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BP-Transformer

BP-Transformer (BPT) is a new type of transformer that has gained popularity for self-attention tasks owing to its better balance between capability and computational complexity. It achieves this by partitioning the input sequence into multi-scale spans through binary partitioning. Motivation for BP-Transformer The motivation behind developing BP-Transformer was to overcome the limitations with existing transformer models that struggle with self-attention and are computationally expensive. BP

Brain Segmentation

Brain segmentation is a medical imaging technique that divides the brain into different regions or structures based on their imaging characteristics. It is a vital tool in neuroscience and neurology research, diagnostics, and treatment planning. Advances in medical imaging technologies have made it possible to acquire detailed images of the brain, which can be used to identify abnormalities, measure brain volume, and track disease progression. Brain segmentation helps neuroscientists and clinici

Brain Tumor Segmentation

What is Brain Tumor Segmentation? Brain tumor segmentation is a medical imaging task that involves the separation of brain tumors from normal brain tissue using magnetic resonance imaging (MRI) scans. The main goal is to produce an accurate binary or multi-class segmentation map that reflects the location and extent of the tumor. Why is Brain Tumor Segmentation Important? Brain tumors are abnormal growths that can develop in different parts of the brain, and they can be life-threatening if n

Branch attention

The Importance of Branch Attention Have you ever struggled to stay focused on one task when there are so many other distractions around you? This is where the concept of branch attention comes into play. Branch attention is a mechanism that helps individuals select which branch or task to focus on when there are multiple options available. What is Branch Attention? Branch attention refers to a cognitive process that helps people prioritize and focus on one task or branch among several. This

Breast Cancer Histology Image Classification (20% labels)

Understanding Breast Cancer Histology Image Classification Breast cancer is one of the most common forms of cancer among women across the globe. It occurs when the abnormal cells in the breast start to grow out of control, eventually forming a tumor. While breast cancer can affect both men and women, it is more prevalent in women. One of the ways to diagnose breast cancer is through histology, where doctors examine the tissue samples to identify if the cells are normal or cancerous. Histology

BRepNet

BRepNet: Revolutionizing CAD with Neural Networks If you've ever worked with CAD (computer-aided design) software, you know how important it is to create accurate and detailed models. However, traditional modeling methods involve approximating the model as meshes or point clouds, which can lead to loss of information and inaccuracies. Fortunately, BRepNet, a neural network designed for CAD applications, is changing the game by operating directly on B-rep data structures. What is BRep Data? B

Bridge-net

The topic of Bridge-net is a technical concept related to the field of text-to-speech architecture. It is an audio model block utilized in the ClariNet architecture to map frame-level hidden representation to sample-level. In simpler terms, it is a tool used to convert written text to spoken words. Understanding Bridge-net in ClariNet The ClariNet architecture is a system that converts written text to speech using deep learning techniques. In this system, Bridge-net plays an important role by

BS-Net

BS-Net is a new architecture designed to predict the severity of COVID-19 based on clinical data from different sources. This architecture uses four different blocks, which work together to estimate a six-valued score of the disease. This score is based on the interpretation of CXRs, which can be difficult and produce inter-rater variability among radiologists. How BS-Net Works The input image is processed using a convolutional backbone known as ResNet-18. Then, segmentation is performed usin

BTmPG

What is BTmPG? BTmPG stands for Back-Translation guided multi-round Paraphrase Generation. It is a method used to generate different variations of a given sentence or phrase. This method uses back-translation to guide the model during training and generates paraphrases in a multiround process. How does BTmPG work? The BTmPG model regards paraphrase generation as a monolingual translation task. It starts with a paraphrase pair, which consists of an original sentence (source sentence) and a ta

building to building transfer learning

Business-to-business (B2B) transfer learning is a method of using machine learning algorithms to transfer knowledge from one building to predict the energy consumption of another building. This is particularly useful when one building has scarce data available for analysis. What is Transfer Learning? Transfer learning is a machine learning technique in which a model trained on one task is used to make predictions on a different task. The idea is to use the knowledge gained from one task to he

Burst Image Super-Resolution

Burst Image Super-Resolution is an emerging field in computer vision and image processing that focuses on reconstructing high-resolution images from a set of low-quality images. The technique involves using multiple images taken from the same scene or object, each with slightly different camera settings, and combining them to create a single high-resolution image. The term "burst" refers to a series of images taken in quick succession without moving the camera. Typically, burst images are used

Byte Pair Encoding

In today's technologically advanced world, natural language processing is a vital field that aims to develop machines capable of understanding human language. One of the critical components of natural language processing is subword segmentation, which breaks down complex words into smaller units. This is where Byte Pair Encoding, or BPE, comes in. What is BPE? BPE is a subword segmentation algorithm that encodes rare and unknown words by dividing them into sequences of subword units. The algo

BytePS

What is BytePS? BytePS is a method used for training deep neural networks. It is a distributed approach that can be used with varying numbers of CPU machines. BytePS can handle traditional all-reduce and parameter server (PS) as two special cases within its framework. How does BytePS work? BytePS makes use of a Summation Service and splits a DNN optimizer into two parts: gradient summation and parameter update. For faster DNN training, the CPU-friendly part, gradient summation, is kept on CP

ByteScheduler

Distributed deep neural network training can be a complex process, especially when it comes to communication between nodes. This is where ByteScheduler comes in. ByteScheduler is a communication scheduler designed specifically to optimize distributed DNN training acceleration. What is ByteScheduler? ByteScheduler is a generic communication scheduler for distributed deep neural network (DNN) training. It is based on the idea that rearranging and partitioning tensor transmissions can lead to op

