The EfficientUNet++ is an advanced neural network architecture designed for efficient and accurate image segmentation tasks. It combines the decoder architecture inspired on the UNet++ structure with the EfficientNet building blocks to achieve higher performance and lower computational complexity.
UNet++ and EfficientNet building blocks
The UNet++ structure is a popular encoder-decoder architecture used for semantic segmentation tasks. It consists of a series of convolutional and pooling laye
The Elastic Dense Block is an advanced modification of the Dense Block that allows for downsampling and upsampling in parallel branches at each layer. This feature lets the network learn from different scales of input in each layer, making it flexible and adaptable to different data scaling policies.
What is the Dense Block?
The Dense Block is a foundational building block for neural networks. It consists of multiple convolutional layers grouped together, and each layer feeds into the next. U
Understanding Elastic Net: Definition, Explanations, Examples & Code
Elastic Net is a regularization algorithm that is used in supervised learning. It is a powerful and efficient method that linearly combines the L1 and L2 penalties of the Lasso and Ridge methods. This combination allows for both automatic feature selection and regularization, making it particularly useful for high-dimensional datasets with collinear features.
Elastic Net: Introduction
Domains
Learning Methods
Type
Ma
What is an Elastic ResNeXt Block?
An Elastic ResNeXt Block is a modification of the ResNeXt Block that is designed to add downsampling and upsampling functionalities in parallel branches at each layer. It is called “elastic” because it allows for each layer to choose the best scale based on a soft policy. The Elastic ResNeXt Block is designed to improve upon the ResNeXt Block by providing a more flexible and adaptive structure that can better handle diverse data and improve performance on vario
Overview of EWC: Overcoming Catastrophic Forgetting in Neural Networks through Continual Learning
As our world becomes more and more connected through technology, the need for artificial intelligence has increased dramatically. One of the key components of AI is the use of neural networks, which allow machines to learn from experience and improve over time. However, when these networks are constantly being updated with new information, they can suffer from a phenomenon called catastrophic forge
What is ELECTRA? An Overview of the Transformer with a New Pre-training Approach
ELECTRA is a groundbreaking transformer model that uses a unique approach to pre-training. Transformer models are a type of neural network that can process variable-length sequences of data in parallel, making them particularly useful for natural language processing (NLP) tasks like text generation and classification. One big challenge in training such models is obtaining large quantities of high-quality labeled da
The Basics of Electric: A Cloze Model for Text Representation Learning
Electric is an advanced energy-based cloze model for representation learning over text, developed in the field of machine learning. It has a similar structure to the popular BERT, but with subtle differences in its architecture and functioning.
The primary purpose of Electric is to generate vector representations for text, and it uses the generative model methodology to achieve this goal. Specifically, it models $p\_{\text{
Electroencephalogram (EEG) is a medical test used to record the electrical activity of the brain. This is done by attaching small electrodes to the scalp to measure changes in the electrical waves which reflect the activity of the brain nerve cells. The process is painless and non-invasive, and is widely used in both research and clinical settings. EEG is a valuable diagnostic tool that can provide insights into various brain disorders and conditions, including epilepsy, sleep disorders, and cog
An eligibility trace is a tool utilized in reinforcement learning to assist with the challenge of credit assignment. Credit assignment is the task of determining which past actions should receive credit or blame for a current outcome. Eligibility traces help to solve this problem by storing recent actions that contribute to the outcome.
Memory Vector
An eligibility trace is represented as a memory vector $\textbf{z}\_{t}$ that is parallel to the long-term weight vector $\textbf{w}\_{t}$. The
What is ELMo?
ELMo stands for Embeddings from Language Models, which is a special type of word representation that was created to better understand the complex characteristics of word use, such as syntax and semantics. It's an innovative new tool that can help researchers and developers to more accurately model language and to better predict how words will be used in different linguistic contexts.
How Does ELMo Work?
The ELMo algorithm works by using a deep bidirectional language model (biLM
Embedded Dot Product Affinity: An Overview
Embedded Dot Product Affinity is a specific type of self-similarity function. This function quantifies the similarity between two points in a space. The function makes use of a dot product function for this purpose in an embedding space. Embedded Dot Product Affinity is a widely used method in machine learning algorithms, particularly in image processing applications.
What is Affinity and Self-Similarity?
Affinity is a mathematical term that describ
Embedded Gaussian Affinity: A Self-Similarity Function
Embedded Gaussian Affinity is a type of self-similarity function used to measure the similarity between two points. It is often used in computer vision to help machines better understand images and videos.
The Math Behind Embedded Gaussian Affinity
The function uses a Gaussian function in an embedding space. The formula for Embedded Gaussian Affinity is:
f(xi, xj) = eθ(xi)TΦ(xj)
Here, θ(xi) = Wθxi and Π(xj) = Wφxj are two embeddings.
Embedding Dropout is a technique used in machine learning to improve the performance of natural language processing tasks. It involves randomly removing word embeddings during training to prevent overfitting and improve the model's generalization ability.
What is Embedding Dropout?
Embedding Dropout is a regularization technique that applies dropout on the embedding matrix at a word level. In simpler terms, it randomly drops out some of the word embeddings during training, so the model cannot
EMG Gesture Recognition
Electromyographic gesture recognition is a technology that allows us to track and analyze the electrical activity of our muscles when we perform certain movements. This can be done by placing electrodes on the skin that pick up the electrical signals produced by the muscles as they contract and relax.
How does it work?
Electromyography (EMG) is a method of measuring the electrical activity of a muscle. When you move your muscles, your brain sends signals to your muscl
Empathetic Response Generation in Dialogue
Empathy is defined as the ability to understand and share the feelings of others. In recent years, researchers and developers in the field of artificial intelligence have been working towards creating empathetic machines that can respond to human emotions in a more emotionally intelligent manner. Empathetic Response Generation is an important subset of this research that pertains to generating empathetic responses in dialogues between humans and machin
What is EMQAP?
EMQAP, or E-Manual Question Answering Pipeline, is an innovative approach for answering questions related to electronic devices. It is built using a technology called RoBERTa, which has been trained with a massive amount of data to understand natural language processing. EMQAP uses supervised multi-task learning to efficiently identify the section of an e-manual where the answer to a question can be found, and the exact answer span within that section.
How Does EMQAP Work?
EMQ
What is EncAttAgg?
EncAttAgg is a technique that was introduced to tackle two main problems that arise when using machine learning models to analyze text data. This technique was developed by researchers in the field of natural language processing and is designed to improve the efficiency and accuracy of these models.
The Problems EncAttAgg Addresses
The first problem that EncAttAgg addresses is the need to efficiently obtain entity-pair-specific mention representations. Entity pairs are pai
Understanding EDLPS: A Novel Method for Obtaining Semantic Sentence Embeddings
If you're interested in natural language processing, you've probably heard of word embeddings. Word embeddings are a way to represent words as numerical vectors, which can then be used as inputs to machine learning models. These embeddings have been found to be incredibly useful, and there are many different methods for obtaining them. However, obtaining sentence-level embeddings is still a relatively new area of res