Neural quantum state tomography, improvements and applications
Advancements and potential applications of neural quantum state tomography, aiming to reduce the exponential classical memory required for expressing quantum states. It discusses the benefits of using machine learning techniques to process and analyze quantum data, such as cleaning up states, manipu
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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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Computational Physics (Lecture 18)
Neural networks explained with the example of feedforward vs. recurrent networks. Feedforward networks propagate data, while recurrent models allow loops for cascade effects. Recurrent networks are less influential but closer to the brain's function. Introduction to handwritten digit classification
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Graph Machine Learning Overview: Traditional ML to Graph Neural Networks
Explore the evolution of Machine Learning in Graphs, from traditional ML tasks to advanced Graph Neural Networks (GNNs). Discover key concepts like feature engineering, tools like PyG, and types of ML tasks in graphs. Uncover insights into node-level, graph-level, and community-level predictions, an
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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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Understanding Neural Networks: Models and Approaches in AI
Neural networks play a crucial role in AI with rule-based and machine learning approaches. Rule-based learning involves feeding data and rules to the model for predictions, while machine learning allows the machine to design algorithms based on input data and answers. Common AI models include Regres
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Block-grained Scaling of Deep Neural Networks for Mobile Vision
This presentation explores the challenges of optimizing Deep Neural Networks (DNN) for mobile vision systems due to their large size and high energy consumption. The LegoDNN framework introduces a block-grained scaling approach to reduce memory access energy consumption by compressing DNNs. The agen
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Understanding the Paradox of Plankton and Biodiversity Correlates
The paradox of plankton challenges the theory that the number of species should match available resources, as seen in diverse plankton ecosystems. Explanations include resource partitioning, temporal and spatial heterogeneity, predation, adaptation to disturbance, and evolutionary processes. Biodive
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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 Set Transformer: A Framework for Attention-Based Permutation-Invariant Neural Networks
Explore the Set Transformer framework that introduces advanced methods for handling set-input problems and achieving permutation invariance in neural networks. The framework utilizes self-attention mechanisms and pooling architectures to encode features and transform sets efficiently, offering insig
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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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Recent Advances in RNN and CNN Models: CS886 Lecture Highlights
Explore the fundamentals of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in the context of downstream applications. Delve into LSTM, GRU, and RNN variants, alongside CNN architectures like ConvNext, ResNet, and more. Understand the mathematical formulations of RNNs and c
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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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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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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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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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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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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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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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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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Convolutional Neural Networks for Sentence Classification: A Deep Learning Approach
Deep learning models, originally designed for computer vision, have shown remarkable success in various Natural Language Processing (NLP) tasks. This paper presents a simple Convolutional Neural Network (CNN) architecture for sentence classification, utilizing word vectors from an unsupervised neura
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Enhancing Sea Surface Temperature Data Using Hadoop-Based Neural Networks
Large-scale sea surface temperature (SST) data are crucial for analyzing vast amounts of information, but face challenges such as data scale, system load, and noise. A Hadoop-based Backpropagation Neural Network framework processes SST data efficiently using a Backpropagation algorithm. The system p
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Advanced Applications of Convolution Modelling in GLM and SPM MEEG Course 2019
Addressing difficulties in experimental design such as baseline correction, temporally overlapping neural responses, and systematic differences in response timings using a convolution GLM, similar to first-level fMRI analysis. The course focuses on the stop-signal task, EEG correlates of stopping a
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Understanding Advanced Classifiers and Neural Networks
This content explores the concept of advanced classifiers like Neural Networks which compose complex relationships through combining perceptrons. It delves into the workings of the classic perceptron and how modern neural networks use more complex decision functions. The visuals provided offer a cle
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Understanding Neural Processing and the Endocrine System
Explore the intricate communication network of the nervous system, from nerve cells transmitting messages to the role of dendrites and axons in neural transmission. Learn about the importance of insulation in neuron communication, the speed of neural impulses, and the processes involved in triggerin
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Understanding Vocal Fold Paralysis and Neural Dysfunction After Thyroid and Parathyroid Surgery
This consensus statement by the American Head and Neck Society Endocrine Surgery Section highlights the importance of recognizing immediate vocal fold paralysis (VFP) and partial neural dysfunction (PND) following thyroid and parathyroid surgery. The report emphasizes the need for early identificati
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Advanced Applications of GLM and SPM in M/EEG Course 2018
This course delves into utilizing Convolution GLM to address challenges such as baseline correction, overlapping neural responses, and systematic response timing differences in EEG experiments. It focuses on the stop-signal task, EEG correlates of movement stopping, and MEG data analysis. The course
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