounts of random input data and found high accuracy in the model’s projected solutions. - **Minimal Data Efficiency**: The study found that very little data is needed to train a reliable model in physics, owing to the mathematical properties of these equations. - **Future Implications**: The research underscores the potential of AI in solving complex mathematical and physical problems, making machine learning for physics an exciting field of study.

If you did not already know: G-Rap Finding the best neural network configuration for a given goal can be challenging, especially when it is not possible to assess the output quality of a network automatically. We present G-Rap, an interactive interface based on Visual Analytics principles for comparing outputs of multiple RNNs for the same training data. G-Rap enables an iterative result generation process that allows a user to evaluate the outputs with contextual statistics. … Relational Induction Neural Network (RINN) The automation design of microwave integrated circuits (MWIC) has long been viewed as a fundamental challenge for artificial intelligence owing to its larger solution space and structural complexity than Go. Here, we developed a novel artificial agent, termed Relational Induction Neural Network, that can lead to an automotive design of MWIC and avoid brute-force computing to examine every possible solution, which is a significant breakthrough in the field of electronics. Through the experiments on microwave transmission line circuit, filter circuit and antenna circuit design tasks, strongly competitive results are obtained respectively. Compared with the traditional reinforcement learning method, the learning curve shows that the proposed architecture is able to quickly converge to the pre-designed MWIC model and the convergence rate is up to four orders of magnitude. This is the first study which has been shown that an agent through training or learning to automatically induct the relationship between MWIC’s structures without incorporating any of the additional prior knowledge. Notably, the relationship can be explained in terms of the MWIC theory and electromagnetic field distribution. Our work bridges the divide between artificial intelligence and MWIC and can extend to mechanical wave, mechanics and other related fields. … Agent-Based Computational Economics (ACE) Agent-based computational economics (ACE) is the area of computational economics that studies economic processes, including whole economies, as dynamic systems of interacting agents. As such, it falls in the paradigm of complex adaptive systems. In corresponding agent-based models, the ‘agents’ are ‘computational objects modeled as interacting according to rules’ over space and time, not real people. The rules are formulated to model behavior and social interactions based on incentives and information. Such rules could also be the result of optimization, realized through use of AI methods (such as Q-learning and other reinforcement learning techniques). The theoretical assumption of mathematical optimization by agents in equilibrium is replaced by the less restrictive postulate of agents with bounded rationality adapting to market forces. ACE models apply numerical methods of analysis to computer-based simulations of complex dynamic problems for which more conventional methods, such as theorem formulation,…

Machine learning in 2024 is characterized by diverse algorithms, deep learning dominance, transfer learning, explainability, AutoML for accessibility, reinforcement learning advances, bias mitigation strategies, edge computing integration, robust transferable models, quantum machine learning, exponential data growth, and ethical AI frameworks, collectively shaping a dynamic and evolving landscape for intelligent system development. 1. Diverse Algorithms: - Wide array of machine learning algorithms, including supervised, unsupervised, and reinforcement learning. - Algorithms tailored for specific tasks, such as classification, regression, clustering, and recommendation systems. 2. Deep Learning Dominance: - Continued dominance of deep learning, especially with neural networks, in solving complex problems. - Advancements in architectures like convolutional and recurrent neural networks for image and sequence data. 3. Transfer Learning: - Increased use of transfer learning, allowing pre-trained models to be adapted for new tasks. - Accelerates training on limited datasets and promotes knowledge transfer across domains. 4. Explainability and Interpretability: - Growing emphasis on making machine learning models more interpretable. - Development of techniques like SHAP (SHapley Additive exPlanations) for understanding model decisions. 5. AutoML for Accessibility: - Rise of AutoML tools that automate the machine learning pipeline, making it accessible to non-experts. - Streamlines tasks like feature engineering, model selection, and hyperparameter tuning. 6. Reinforcement Learning Advances: - Ongoing progress in reinforcement learning for training agents to make sequential decisions. - Applications in robotics, gaming, and optimization problems. 7. Bias Mitigation Strategies: - Increased awareness and efforts to address biases in machine learning models. - Implementation of fairness-aware algorithms and ethical considerations in model development. 8. Edge Computing Integration: - Integration of machine learning models into edge devices for local processing. - Reduces latency and enhances privacy in applications like IoT and mobile devices. 9. Robust Transferable Models: - Development of models that perform well across diverse datasets and scenarios. - Generalization capabilities enable broader applicability. 10. Quantum Machine Learning: - Exploration of quantum computing for machine learning tasks. - Potential for exponential speedup in certain computations, impacting fields like optimization and cryptography. 11. Exponential Data Growth: - Coping with the challenges of handling vast amounts of data, necessitating scalable and efficient machine learning solutions. - Integration of big data technologies for large-scale model trainings

