L7: Neural Network 101 — DNN and GNN
Basics of neural networks including DNN and GNN Cong, their optimization opportunities, and their applications in machine learning. Presented by Callie Hao, Assistant Professor at Georgia Institute of Technology.
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Neural Network and Variational Autoencoders
The concepts of neural networks and variational autoencoders. Understand decision-making, knowledge representation, simplification using equations, activation functions, and the limitations of a single perceptron.
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Introduction to Deep Learning: Neural Networks and Multilayer Perceptrons
Explore the fundamentals of neural networks, including artificial neurons and activation functions, in the context of deep learning. Learn about multilayer perceptrons and their role in forming decision regions for classification tasks. Understand forward propagation and backpropagation as essential
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Rainfall-Runoff Modelling Using Artificial Neural Network: A Case Study of Purna Sub-catchment, India
Rainfall-runoff modeling is crucial in understanding the relationship between rainfall and runoff. This study focuses on developing a rainfall-runoff model for the Upper Tapi basin in India using Artificial Neural Networks (ANNs). ANNs mimic the human brain's capabilities and have been widely used i
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Apache MINA: High-performance Network Applications Framework
Apache MINA is a robust framework for building high-performance network applications. With features like non-blocking I/O, event-driven architecture, and enhanced scalability, MINA provides a reliable platform for developing multipurpose infrastructure and networked applications. Its strengths lie i
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Modeling and Generation of Realistic Network Activity Using Non-Negative Matrix Factorization
The GHOST project focuses on the challenges of modeling, analyzing, and generating patterns of network activity. By utilizing Non-Negative Matrix Factorization (NMF), realistic network activity patterns can be created and injected into live wireless networks. Understanding and predicting user behavi
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Automated Anomaly Detection Tool for Network Performance Optimization
Anomaly Detection Tool (ADT) aims to automate the detection of network degradation in a mobile communications network, reducing the time and effort required significantly. By utilizing statistical and machine learning models, ADT can generate anomaly reports efficiently across a large circle network
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Understanding Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) are powerful tools for sequential data learning, mimicking the persistent nature of human thoughts. These neural networks can be applied to various real-life applications such as time-series data prediction, text sequence processing,
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Understanding Mechanistic Interpretability in Neural Networks
Delve into the realm of mechanistic interpretability in neural networks, exploring how models can learn human-comprehensible algorithms and the importance of deciphering internal features and circuits to predict and align model behavior. Discover the goal of reverse-engineering neural networks akin
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Localised Adaptive Spatial-Temporal Graph Neural Network
This paper introduces the Localised Adaptive Spatial-Temporal Graph Neural Network model, focusing on the importance of spatial-temporal data modeling in graph structures. The challenges of balancing spatial and temporal dependencies for accurate inference are addressed, along with the use of distri
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Graph Neural Networks
Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d
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Understanding Keras Functional API for Neural Networks
Explore the Keras Functional API for building complex neural network models that go beyond sequential structures. Learn how to create computational graphs, handle non-sequential models, and understand the directed graph of computations involved in deep learning. Discover the flexibility and power of
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Ensuring Reliability of Deep Neural Network Architectures
This study focuses on assuring the reliability of deep neural network architectures against numerical defects, highlighting the importance of addressing issues that lead to unreliable outputs such as NaN or inf. The research emphasizes the widespread and disastrous consequences of numerical defects
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Exploring a Cutting-Edge Convolutional Neural Network for Speech Emotion Recognition
Human speech is a rich source of emotional indicators, making Speech Emotion Recognition (SER) vital for intelligent systems to understand emotions. SER involves extracting emotional states from speech and categorizing them. This process includes feature extraction and classification, utilizing tech
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Hands-on Machine Learning with Python: Implement Neural Network Solutions
Explore machine learning concepts from Python basics to advanced neural network implementations using Scikit-learn and PyTorch. This comprehensive guide provides step-by-step explanations, code examples, and practical insights for beginners in the field. Covering topics such as data visualization, N
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Revolutionizing Network Management with Intent-Based Networking
Explore the concept and benefits of Intent-Based Networking (IBN) in simplifying network configuration and enhancing efficiency. Learn how IBN automates network operations, aligns with business objectives, improves security, and ensures scalability and reliability. Discover the potential of IBN tool
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Understanding Artificial Neural Networks From Scratch
Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function
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Understanding Back-Propagation Algorithm in Neural Networks
Artificial Neural Networks aim to mimic brain processing. Back-propagation is a key method to train these networks, optimizing weights to minimize loss. Multi-layer networks enable learning complex patterns by creating internal representations. Historical background traces the development from early
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Network Compression Techniques: Overview and Practical Issues
Various network compression techniques such as network pruning, knowledge distillation, and parameter quantization are discussed in this content. The importance of pruning redundant weights and neurons in over-parameterized networks is highlighted. Practical issues like weight pruning and neuron pru
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A Deep Dive into Neural Network Units and Language Models
Explore the fundamentals of neural network units in language models, discussing computation, weights, biases, and activations. Understand the essence of weighted sums in neural networks and the application of non-linear activation functions like sigmoid, tanh, and ReLU. Dive into the heart of neural
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Network Slicing with OAI 5G CN Workshop Overview
Overview of Network Slicing with OAI 5G CN workshop focusing on the crucial role of network slicing in realizing the service-oriented 5G vision. This workshop covers topics like multiple logical networks creation on shared infrastructure, different types of network slices, preparation and instantiat
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Assistive Speech System for Individuals with Speech Impediments Using Neural Networks
Individuals with speech impediments face challenges with speech-to-text software, and this paper introduces a system leveraging Artificial Neural Networks to assist. The technology showcases state-of-the-art performance in various applications, including speech recognition. The system utilizes featu
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Advancing Physics-Informed Machine Learning for PDE Solving
Explore the need for numerical methods in solving partial differential equations (PDEs), traditional techniques, neural networks' functioning, and the comparison between standard neural networks and physics-informed neural networks (PINN). Learn about the advantages, disadvantages of PINN, and ongoi
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Understanding Perceptron Learning Algorithm in Neural Networks
Perceptron is the first neural network learning model introduced in the 1960s by Frank Rosenblatt. It follows a simple and limited (single-layer model) approach but shares basic concepts with multi-layer models. Perceptron is still used in some current applications, especially in large business prob
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Understanding the Need for Neural Network Accelerators in Modern Systems
Neural network accelerators are essential due to the computational demands of models like VGG-16, emphasizing the significance of convolution and fully connected layers. Spatial mapping of compute units highlights peak throughput, with memory access often becoming the bottleneck. Addressing over 300
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Understanding Hopfield Nets in Neural Networks
Hopfield Nets, pioneered by John Hopfield, are a type of neural network with symmetric connections and a global energy function. These networks are composed of binary threshold units with recurrent connections, making them settle into stable states based on an energy minimization process. The energy
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Exploring Biological Neural Network Models
Understanding the intricacies of biological neural networks involves modeling neurons and synapses, from the passive membrane to advanced integrate-and-fire models. The quality of these models is crucial in studying the behavior of neural networks.
