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EvoNorms

EvoNorms are a new type of computation layer used in designing neural networks. Neural networks are a type of artificial intelligence that attempts to mimic the way the human brain processes information by using layers of nodes that work together to make predictions or decisions. In order for these networks to work effectively, normalization and activation are critical components that ensure the data is processed correctly. EvoNorms take these concepts to a new level by combining them into a sin

Exact Fusion Model

What is the Exact Fusion Model (EFM)? The Exact Fusion Model, or EFM for short, is a technique used to aggregate a feature pyramid. It is based on a machine learning algorithm called YOLOv3, which assigns one bounding box per ground truth object. The EFM is designed to assemble features from three different scales to better detect objects in an image. How does the EFM work? The EFM uses anchor boxes to assign bounding boxes to objects in an image. Each ground truth bounding box is matched wi

Expectation Maximization

Understanding Expectation Maximization: Definition, Explanations, Examples & Code Expectation Maximization (EM) is a popular statistical technique used for finding maximum likelihood estimates of parameters in probabilistic models. This algorithm is particularly useful in cases where the model depends on unobserved latent variables. EM falls under the clustering category and is commonly used as an unsupervised learning method. Expectation Maximization: Introduction Domains Learning Method

Expected Sarsa

Expected Sarsa is a type of reinforcement learning algorithm that is similar to Q-learning but instead of always choosing the action with the maximum reward, it takes into account the likelihood of each action under the current policy. This helps to eliminate the variance caused by randomly selecting actions. What is Reinforcement Learning? Reinforcement learning is a type of machine learning that involves an agent interacting with an environment to learn the optimal actions to take in order

Experience Replay

Experience Replay: What is it? Experience Replay is a technique used in reinforcement learning. In reinforcement learning, an agent learns to make decisions in an environment and receives feedback in the form of rewards. By giving positive feedback for good decisions and negative feedback for bad ones, the agent learns to make better decisions in the future. Experience Replay is a way to improve this learning process by storing the agent's experiences and using them to improve its performance.

Explanation vs Attention: A Two-Player Game to Obtain Attention for VQA

Explanation vs Attention: Improving Visual Question Answering (VQA) Visual Question Answering (VQA) is a challenging task that requires a machine to answer questions based on images. One of the important factors in VQA is attention, which determines which parts of an image should be focused on to answer a given question. However, supervising attention can be difficult. In this paper, the authors propose using visual explanations, obtained through class activation mappings, as a means of supervi

Exponential Decay

Exponential Decay: Understanding the Learning Rate Schedule In the field of machine learning, one of the most important factors that determines the accuracy and efficiency of an algorithm is the learning rate. The learning rate controls how fast the model learns and adjusts its weight values as it processes data. However, using a fixed learning rate can lead to suboptimal performance, as the algorithm may overshoot or undershoot the optimal solution. This is where a learning rate schedule comes

Exponential Linear Squashing Activation

The Exponential Linear Squashing Activation Function, or ELiSH, is a type of activation function commonly used in neural networks. It is similar to the Swish function, which combines ELU and Sigmoid functions, but has unique properties that make it useful for various machine learning tasks. What is an Activation Function? Before we dive into ELiSH, let's first review what an activation function is and why it's important for neural networks. In a neural network, each neuron has an activation f

Exponential Linear Unit

In machine learning, an activation function is applied to the output of each neuron in a neural network. The exponential linear unit (ELU) is an activation function that is commonly used in neural networks. Mean Unit Activations ELUs have negative values which allows them to push mean unit activations closer to zero. This is similar to batch normalization, but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natu

Extended Transformer Construction

Extended Transformer Construction, also known as ETC, is an enhanced version of the Transformer architecture that utilizes a new attention mechanism to extend the original in two main ways: (1) it allows for a larger input length, up to several thousands, and (2) it can process structured inputs as well as sequential ones. What is ETC? The Transformer architecture is a machine learning model used for natural language processing tasks such as translation and summarization. The original Transfo

eXtreme Gradient Boosting

Understanding eXtreme Gradient Boosting: Definition, Explanations, Examples & Code XGBoost, short for eXtreme Gradient Boosting, is a popular machine learning algorithm that employs the gradient boosting framework. It leverages decision trees as base learners and combines them to produce a final, more robust prediction model. Renowned for its speed and performance, XGBoost is primarily used for supervised learning tasks such as regression and classification. It is classified as an Ensemble algo

eXtreme-Video-Frame-Interpolation

Video Frame Interpolation (VFI) is a technique used to increase the frames per second (fps) of a video by using software to create new frames between existing frames. This can result in smoother, better quality videos. One type of VFI is eXtreme-Video-Frame-Interpolation, which is designed to handle videos with extreme motion, like those in the X4K1000FPS dataset. What is the X4K1000FPS Dataset? The X4K1000FPS dataset is a collection of 4K videos that have a frame rate of 1000 fps. This makes

Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions

The EESP Unit, or Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions, is an innovative image model block developed for edge devices as part of the ESPNetv2 CNN architecture. It uses a reduce-split-transform-merge strategy to process input feature maps and learn representations in parallel. What is the EESP Unit? The EESP Unit is a unique element of the ESPNetv2 architecture designed specifically for edge devices, which have limited processing power and memory com

ExtremeNet

Overview of ExtremeNet ExtremeNet is an advanced object detection framework that detects the four extreme points (top-most, left-most, bottom-most, right-most) of an object. This framework uses a keypoint estimation approach to locate extreme points by predicting multi-peak heatmaps for each object category. Additionally, ExtremeNet uses one heatmap per category to predict the object center, by calculating the average of two bounding box edges in both the x and y dimensions. How ExtremeNet Wo