C5.0

Understanding C5.0: Definition, Explanations, Examples & Code C5.0 is a decision tree algorithm used for supervised learning. It is an updated version of the earlier ID3 algorithm, and is widely used to generate decision trees. C5.0: Introduction Domains Learning Methods Type Machine Learning Supervised Decision Tree C5.0 is a decision tree algorithm that is widely used in supervised learning. It is an updated version of the ID3 algorithm and is known for its high accuracy and per

CAMoE

What is CAMoE? CAMoE is a cutting-edge technology that enables video-text retrieval through a multi-stream corpus alignment network with single gate Mixture-of-Experts. This technology is designed to extract multi-perspective video representations, including action, entity, scene, among others, and align them with their corresponding text descriptions. How Does CAMoE Work? CAMoE relies on Mixture-of-Experts (MoE) to extract multiple perspectives from videos, which allows for a more comprehen

CANINE

Canine: A Language Understanding Encoder Canine is a pre-trained encoder for language understanding. It operates directly on character sequences, without explicit tokenization or vocabulary. It uses a pre-training strategy with soft inductive biases in place of hard token boundaries. Essentially, Canine is a machine learning algorithm that understands language by analyzing sequences of characters, which is different from many other algorithms that rely on pre-defined word boundaries. Canine's

Canonical Tensor Decomposition with N3 Regularizer

CP-N3: A Hierarchical Tensor Decomposition Method for Data Analysis CP-N3 is a powerful method for decomposing complex data structures into their component parts. This technique uses a mathematical tool called a tensor to represent complex data sets, and then applies a decomposition algorithm to obtain a set of simpler, more manageable representations. In particular, CP-N3 uses a canonical tensor decomposition method that is trained using a regularized variant of the N3 regularization technique

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Model Blocks Audio to Text Augmented Reality Methods Auto Parallel Methods Autoencoding Transformers AutoML Autoregressive Transformers Backbone Architectures Bare Metal Bare Metal Cloud Bayesian Reinforcement Learning Behaviour Policies Bidirectional Recurrent Neural Networks Bijective Transformation Binary Neural Networks Board Game Models Bot Detection Cache Replacement Models CAD Design Models Card Game Models Cashier-Free Shopping ChatGPT ChatGPT Courses ChatGPT Plugins ChatGPT Tools Cloud GPU Clustering Code Generation Transformers Computer Code Computer Vision Computer Vision Courses Conditional Image-to-Image Translation Models Confidence Calibration Confidence Estimators Contextualized Word Embeddings Control and Decision Systems Conversational AI Tools Conversational Models Convolutional Neural Networks Convolutions Copy Mechanisms Counting Methods Data Analysis Courses Data Parallel Methods Deep Learning Courses Deep Tabular Learning Degridding Density Ratio Learning 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 Networks Generalization Generalized Additive Models Generalized Linear Models Generative Adversarial Networks Generative Audio Models Generative Discrimination Generative Models Generative Sequence Models Generative Training Generative Video Models Geometric Matching Graph Data Augmentation Graph Embeddings Graph Models Graph Representation Learning Graphics Models Graphs Heuristic Search Algorithms Human Object Interaction Detectors Hybrid Fuzzing Hybrid Optimization Hybrid Parallel Methods Hyperparameter Search Image Colorization Models Image Data Augmentation Image Decomposition Models Image Denoising Models Image Feature Extractors Image Generation Models Image Inpainting Modules Image Manipulation Models Image Model Blocks Image Models Image Quality Models Image Representations Image Restoration Models Image Retrieval Models Image Scaling Strategies Image Segmentation Models Image Semantic Segmentation Metric Image Super-Resolution Models Imitation Learning Methods Incident Aggregation Models Inference Attack Inference Engines Inference Extrapolation Information Bottleneck Information Retrieval Methods Initialization Input Embedding Factorization Instance Segmentation Models Instance Segmentation Modules Interactive Semantic Segmentation Models Interpretability Intra-Layer Parallel Keras Courses Kernel Methods Knowledge Base Knowledge Distillation Label Correction Lane Detection Models Language Model Components Language Model Pre-Training Large Batch Optimization Large Language Models (LLMs) Latent Variable Sampling Layout Annotation Models Leadership Inference Learning Rate Schedules Learning to Rank Models Lifelong Learning Likelihood-Based Generative Models Link Tracking Localization Models Long-Range Interaction Layers Loss Functions Machine Learning Machine Learning Algorithms Machine Learning Courses Machine Translation Models Manifold Disentangling Markov Chain Monte Carlo Mask Branches Massive Multitask Language Understanding (MMLU) Math Formula 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 Optimization Structured Prediction Style Transfer Models Style Transfer Modules Subscription Managers Subword Segmentation Super-Resolution Models Supervised Learning Synchronous Pipeline Parallel Synthesized Attention Mechanisms Table Parsing Models Table Question Answering Models Tableau Courses Tabular Data Generation Taxonomy Expansion Models Temporal Convolutions TensorFlow Courses Ternarization Text Augmentation Text Classification Models Text Data Augmentation Text Instance Representations Text-to-Speech Models Textual Inference Models Textual Meaning Theorem Proving Models Thermal Image Processing Models Time Series Time Series Analysis Time Series Modules Tokenizers Topic Embeddings Trajectory Data Augmentation Trajectory Prediction Models Transformers Twin Networks Unpaired Image-to-Image Translation Unsupervised Learning URL Shorteners Value Function Estimation Variational Optimization Vector Database Video Data Augmentation Video Frame Interpolation Video Game Models Video 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