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Neural Networks News
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Exploring Classical and Learned Local Search Heuristics for Combinatorial Optimization
- This paper explores classical and learned local search heuristics for combinatorial optimization (CO) problems.
- Classical heuristics are discussed, including the Structure-to-Vector GNN trained with Deep Q-Networks (DQN) and the embedding Graph Convolution Network (GCN) with reinforcement learning (RL).
- Generalized learned heuristics, such as ECO-DQN and ECORD, are introduced for the Max-Cut problem.
- Learning heuristics for SAT (Boolean Satisfiability) problems, including the integration of GNNs with SAT solvers, is also explored.
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Hackernoon
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Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?
- Combining neural networks with local search heuristics has shown promising outcomes in combinatorial optimization.
- Three limitations in the empirical evaluation of these integration attempts have been identified.
- A simple learned heuristic based on Tabu Search surpasses state-of-the-art learned heuristics in performance and generalizability.
- These findings challenge prevailing assumptions and open up avenues for future research in combinatorial optimization.
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Medium
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Hybridized neural network and decision tree based classifier for prognostic decision making in…
- The Radial Basis Function (RBF) network is chosen for its advantages, such as quicker convergence, reduced extrapolation errors, and enhanced reliability compared to conventional multilayer perceptrons.
- A novel approach is adopted by considering the derivative of the activation function for weight updates and error computation, facilitating improved performance.
- The min-max normalization technique is utilized for normalization, converting the minimum and maximum values to a scaled value within the range (0, 1).
- Decision trees (DT) are recognized for their efficiency in managing large datasets, identifying key features, and offering interpretable outcomes.
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Residual Networks a Convolution Network Derivative: Deep Learning Tutorial
- The key idea behind ResNets is the introduction of skip connections, allowing gradients to bypass layers and facilitate the training of deep networks.
- ResNets consist of multiple residual blocks, with each block comprising identity and residual mappings.
- Residual Networks differentiate from traditional CNNs through the use of skip connections, overcoming limitations in training deep neural networks.
- ResNets have become a fundamental building block in deep learning architectures, particularly in image processing and computer vision.
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Medium
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Exploring PyTorch: From Fundamentals to Neural Network Construction
- PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab (FAIR). It provides a flexible and intuitive platform for building and training neural networks.
- Tensors are the foundational components in PyTorch and serve as specialized data structures similar to arrays and matrices. They are optimized for automatic differentiation and can run on GPUs.
- PyTorch simplifies data management with DataLoader and Dataset primitives, which facilitate handling pre-loaded and custom datasets.
- Constructing neural networks in PyTorch involves using torch.nn's building blocks, such as linear layers and activation functions like ReLU.
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Medium
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What is Backpropagation and How Does It Work?
- A neural network is a computational model that consists of many interconnected units called neurons.
- The network learns by adjusting its weights, which determine how each neuron combines its inputs.
- A loss function measures how well the network performs on a given task.
- Backpropagation is an algorithm used to update the weights of a neural network based on the calculated loss.
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Medium
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Early Stopping
- Early Stopping is a regularization technique used during model fitting to prevent overfitting.
- It evaluates progress at the end of each epoch and stops training when the model is no longer improving.
- The author shares a personal experience of wasting time on a non-relevant task and proposes a personal Early Stopping strategy.
- The strategy involves setting a time limit and periodically assessing progress to avoid pursuing unproductive paths.
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Medium
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Learning Symbolic Physics with Graph Networks
- Researchers are using graph networks and symbolic regression to learn symbolic physics.
- Graph networks have proven effective in capturing the dynamics of physical systems.
- Symbolic regression with tools like 'eureqa' fits algebraic formulas to trained models.
- GNNs with lower-dimensional message-passing spaces exhibit better generalization capabilities for predicting dynamics in systems affected by an inverse-square law.
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Medium
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Convolutional Neural Networks: Deep Dive
- Convolutional Neural Networks (CNNs) are algorithms designed for image processing and classification.
- CNNs process data in grids and are compatible with image encoding, making them ideal for image and video classification.
- CNNs consist of layers such as input layer, hidden layers, and output layer, where the learning and prediction take place.
- Convolutional and pooling layers in CNNs extract key features from images, reduce image size, and improve classification accuracy.
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Medium
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Conquering the Steep Ascent: Gradient Clipping as the Linchpin of Stable Neural Network Training
- Gradient clipping is a strategic intervention to prevent the exploding gradient problem during neural network training.
- It curtails excessively large gradients, ensuring stable training and convergence.
- Gradient clipping is particularly useful for training deep neural networks and recurrent neural networks.
- The implementation of gradient clipping in a synthetic dataset demonstrated stable convergence and accurate predictions.
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Medium
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The AI Odyssey: Journey through the Top Ten AI Websites
- AI Academy: A comprehensive platform dedicated to educating users about artificial intelligence.
- AI Assist: A virtual assistant powered by cutting-edge AI algorithms, streamlining workflows and enhancing productivity.
- AI Art Gallery: An immersive platform showcasing artworks created with the assistance of artificial intelligence, challenging traditional notions of artistry and innovation.
- AI Health Hub: A revolutionary platform revolutionizing healthcare with AI-driven solutions, improving patient care and medical outcomes.
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Medium
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Basalt - Machine Learning in Pure Mojo
- Basalt is a machine learning library built from the combination of the Voodoo and Dainemo projects.
- Voodoo aimed to create the Pytorch/Tensorflow equivalent for Mojo, while Dainemo took a different approach with static graph compilation.
- Basalt achieves speeds comparable to Pytorch and Tensorflow, with benchmarks consistently beating Pytorch for smaller parameter models.
- While Basalt is still in its infancy, there is ongoing optimization and refinement to further improve its performance.
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Medium
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Convolutional Neural Networks: Deep Learning. (What are convolutions)
- Convolutional neural networks (CNN) are used to recognize and understand images.
- CNN uses convolutions, where a small filter scans over an image to find patterns.
- The process involves convolution, pooling, and fully connected layers.
- Advantages of CNN include image recognition and feature extraction.
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Medium
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Huawei & Peking U’s DiJiang: A Transformer Achieving LLaMA2–7B Performance at 1/50th the Training…
- Huawei Noah’s Ark Lab and Peking University introduce DiJiang, a Frequency Domain Kernelization approach.
- DiJiang achieves performance akin to LLaMA2–7B at 1/50th of the training cost.
- The approach maps queries and keys to the frequency domain using Discrete Cosine Transform (DCT), enabling linear complexity computation.
- DiJiang contributes to crafting efficient and scalable Transformer models for natural language processing tasks.
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Medium
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How to use Checkpoint Strategies with Keras and TensorFlow: Ensuring Training Resilience
- Model checkpointing is a strategic process in deep learning workflows, designed to save snapshots of your model’s state at specified intervals.
- Both Keras and TensorFlow offer built-in mechanisms to automate the checkpointing process, with the ModelCheckpoint callback in Keras providing a flexible approach.
- Implementing model checkpointing involves preparing the dataset, defining the model, configuring the ModelCheckpoint callback, and initiating the training process.
- Early stopping is another powerful technique that can be used alongside model checkpointing to automatically stop training when a monitored metric stops improving.
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