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Slime Mould Algorithm

The Slime Mould Algorithm, commonly referred to as SMA, is a new and innovative stochastic optimizer with a unique mathematical model inspired by the oscillation mode of slime moulds in nature. This algorithm uses adaptive weights to simulate the process of producing feedback in the form of positive and negative propagation waves, which ultimately forms the optimal path for connecting food sources. SMA has excellent exploratory abilities and high exploitation propensity, making it a powerful too

Slot Attention

Overview of Slot Attention Slot Attention is a component used in deep learning and artificial intelligence that can identify and analyze different objects or features in an image. It helps in producing a set of task-dependent abstract representations, known as slots, that are exchangeable and can bind to any object in the input by specializing through multiple rounds of attention. Slot Attention is designed to work with the output of convolutional neural networks, which are computational model

SlowMo

SlowMo: Distributed Optimization for Faster Learning SlowMo, short for Slow Momentum, is a distributed optimization method designed to help machines learn faster. It does this by periodically synchronizing workers and performing a momentum update using ALLREDUCE after several iterations of an initial optimization algorithm. This allows for better coordination among machines during the learning process, resulting in more accurate and faster results. How SlowMo Works SlowMo is built upon exist

SM3

SM3 is a memory-efficient adaptive optimization method used in machine learning. It helps reduce the memory overhead of the optimizer, allowing for larger models and batch sizes. This new approach has retained the benefits of standard per-parameter adaptivity while reducing the memory requirements, making it a popular choice in modern machine learning. Why traditional methods don't work for large scale applications Standard adaptive gradient-based optimizers, such as AdaGrad and Adam, tune th

Smile Recognition

Smile Recognition: An Overview Smile recognition is an exciting field of research that focuses on the identification and analysis of smiling faces in photos or videos. This technology has numerous applications, including in the fields of security, entertainment, and healthcare. Smile recognition uses cutting-edge artificial intelligence algorithms and deep learning techniques to detect and analyze facial expressions, allowing it to recognize and categorize different types of smiles. In this art

Smish

The World of Smish and Deep Learning Methods Smish is a relatively new activation function that has been introduced to the deep learning community. In the field of machine learning, an activation function is an essential component of deep neural networks. Activation functions help to introduce non-linearity into the network and make it capable of modeling complex and non-linear relationships between input and output data. Researchers from different parts of the world have proposed a wide range

Smooth ReLU

Have you heard of SmeLU? It's a method that's gaining popularity in the world of artificial intelligence and natural language processing. SmeLU stands for Small Embeddings for Language Understanding, and it's a technique that helps machines more accurately understand human language. What is SmeLU? SmeLU is a method for processing human language that uses small, efficient embeddings. An embedding is a way of representing words and phrases as vectors of numbers, which can be processed by a comp

Snapshot Ensembles: Train 1, get M for free

Snapshot Ensembles: A Unique Method to Create Strong Learners The use of deep neural networks has become increasingly popular in recent years owing to their ability to solve complex problems. However, training multiple deep neural networks can be very expensive in terms of time, hardware, and computational resources. This cost often poses a significant barrier to creating deep ensembles, which are groups of multiple neural networks trained on the same dataset. To overcome this obstacle, Huang a

SNet

What is SNet? SNet is a type of neural network architecture used for object detection in deep learning. Specifically, it is the backbone architecture used in the ThunderNet two-stage object detector, which is one of the latest state-of-the-art object detection models. How does SNet work? SNet is a convolutional neural network (CNN) architecture, meaning it is designed to work with image data. In particular, SNet is based on the ShuffleNetV2 architecture, which is known for its small size and

SNIP

SNIP, or Scale Normalization for Image Pyramids, is a technique used for object detection in computer vision. It is a multi-scale training scheme that selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. What is multi-scale training? Multi-scale training (MST) is a technique used for object detection in computer vision that involves observing each image at different resolutions. This is because at a high resolution, large objects a

Social-STGCNN

Social-STGCNN: Understanding Human Trajectories Human contact with the environment is an essential aspect of daily life. Our movements are not just influenced by our bodies, but also by the actions of objects and other individuals around us. With the increasing demand for intelligent transportation systems and the development of autonomous vehicles, predicting human trajectories has become a critical area of research for enhancing safety, efficiency, and comfort in various settings. This articl

Soft Actor-Critic (Autotuned Temperature)

Soft Actor-Critic (Autotuned Temperature): An Overview Reinforcement learning is a type of machine learning that involves training an agent to take actions based on the environment it is in. Soft Actor-Critic (SAC) is a popular reinforcement learning algorithm that has been modified with Autotuned Temperature to improve its performance. SAC is used to find the maximum entropy policy, which means choosing actions that have the highest probability of reaching a particular goal while also account

Soft Actor Critic

What is Soft Actor Critic? Soft Actor Critic (SAC) is a type of algorithm used in deep reinforcement learning (RL). It is based on the maximum entropy RL framework and is an off-policy actor-critic deep RL algorithm that combines off-policy updates with a stable stochastic actor-critic formulation. Unlike other deep RL algorithms, SAC maximizes expected reward while also maximizing entropy, meaning it acts randomly as possible to explore more widely and optimize the policy. The SAC objective ha

Soft-NMS

What is Soft-NMS? Soft-NMS is an algorithm that improves upon the traditional Non-Maximum Suppression (NMS) method used in object detection. NMS is used to sort detection boxes in order of their scores and eliminate those with a significant overlap with another detection box. Soft-NMS decays the scores of overlapping detection boxes gradually, allowing all objects to remain in the detection process. Why is NMS used in Object Detection? In object detection, the goal is to identify objects in

Soft Pooling

SoftPool: Retaining More Information for Better Classification Accuracy What is SoftPool? SoftPool is a new method for pooling in neural networks that sums exponentially weighted activations. This leads to a more refined downsampling process compared to other pooling methods. Downsampling is when the resolution of an activation map is reduced, making it smaller and easier to process. Pooling is an important operation used in deep learning. It takes an input tensor (a multi-dimensional array)

Soft Split and Soft Composition

Soft Split and Soft Composition: A Guide to Understanding The FuseFormer architecture is a recently developed model that has caught the interest of the machine learning community. It has shown exceptional results in the task of image segmentation, which is used in many fields such as medical imaging, robotics, and self-driving cars. One of the unique aspects of the FuseFormer architecture is the use of Soft Split and Soft Composition operations, which we'll be discussing in this article. What

Softmax

Overview of Softmax The Softmax function is commonly used in machine learning for multiclass classification. Its purpose is to transform a previous layer's output into a vector of probabilities. This allows us to determine the likelihood of a particular input belonging to a specific class. How Does Softmax Work? The Softmax function takes an input vector ($x$) and a weighting vector ($w$). It then calculates the probability that a given input belongs to a specific class (j). Softmax works b

Softplus

The Softplus function is a mathematical equation used in machine learning and neural networks as an activation function. It is used to introduce non-linearity in the output of a neuron or neural network. What is an Activation Function? Activation functions are used in neural networks to control the output of a neuron. A neuron is a computational unit that takes inputs, performs a calculation, and produces an output. Activation functions are applied to the output of neurons to introduce non-li

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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 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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