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CodeBERT

CodeBERT is a special kind of computer model that can help people understand computer code and information written in English. It is called a bimodal model because it can understand both programming language (PL) and natural language (NL). This model can help people do many things, like find specific code that they need or automatically write descriptions of how code works. How Does CodeBERT Work? CodeBERT is made with a special kind of neural network called a Transformer. This network helps

CodeSLAM

What is CodeSLAM? CodeSLAM is a technology that enables 3D geometry representation of a scene using a variational autoencoder's latent space. A depth map is generated from the RGB image and the unknown code $D = G_\theta(I,c)$. How Does CodeSLAM Work? During training, the generator and encoder are trained using a standard autoencoding task to learn the weights of the $G_\theta$ network. At test time, you can find the code $c$ and the image's pose by optimizing the reprojection error over mul

CodeT5

CodeT5 is a new model that uses Transformer technology for better code understanding and generation. It is based on the T5 architecture, which has been extended to include two identifier tagging and prediction tasks that help the model to better leverage the token type information from programming languages. CodeT5 uses a bimodal dual learning objective for a bidirectional conversion between natural language and programming language, which helps improve the natural language-programming language

COLA

What is COLA? COLA stands for “Contrastive Learning of Audio”. It is a method used to train artificial intelligence models to learn a general-purpose representation of audio. Essentially, COLA helps machines understand what different sounds mean. How Does COLA Work? The COLA model learns by contrasting similarities and differences within audio segments. It assigns a high level of similarity to segments extracted from the same recording, while labeling segments from different recordings as le

Collaborative Distillation

Collaborative Distillation: A New Method for Neural Style Transfer Collaborative distillation is a novel method for knowledge distillation in encoder-decoder based neural style transfer. This method aims to reduce the number of convolutional filters required in neural style transfer by leveraging the collaborative relationship between encoder-decoder pairs. The concept of collaborative distillation is rooted in the idea that encoder-decoder pairs work together to create an exclusive collaborat

Collapsing Linear Unit

CoLU is a cleverly crafted activation function that has numerous unique properties favorable to the performance of deeper neural networks. Developed alongside similar activation functions, Swish and Mish, CoLU boasts properties such as smoothness, differentiability, and being unbounded above while simultaneously being bounded below. It is also non-saturating and non-monotonic. What is an Activation Function? Before discussing the properties and benefits of CoLU, it is essential to understand

Color Constancy

Understanding Color Constancy: What It Is and How It Works Color constancy is the incredible ability of the human vision system to perceive the colors of objects in a scene largely invariant to the color of the light source. That is, we are able to see colors as we know them, regardless of the ambient light. For instance, a white shirt appears white whether we see it outdoors in daylight or indoors under artificial light. This is due to the visual system’s amazing capacity to adapt to illuminan

Color Jitter

Image data augmentation is an important technique used in machine learning to prevent overfitting and improve the accuracy of image classification models. One such technique is ColorJitter which is used to modify the color of images by randomizing the brightness, contrast, and saturation values. What is Image Data Augmentation? Before diving into the details of ColorJitter, it's essential to understand what image data augmentation is and why it is used. Image data augmentation is a technique

Colorization Transformer

Overview of Colorization Transformer Colorization Transformer is a complex probabilistic model used to add color to black and white images. A global receptive field with only two layers and a reduced complexity of $O(D\sqrt{D})$ instead of $O(D^2)$ are the main benefits of colorization transformer's axial self-attention blocks. To perform colorization on high-resolution grayscale images, the process is split into three simpler sequential tasks using a variation of Axial Transformer. What is C

Colorization

Colorization is an innovative approach to self-supervision learning that uses the process of colorizing images to create more efficient image representations. This method is gaining momentum in various applications, such as in the field of machine learning, where it is used to teach artificial intelligence how to interpret and generate images. What is Colorization? Colorization is a technique of inferring what colors were present in a gray-scale image, creating the illusion of a color image.

ComiRec

Overview of ComiRec If you are someone who loves reading comic books, manga or graphic novels, then you must be familiar with the struggle of finding new and exciting content to read. Sometimes you may end up scrolling through endless pages of similar recommendations, trying to find something new to read. That's where **ComiRec** comes in, a new framework for sequential recommendation that prioritizes your interests to offer personalized recommendations. ComiRec is a framework designed to cate

Common Sense Reasoning

Common Sense Reasoning: How Our World Knowledge Helps Us Make Inferences What is Common Sense Reasoning? Common sense can be defined as the basic level of practical knowledge and perception that we all possess about the world around us. It is the knowledge that we use in our everyday lives to make sense of the situations we find ourselves in. Common Sense Reasoning (CSR) is a branch of artificial intelligence (AI) that focuses on creating machines that can reason in the same way that humans