🎉Project: Food Vision model using Transfer Learning🍔🍕 🤔Problem Definition: Given a dataset named food101 having 101 different classes of food. This dataset contains 101,000 images. For each class, 250 manually reviewed test images are provided as well as 750 training images. The data is downloaded from the TensorFlow datasets and split into the training and testing set. 🎯Objective: The objective of this project is to design a deep-learning model that can classify different images of food. It should beat the DeepFood a 2016 paper that used a Convolutional Neural Network trained for 2-3 days to achieve 77.4% top-1 accuracy.   📖Resources Required: Google Colab plays a bigger role in this project as it free Jupyter Notebook environment provided by Google where anyone can use free GPUs and TPUs which can solve all these types of problems. In deep learning, GPUs play a crucial role in accelerating the training and inference processes of neural networks. Secondly, TensorFlow documentation helps a lot to understand things much better. 🚀Model Architecture: Made a comparison between ResNet50 and EfficientNetB4 to get a suitable model architecture for my project. And the comparison shows that EfficientNetB4 is much better than ResNet50. So I created my model with EfficientNetB4 (convolutional Neural Network architecture) and it is giving quite good results. I have made use of mixed precision to decrease the time my model takes to train. I have also made use of data augmentation which is a technique used in machine learning and deep learning to artificially increase the size and diversity of a training dataset by applying various transformations to the existing data. 🦾Transfer Learning: Transfer learning is a machine learning technique in which a model that has been pre-trained on a large dataset is used as a starting point for training a new model on a different but related task or domain. First of all, I have made use of Feature Extraction by freezing all the base model layers. But feature extraction model after fitting is not able to produce the desired results. So I went for Fine Tuning by unfreezing the batch normalization layers and fortunately, the accuracy increased and beat the DeepFood Paper(2016) Top-1 accuracy according to the objective specified. 💯Evaluation Metrics: The primary classification metric for this project is accuracy. However, I have also made use of other classification metrics e.g. Confusion Matrix, Classification report, and F1-scores. 🤖tensorBoard.dev: tensorBoard.dev is a free public service that enables anyone to upload their TensorBoard logs and get a permalink that can be shared with everyone in academic papers, blog posts, social media, etc. This can enable better reproducibility and collaboration. #tensorflow #transferlearning #neuralnetworks #learningprogress Below is a video📽️ that explains my Project that how it is performing:-

1. **Introduction:** - The article explores building an AI algorithm for content recommendation using Graph Neural Networks (GNNs) with a focus on Link Regression. - Written by Joseph George Lewis for Towards Data Science. 2. **Graph Data Structures:** - Explains the concept of graphs in the context of GNNs, using a social network example. - Nodes represent individuals, edges represent connections (friendships), and edge weight can signify interaction frequency. 3. **Heterogeneous Graphs:** - Introduces the concept of a heterogeneous graph with different node types, like people and anime. 4. **Graph Neural Networks (GNNs):** - Extension of Neural Networks designed for graph data structures. - Graph SAGE (Graph Sample and Aggregated) is used for message passing, encoding information from neighbors. 5. **Application using PyTorch Geometric:** - Applies GNN concepts to build a recommendation engine using PyTorch Geometric for link regression. - Utilizes an anime dataset from Kaggle, focusing on user ratings. 6. **Feature Engineering:** - Extracts node features based on anime type and genre. - Uses a sentence transformer to generate embeddings for anime titles. 7. **Building a Graph Dataset:** - Constructs a PyTorch graph dataset, making it undirected to enable effective message passing. 8. **Building a Graph Neural Network:** - Describes the GNN model architecture with a Graph Encoder using SageConv layers and an Edge Decoder using Linear layers. 9. **Training the Model:** - Uses RMSE (Root Mean Square Error) for loss during the training process. 10. **Model Evaluation:** - Evaluates the model using unseen test data. - Highlights a drawback of ordinal ratings but uses regression approach with clamping predictions between 0 and 10. 11. **Results:** - Presents the overall RMSE and charts showing model performance across different actual ratings. 12. **Future Recommendations:** - Suggests exploring link prediction, classification, and additional training data for future improvements. 13. **References:** - Provides links to the GitHub repository, dataset source, YouTube series on GNNs, and PyTorch Geometric documentation. In summary, the article guides you through understanding GNNs, applying them to build a content recommendation engine, and evaluating the model's performance. It emphasizes practical implementation using PyTorch Geometric and provides resources for further exploration.