Face Alignment

What is Face Alignment? In today's digital age, photos have become an essential part of our daily lives. Be it social media, work meetings, or personal memories, we click photos of ourselves and others. But, have you ever noticed how some people look great in every photo they take, while others struggle to get a good shot? This can be due to various reasons, including camera angles, lighting, and most importantly, face alignment. Why is Face Alignment Important? Face alignment is crucial for

Face Anti-Spoofing

What is Facial Anti-Spoofing? Facial anti-spoofing is a technology used to prevent false facial verification. It uses different methods to ensure that the person being verified is actually the authorized person and not a photo, video or mask being used to trick the system. The goal of facial anti-spoofing is to enhance the security of facial recognition systems so that they are not easily fooled by malicious individuals. Types of Face Spoofing Attacks There are different types of face spoofi

Face Detection

Face detection is a computer technology that helps to locate and identify human faces in images or videos automatically. It is a vital system that is used to support many types of applications such as facial recognition, tracking, and analysis. Due to its importance in many industries, face detection is a rapidly growing research field that offers immense potential to revolutionize many areas of human life. How Does Face Detection Work? Face detection works by utilizing machine learning algor

Face Generation

Face generation refers to the process of generating or interpolating new faces from an existing dataset. This cutting-edge technology is a part of the larger field of image generation. What is Face Generation? Face generation utilizes artificial intelligence (AI) techniques to produce life-like images of individuals who do not actually exist. The images are created using an existing dataset that contains various facial features. The AI algorithm studies the dataset and learns to create new fa

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Dependency Parsers Deraining Models Detection Assignment Rules Dialog Adaptation Dialog System Evaluation Dialogue State Trackers Dimensionality Reduction Discriminators Distillation Distributed Communication Distributed Methods Distributed Reinforcement Learning Distribution Approximation Distributions Document Embeddings Document Summary Evaluation Document Understanding Models Domain Adaptation Downsampling E-signing Efficient Planning Eligibility Traces Ensembling Entity Recognition Models Entity Retrieval Models Environment Design Methods Exaggeration Detection Models Expense Trackers Explainable CNNs Exploration Strategies Face Privacy Face Recognition Models Face Restoration Models Face-to-Face Translation Factorization Machines Feature Extractors Feature Matching Feature Pyramid Blocks Feature Upsampling Feedforward Networks Few-Shot Image-to-Image Translation Fine-Tuning Font Generation Models Fourier-related Transforms Free AI Tools Free Subscription Trackers Gated Linear 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Detection Models Mean Shift Clustering Medical Medical Image Models Medical waveform analysis Mesh-Based Simulation Models Meshing Meta-Learning Algorithms Methodology Miscellaneous Miscellaneous Components Mixture-of-Experts Model Compression Model Parallel Methods Momentum Rules Monocular Depth Estimation Models Motion Control Motion Prediction Models Multi-Modal Methods Multi-Object Tracking Models Multi-Scale Training Music Music source separation Music Transcription Natural Language Processing Natural Language Processing Courses Negative Sampling Network Shrinking Neural Architecture Search Neural Networks Neural Networks Courses Neural Search No Code AI No Code AI App Builders No Code Courses No Code Tools Non-Parametric Classification Non-Parametric Regression Normalization Numpy Courses Object Detection Models Object Detection Modules OCR Models Off-Policy TD Control Offline Reinforcement Learning Methods On-Policy TD Control One-Stage Object Detection Models Open-Domain Chatbots Optimization Oriented Object Detection Models Out-of-Distribution Example Detection Output Functions Output Heads Pandas Courses Parameter Norm Penalties Parameter Server Methods Parameter Sharing Paraphrase Generation Models Passage Re-Ranking Models Path Planning Person Search Models Phase Reconstruction Point Cloud Augmentation Point Cloud Models Point Cloud Representations Policy Evaluation Policy Gradient Methods Pooling Operations Portrait Matting Models Pose Estimation Blocks Pose Estimation Models Position Embeddings Position Recovery Models Prioritized Sampling Prompt Engineering Proposal Filtering Pruning Python Courses Q-Learning Networks Quantum Methods Question Answering Models Randomized Value Functions Reading Comprehension Models Reading Order Detection Models Reasoning Recommendation Systems Recurrent Neural Networks Region Proposal Regularization Reinforcement Learning Reinforcement Learning Frameworks Relation Extraction Models Rendezvous Replay Memory Replicated Data Parallel Representation Learning Reversible Image Conversion Models RGB-D Saliency Detection Models RL Transformers Robotic Manipulation Models Robots Robust Training Robustness Methods RoI Feature Extractors Rule-based systems Rule Learners Sample Re-Weighting Scene Text Models scikit-learn Scikit-learn Courses Self-Supervised Learning Self-Training Methods Semantic Segmentation Models Semantic Segmentation Modules Semi-supervised Learning Semi-Supervised Learning Methods Sentence Embeddings Sequence Decoding Methods Sequence Editing Models Sequence To Sequence Models Sequential Blocks Sharded Data Parallel Methods Skip Connection Blocks Skip Connections SLAM Methods Span Representations Sparsetral Sparsity Speaker Diarization Speech Speech Embeddings Speech enhancement Speech Recognition Speech Separation Models Speech Synthesis Blocks Spreadsheet Formula Prediction Models State Similarity Metrics Static Word Embeddings Stereo Depth Estimation Models Stochastic 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Inpainting Models Video Instance Segmentation Models Video Interpolation Models Video Model Blocks Video Object Segmentation Models Video Panoptic Segmentation Models Video Recognition Models Video Super-Resolution Models Video-Text Retrieval Models Vision and Language Pre-Trained Models Vision Transformers VQA Models Webpage Object Detection Pipeline Website Monitoring Whitening Word Embeddings Working Memory Models