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Exploring Neural Quantum States and Symmetries in Quantum Mechanics
This article delves into the intricacies of anti-symmetrized neural quantum states and the application of neural networks in solving for the ground-state wave function of atomic nuclei. It discusses the setup using the Rayleigh-Ritz variational principle, neural quantum states (NQSs), variational pa
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Automated Melanoma Detection Using Convolutional Neural Network
Melanoma, a type of skin cancer, can be life-threatening if not diagnosed early. This study presented at the IEEE EMBC conference focuses on using a convolutional neural network for automated detection of melanoma lesions in clinical images. The importance of early detection is highlighted, as exper
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Understanding Snort: An Open-Source Network Intrusion Detection System
Snort is an open-source Network Intrusion Detection System (NIDS) developed by Cisco, capable of analyzing network packets to identify suspicious activities. It can function as a packet sniffer, packet logger, or a full-fledged intrusion prevention system. By monitoring and matching network activity
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Learning a Joint Model of Images and Captions with Neural Networks
Modeling the joint density of images and captions using neural networks involves training separate models for images and word-count vectors, then connecting them with a top layer for joint training. Deep Boltzmann Machines are utilized for further joint training to enhance each modality's layers. Th
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Understanding Spiking Neurons and Spiking Neural Networks
Spiking neural networks (SNNs) are a new approach modeled after the brain's operations, aiming for low-power neurons, billions of connections, and high accuracy training algorithms. Spiking neurons have unique features and are more energy-efficient than traditional artificial neural networks. Explor
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Role of Presynaptic Inhibition in Stabilizing Neural Networks
Presynaptic inhibition plays a crucial role in stabilizing neural networks by rapidly counteracting recurrent excitation in the face of plasticity. This mechanism prevents runaway excitation and maintains network stability, as demonstrated in computational models by Laura Bella Naumann and Henning S
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Understanding Word2Vec: Creating Dense Vectors for Neural Networks
Word2Vec is a technique used to create dense vectors to represent words in neural networks. By distinguishing target and context words, the network input and output layers are defined. Through training, the neural network predicts target words and minimizes loss. The hidden layer's neuron count dete
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Strategies for Improving Generalization in Neural Networks
Overfitting in neural networks occurs due to the model fitting both real patterns and sampling errors in the training data. The article discusses ways to prevent overfitting, such as using different models, adjusting model capacity, and controlling neural network capacity through various methods lik
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Introduction to Neural Networks in IBM SPSS Modeler 14.2
This presentation provides an introduction to neural networks in IBM SPSS Modeler 14.2. It covers the concepts of directed data mining using neural networks, the structure of neural networks, terms associated with neural networks, and the process of inputs and outputs in neural network models. The d
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Detecting Image Steganography Using Neural Networks
This project focuses on utilizing neural networks to detect image steganography, specifically targeting the F5 algorithm. The team aims to develop a model that is capable of detecting and cleaning hidden messages in images without relying on hand-extracted features. They use a dataset from Kaggle co
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Understanding Convolutional Neural Networks: Architectural Characterizations for Accuracy Inference
This presentation by Duc Hoang from Rhodes College explores inferring the accuracy of Convolutional Neural Networks (CNNs) based on their architectural characterizations. The talk covers the MINERvA experiment, deep learning concepts including CNNs, and the significance of predicting CNN accuracy be
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Incremental Neural Coreference Resolution: Constant Memory Approach
This research delves into Incremental Neural Coreference Resolution using a Limited-memory algorithm for efficient processing while addressing memory constraints. It explores techniques such as neural components and explicit entity representations, making advancements in resolving coreference in lon
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Estimation of Ionospheric Critical Plasma Frequencies Using GNSS Measurements
This research focuses on estimating the critical plasma frequency of the ionosphere, specifically the F2 layer (f0F2), using GNSS measurements. The study reviews past work on ionospheric modeling, discusses neural network training inputs, and presents a single station neural network model (NNT2F2).
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