Excited to share insights on image classification in computer vision! With the evolution of Convolution Neural Networks (CNNs), image classification has become easier. CNNs extract features from data, making tasks like classifying Indian medicinal leaves a breeze! Transfer Learning comes to the rescue, addressing major challenges like the need for vast data, overfitting, and computational resources. Leveraging models pre-trained on ImageNet, such as InceptionResNetV2, slashes training time and yields impressive results. In my recent project, I classified 80 types of Indian medicinal leaves using TensorFlow and a P100 GPU on Kaggle. Data preprocessing, validation, and augmentation were crucial steps. Visualizing the dataset gave insights into its diversity. Dividing data into training, validation, and testing sets ensured model generalization. Fine-tuning with multiple learning rates boosted performance, achieving a validation accuracy of ~93.45%—a significant improvement! Training and validation plots showcased the model's learning curve, indicating steady improvement without overfitting. Model evaluation on the test set confirmed its robustness, with an accuracy of ~91%. Inference and visualization of predictions provided real-world application insights. It's essential to understand the nuances of fine-tuning for custom tasks, leveraging GPU resources effectively, and mitigating overfitting. Check out the full article and notebook for a deep dive into the project: [ https://lnkd.in/g2cqMvHa] 👍 If you find this project insightful, don't forget to upvote, like, comment, and share! Let's keep pushing the boundaries of computer vision together. Read this medium article : https://lnkd.in/gARM5TXA based on the same project for clear explanationa and insights #ImageClassification #ComputerVision #TransferLearning #DeepLearning #KaggleProject

The objective of this text is to predict the next………..   Transform   The objective of this text is to predict the next word………   Transform   The objective of this text is to predict the next word using………   Transform   The objective of this text is to predict the next word using AI……….   This is what happens behind the scenes inside Generative AI models. Let's break it down.   First let's understand the basics. What is GPT?   GPT which stands for – Generative Pre-trained Transformer, is a fancy name for a machine-learning algorithm   Generative: The algorithm generates new text.   Pretrained: The model is trained on a large amount of text data   Transformer: This refers to the type of neural network architecture used to understand and predict the next word in a string of text   How does GPT know what would be the next text?   Here’s where the neural network algorithm of transformers comes in   How?   The text we input into GTP is broken down into smaller pieces called tokens. But remember Computers 101 – The only language computers understand is Boolean i.e. 1s and 0s (Switch on and off of a transistor)   Therefore, the parts of text input or tokens are converted into a numerical vector. What is a vector? High school mathematics 101 – a vector is a series of coordinates/numbers representing a point's distance and direction.   P in the GPT – the pre-trained model which had been fed with vast amount of data to understand and predict the next vector (series of numbers) basis the vectors that have been fed into it.   Again think high school mathematics (Linear Algebra)   Let's say there is a linear equation in a 2-D space   y = 2x + C   When you input x, you can predict y   You feed vector x, the model predicts vector y   How does the GPT model work specifically? it predicts the next vector (y) based on the last vector of the input series of vectors (x)   This new vector is added to the previous vectors, and the process repeats, generating one vector at a time.   Now the vectors are re-transformed to a token and back to a string of text     The objective of this text is to predict the next………..   Transform   The objective of this text is to predict the next word………     Of course this is the most simplified explanation of what goes behind the scenes in Gen-AI models. In reality, the vectors are multidimensional, and the model equations are far more complex, often looking like y = f(ax + bx + cx + ...). But starting with these fundamentals helps in understanding the basics.   AI permeating into every aspect of our lives is inevitable and I believe it's fundamental to understand what goes behind the scenes   In an effort to demystifAI AI, I'll be posting a series of such periodic insights to help the larger community get a grasp on AI.     #AI #MachineLearning #TechTalk #AIInsights #GPT #demystifAI

Discover the transformative power of deep learning architectures! Explore CNNs, RNNs, GANs, Transformers, and more in this insightful article, unveiling their diverse applications and revolutionary impact across AI domains. #DeepLearning #AI #Innovation

📃Scientific paper: Physics-Driven Probabilistic Deep Learning for the Inversion of Physical Models With Application to Phenological Parameter Retrieval From Satellite Times Series;Approches guidées par la physique d'apprentissage profond pour l'inversion de modèles physiques avec application à l'inférence de paramètres phénologiques à partir de séries temporelles d'images satellites. Abstract: International audience; Recent Sentinel satellite constellations and deep learning methods offer great possibilities for estimating the states and dynamics of physical parameters on a global scale. Such parameters and their corresponding uncertainties can be retrieved by machine learning methods solving probabilistic inverse problems. Nevertheless, the scarcity of reference data to train supervised methodologies is a well-known constraint for remote sensing applications. To address such limitations, this work presents a new generic physics-guided probabilistic deep learning methodology to invert physical models. The presented methodology proposes a new strategy to combine probabilistic deep learning methods and physical models avoiding simulation-driven machine learning. The inverse problem is addressed through a Bayesian inference framework by proposing a new physically constrained self-supervised representation learning methodology. To show interest in the proposed strategy, the methodology is applied to the retrieval of phenological parameters from normalized difference vegetation index (NDVI) time series. As a result, the probability distributions of the intrinsic phenological model parameters are inferred. The feasibility of the method is evaluated on both simulated and real Sentinel-2 data and compared with different standard algorithms. Promising results show satisfactory accuracy predictions and low inference times for real applications. Continued on ES/IODE ➡️ https://etcse.fr/JZa7 ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.