Community Question Answering

Community question answering is a valuable resource for people looking for answers to their questions. It involves asking questions on Q&A forums or boards, like Stack Overflow and Quora, and receiving answers from other community members. How Community Question Answering Works Community question answering works by creating an online community of experts who can help answer questions. People post their questions on a forum or board, and other members who are knowledgeable about the topic will

Commute Times Layer

Overview of CT-Layer: A Differentiable and Learnable Rewiring Layer CT-Layer is a graph neural network layer that is able to rewire a graph in an inductive and parameter-free way according to the commute times distance or effective resistance. CT-Layer addresses the issue of learning a differentiable way to compute the CT-embedding of the graph, which is not possible with the traditional spectral version. CT-Layer provides a new approach to rewire a given graph optimally, leading to a better un

Compact Convolutional Transformers

Compact Convolutional Transformers: Increasing Flexibility and Accuracy in Artificial Intelligence Models Compact Convolutional Transformers (CCT) are a form of artificial intelligence models that utilize sequence pooling and convolutional embedding to improve the inductive bias and accuracy of models. By removing the need for positional embeddings, CCT is able to increase the flexibility of input parameters while maintaining or even improving accuracy over similar models such as ViT-Lite. In t

Compact Global Descriptor

When it comes to machine learning and image processing, the Compact Global Descriptor (CGD) is an important model block for modeling interactions between different dimensions, such as channels and frames. Essentially, a CGD helps subsequent convolutions access useful global features, acting as a form of attention for these features. What is a Compact Global Descriptor? To understand what a Compact Global Descriptor is, it may be helpful to first define what is meant by a "descriptor" in this

Complex Query Answering

Complex Query Answering Complex query answering involves predicting the existence of relationships between nodes in a knowledge graph. This task becomes challenging when dealing with incomplete information and complex relationships between nodes, such as 2-hop and 3-paths, or intersecting paths with intermediate variables. What is a Knowledge Graph? A knowledge graph is a structure that organizes information into entities and relationships between them. It is used to represent human knowledg

ComplEx with N3 Regularizer and Relation Prediction Objective

ComplEx-N3-RP is a type of machine learning model that is designed to predict relationships between different objects or entities. This type of model is used in a wide range of applications, including natural language processing, social network analysis, and recommendation systems. What is ComplEx? ComplEx, which stands for Complex-valued Embedding of Entities and Relations, is a type of neural network that is designed to represent objects and relationships in a complex vector space. This mea

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2D Parallel Distributed Methods 3D Face Mesh Models 3D Object Detection Models 3D Reconstruction 3D Representations 6D Pose Estimation Models Action Recognition Blocks Action Recognition Models Activation Functions Active Learning Actor-Critic Algorithms Adaptive Computation Adversarial Adversarial Attacks Adversarial Image Data Augmentation Adversarial Training Affinity Functions AI Adult Chatbots AI Advertising Software AI Algorithm AI App Builders AI Art Generator AI Art Generator Anime AI Art Generator Free AI Art Generator From Text AI Art Tools AI Article Writing Tools AI Assistants AI Automation AI Automation Tools AI Blog Content Writing Tools AI Brain Training AI Calendar Assistants AI Character Generators AI Chatbot AI Chatbots Free AI Coding Tools AI Collaboration Platform AI Colorization Tools AI Content Detection Tools AI Content Marketing Tools AI Copywriting Software Free AI Copywriting Tools AI Design Software AI Developer Tools AI Devices AI Ecommerce Tools AI Email 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Writers AI Summarization Tools AI Summarizers AI Testing Tools AI Text Generation Tools AI Text to Speech Tools AI Tools For Recruiting AI Tools For Small Business AI Transcription Tools AI User Experience Design Tools AI Video Chatbots AI Video Creation Tools AI Video Transcription AI Virtual Assistants AI Voice Actors AI Voice Assistant Apps AI Voice Changers AI Voice Chatbots AI Voice Cloning AI Voice Cloning Apps AI Voice Generator Celebrity AI Voice Generator Free AI Voice Translation AI Wearables AI Web Design Tools AI Web Scrapers AI Website Builders AI Website Builders Free AI Writing Assistants AI Writing Assistants Free AI Writing Tools Air Quality Forecasting Anchor Generation Modules Anchor Supervision Approximate Inference Arbitrary Object Detectors Artificial Intelligence Courses Artificial Intelligence Tools Asynchronous Data Parallel Asynchronous Pipeline Parallel Attention Attention Mechanisms Attention Modules Attention Patterns Audio Audio Artifact Removal Audio 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