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MoGA-B

MoGA-B is a type of neural network that has been optimized for mobile devices. Specifically, it is designed to have low latency, meaning that it can quickly process data without causing delays. This neural network was discovered through a method called neural architecture search, which involves using computer algorithms to explore different variations of neural network architectures and select the best one for a given task. What is a convolutional neural network? Before we dive into MoGA-B sp

MoGA-C

MoGA-C is a new type of convolutional neural network that has been optimized for mobile devices. It was discovered through a process called Neural Architecture Search, which is a method of using artificial intelligence to find the best structure for a neural network. In this case, MoGA-C was designed to be fast and efficient, and it was built using a basic building block known as inverted residual blocks from MobileNetV2. The network also includes experimental squeeze-and-excitation layers. Wh

Mogrifier LSTM

The Mogrifier LSTM is an extension of the LSTM (Long Short-Term Memory) algorithm used in machine learning. The Mogrifier LSTM adds a gating mechanism to the input of the LSTM, where the gating is conditioned on the output of the previous step. Then, the gated input is used to gate the output of the previous step. After a few rounds of this mutual gating, the last updated inputs are fed to the LSTM. This process is called "modulating," and it allows the Mogrifier LSTM to learn patterns in the da

Momentum Contrast

If you have ever heard the term "MoCo", you might be wondering what it means. MoCo stands for Momentum Contrast, which is a type of self-supervised learning algorithm. But what does that even mean? Let's break it down. What is MoCo? MoCo is a method for training computer programs to recognize and classify images or patches of data. Specifically, it uses a type of machine learning called unsupervised learning. This means that the program does not need explicit labels or instructions in order t

Momentum

Momentum offers a comprehensive workflow automation platform for revenue teams, designed to streamline sales processes and improve productivity. Its tools, such as Deal Rooms, Automated Slack channels, Cues, Approvals, Playbooks, and Advanced Automations, centralize all sales-related activities and enhance communication, thus enabling real-time collaboration. Momentum's features include AI Summaries and Notifications, which provide pre-built recipes, notifications, account rooms, and customer c

Momentumized, adaptive, dual averaged gradient

MADGRAD is a modification of a deep learning optimization method called AdaGrad-DA. It improves the performance of AdaGrad-DA, enabling it to solve more complex problems effectively. MADGRAD gives excellent results, surpassing even the best optimization method Adam in various cases. In this article, we'll provide an overview of the MADGRAD method and explain how it works for deep learning optimization. What is Optimization? Optimization is a critical aspect of machine learning, a subset of ar

Monocular 3D Human Pose Estimation

Monocular 3D human pose estimation is a process that involves predicting the 3D locations of various body parts using only a single RGB camera. This task has applications in various fields, such as sports analysis, human-computer interaction, and health monitoring. What is Monocular 3D Human Pose Estimation? Human pose estimation is the process of detecting and locating the body parts of humans in images or videos. This process is significant in various fields, such as sports analysis, comput

Monocular Depth Estimation

Monocular Depth Estimation: Understanding the Depth of a 2D Image Monocular Depth Estimation is a critical task in computer vision that allows us to estimate the distance between the camera and various objects and surfaces in the image. It involves the use of a single RGB image to determine the precise depth value of every pixel in the image. This technique is significant for a variety of applications such as 3D scene reconstruction, autonomous driving, and augmented reality (AR). The Challen

Monte-Carlo Tree Search

Monte-Carlo Tree Search: An Introduction If you've ever played a game with an AI opponent, chances are that the AI was using some form of planning algorithm to determine its next move. One such algorithm that has gained popularity in recent years is the Monte-Carlo Tree Search (MCTS). It's a planning algorithm that uses Monte Carlo simulations to make decisions, and it's used in a variety of fields, from game AI to robotics, and even finance. What is Monte Carlo Simulation? Before we dive in

Morphence

Many machine learning models, such as those used in image recognition and speech processing, are vulnerable to attacks from adversarial examples. Adversarial examples are inputs that have been intentionally manipulated to trigger the model into making an incorrect prediction. This can have serious implications, such as misidentification in security systems or misdiagnosis in medical applications. Introducing Morphence Morphence is an approach to adversarial defense that aims to make a model a

Motion Forecasting

Motion Forecasting: Predicting the Future of Tracked Objects Have you ever watched a movie where technology experts use satellite images or cameras to track the movement of a vehicle or person? They can tell where the vehicle or person is right now and how fast they're moving. However, what if we could also predict where the vehicle or person is going to be in the future? That's what motion forecasting is all about. The Definition of Motion Forecasting Motion forecasting is the process of pr

MotionNet

MotionNet: Revolutionizing Joint Perception and Motion Prediction MotionNet is a cutting-edge system designed for joint perception and motion prediction using a bird's eye view (BEV) map. It encodes the object's group and movement data from 3D point clouds into each grid cell. It takes a sequence of LiDAR scans as input and outputs the BEV map. The MotionNet infers an object's state of motion from a sequence of LiDAR scans, and then predicts its position and posture in the future. Having an ac

Movement Pruning

Movement pruning is a pruning method used for simplifying the structure of deep neural networks by removing some of the connections between neurons. This technique is more adaptive to fine-tuning of pre-trained models and is a first-order weight pruning method. Unlike magnitude pruning, movement pruning methods derive importance from first-order information. Instead of selecting weights that are far from zero, movement pruning retains connections that are moving away from zero during the trainin

MoViNet

Mobile Video Network, or MoViNet, is a novel technology that allows for efficient video network computation and memory. It is designed to work on streaming videos for online inference. The technique includes three main elements that optimize efficiency while lowering the peak memory usage of 3D Convolutional Neural Networks (CNNs). Neural Architecture Search The first step in developing MoViNet involved creating a video network search space and employing neural architecture search. The goal w

MPNet

What is MPNet and How Does it Work? MPNet is a pre-training method for language models that combines two approaches, masked language modeling (MLM) and permuted language modeling (PLM), to create a more efficient and effective model. It was designed to address the issues of two other popular pre-training models, BERT and XLNet. MPNet takes into consideration the dependency among predicted tokens and alleviates the position discrepancy of XLNet by utilizing the position information of all tokens

MPRNet

Overview of MPRNet MPRNet is a cutting-edge technology that aids image processing experts in restoring degraded input. It is a multi-stage progressive image restoration architecture, which means that it breaks down the overall recovery process into manageable steps. As a result, restoration becomes quicker and more efficient. Image restoration is the process of improving the quality of a digital image that has been degraded by noise, blur, or other unwanted artifacts. MPRNet learns the restora

MT-PET

MT-PET: A Multi-Task Approach to Exaggeration Detection If you're interested in natural language processing, you might have heard of PET, or Pattern Exploiting Training. It's a technique that uses masked language modeling to transform tasks into cloze-style question answering tasks, making them easier to solve. It has been shown to be effective in few-shot learning, where there is only a small amount of data available for training. However, a new technique called MT-PET takes this idea to the n

mT5

MT5: Multilingual Natural Language Processing Advancement What is MT5? MT5 is a natural language processing (NLP) advancement that is designed to handle multiple languages. It is a multilingual variant of T5 that has been pre-trained on a large dataset of over 101 languages. MT5 is used for machine translation, text classification, summarization, and question answering. Why is MT5 Important? MT5 is important because it bridges the gap between cross-lingual NLP models and multilingual model

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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