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XLNet

XLNet is a type of language model that uses a technique called autoregressive modeling to predict the likelihood of a sequence of words. Unlike other language models, XLNet does not rely on a fixed order to predict the likelihood of a sequence, but instead uses all possible factorization order permutations to learn bidirectional context. This allows each position in the sequence to learn from both the left and the right, maximizing the context for each position. What is Autoregressive Language

XLSR

XLSR: Multilingual Speech Recognition Model Have you ever considered how speech recognition works for multiple languages? How do you train a model to understand various tongues? The answer is XLSR - a multilingual speech recognition model built on wav2vec 2.0. The model is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. In simpler terms, XLSR is a speech recognition model that recognizes m

YellowFin

YellowFin: An Efficient Learning Rate and Momentum Tuner YellowFin is a state-of-the-art optimization algorithm that automatically tunes the learning rate and momentum in deep learning models. It is motivated by a robustness analysis of quadratic objectives and aims to improve the convergence rate of deep neural networks by optimizing hyperparameters. The significance of YellowFin lies in the fact that it extends the notion of tuning learning rates and momentum to non-convex objectives. This a

YOLOP

What is YOLOP? YOLOP is a new technology in the field of self-driving cars that stands for "You Only Look Once Perception". It is a driving perception network that performs multiple tasks simultaneously such as traffic object detection, drivable area segmentation, and lane detection. YOLOP uses a lightweight CNN to extract image features which are then fed to three decoders to complete their respective tasks. YOLOP is considered as a lightweight version of Tesla's HydraNet self-driving vehicle

YOLOv1

YOLOv1: The Revolutionary Single-stage Object Detection Model YOLOv1 is a groundbreaking object detection model that has greatly revolutionized object detection in computer vision. It is a single-stage object detection model that uses deep neural networks to identify objects in images, making it faster and more accurate than previous object detection methods. How YOLOv1 Works The YOLOv1 network transforms object detection into a regression problem. By using spatially separated bounding boxes

YOLOv2

Object detection is a key area in computer vision, and YOLOv2 is a powerful tool used for this purpose. YOLOv2 stands for You Only Look Once version 2, and is an improved version of the earlier YOLOv1. What is Object Detection? Object detection is the process of identifying objects in images or videos and accurately placing a bounding box around them. This is a crucial task for many applications such as self-driving cars, surveillance systems, and augmented reality. What is YOLOv2? YOLOv2

YOLOv3

YOLOv3 is an advanced object detection model that is designed to detect objects in real-time. It is a single-stage model that has made significant improvements over YOLOv2. The model is built on a new backbone network, Darknet-53, which uses residual connections to improve performance. Additionally, YOLOv3 uses three different scales from which it extracts features, allowing it to provide better object detection results. What is Object Detection? Object detection is a computer vision techniqu

YOLOv4

YOLOv4: The Latest Advancement in Object Detection Model When it comes to detecting objects in images, YOLOv4 is the latest state-of-the-art model that is taking the field by storm. Building on the success of the previous version, YOLOv3, this new model includes various bags of tricks and modules to improve its performance and accuracy. What is Object Detection? Object detection is a computer vision technique that aims to find and identify objects within an image or video. It is a challengin

YOLOX

YOLOX is an object detector that has been making several modifications to YOLOv3 with a DarkNet53 backbone. This modified detector has been altered for better performance by replacing the head with a decoupled one, reducing feature channel and adding two parallel branches. Moreover, it has added Mosaic and MixUp into the augmentation strategies to enhance performance. This article will explore further the modifications of the YOLOX detector alongside its features. YOLOX Features The YOLOX det

You Only Hypothesize Once

The YOHO framework for point cloud registration If you work with 3D data, you know how important it is to be able to align different point clouds in a reliable, repeatable way. Point cloud registration is the process of finding the spatial transformation that brings two point clouds into a common reference frame, meaning that corresponding points from the two clouds can be matched up. Researchers have proposed many algorithms for point cloud registration, but they often suffer from sensitivity

ZCA Whitening

What is ZCA Whitening? ZCA Whitening is a method used for image preprocessing, which means it is a step that is taken to prepare an image for further analysis. Essentially, the goal of ZCA Whitening is to transform the data in an image so that the features (or elements) are uncorrelated, which can make it easier to work with the image data. ZCA stands for "Zero-phase Component Analysis," which refers to the mathematical techniques used to achieve this type of transformation. The end result of Z

ZeRO-Infinity

ZeRO-Infinity is a cutting-edge technology designed to help data scientists tackle larger and more complex machine learning projects. It is an extension of ZeRO, a sharded data parallel system that allows for parallel training of large models across multiple GPUs. However, what sets ZeRO-Infinity apart is its innovation in heterogeneous memory access, which includes the infinity offload engine and memory-centric tiling. Infinity Offload Engine One of the biggest challenges of training large m

ZeRO-Offload

What is ZeRO-Offload? ZeRO-Offload is a method for distributed training where data is split between multiple GPUs and CPUs. It is called a sharded data parallel method because it exploits both CPU memory and compute for offloading. This efficient method offers a clear path towards efficiently scaling on multiple GPUs by working with ZeRO-powered data parallelism. How ZeRO-Offload Works ZeRO-Offload maintains a single copy of the optimizer states on the CPU memory regardless of the data paral

Zero-padded Shortcut Connection

The Zero-padded Shortcut Connection is a type of residual connection that is utilized in the PyramidNet architecture. PyramidNets use residual connections to enable deeper networks while preventing the accuracy from degrading, and the zero-padded method is one of the techniques they use. What is a residual connection? Residual connections, also known as skip connections, are designed to solve the problem of vanishing gradients. Vanishing gradients occur when the gradient of a loss function go

Zero-Shot Learning

Zero-shot learning, or ZSL, is a model's ability to detect classes that it has never seen before during training. This means that even if the classes are not known during supervised learning, the model can still identify them through other means. How ZSL Works Earlier approaches in ZSL use attributes in a two-step approach to infer unknown classes. In computer vision, more recent advances learn mappings from the image feature space to semantic space. This involves learning how to identify ima

Zero-Shot Machine Translation

Zero-Shot Machine Translation: A New Era of Language Learning Introduction: Language is one of the biggest bridges between people and cultures worldwide. However, communicating across languages has been a barrier for humankind from time immemorial. Thanks to the advancements in technology in the 21st century, this problem has been solved to a great extent with the introduction of machine translation. Machine translation is the use of software to translate text or speech from one language to a

Zero-shot Relation Triplet Extraction

What is Zero-shot Relation Triplet Extraction? Zero-shot Relation Triplet Extraction refers to the process of extracting important information from a given sentence in the form of triplet consisting of the head entity, relation label, and tail entity. It is a natural language processing task that is being widely studied in the field of machine learning and artificial intelligence. In simple terms, the goal of the task is to extract important pieces of information from text without any prior kno

Zero Shot Skeletal Action Recognition

Zero Shot Skeletal Action Recognition Zero Shot Learning for 3D skeletal action recognition is a task to recognize human action from skeleton joints data without any pre-training information or any human-labeled data. This task is one of the most challenging tasks for the machine learning community. Many previous works in this field rely on heavily pre-training or human-labeled data that may limit their scalability and generalization. The Challenge in Skeletal Action Recognition Skeletal act

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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