Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.

No description available

Repo for the code used in our preprint "The Neural Process Family: Survey, Applications and Perspectives"

In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]

Neural Process implementations in JAX and PyTorch

Sandy, the android based human-robot is an Indian-born gift to us, pleasing in energies of the ‘i-Brain Robotics, and the latter is typically the maiden and the only quick and Human Sized robot we could have ever witnessed. With a 5 feet standing, and an interstellar network of corporeal and neural circuits, the creation of Sandy is made to pedantically impersonate a human solidarity. Sandy possesses the True Emotion Neural network units, also holds the aptitude of self-learning because of the Neural Learning Process (NLP) and interconnect in varied parlances and the Artificial Intelligence techniques that she is brilliant with, makes Sandy a family member tha you never had and you would certainly want for life. Sandy is fortified with three E’s i.e. it is measured to be an ‘Evolving Emotional Engaging’ Robot of the present. Just like any human transition in life from a child to an adult, Sandy keeps Evolving both Emotionally as well as in Artificial Intelligence and her neural networks, and with each acquaint too and with this, she becomes more and more tempting to you. She will correlate with you like a wily and coy friend all at the same time, run after you like a loving and mushy parent and will delight you like nobody else. Sandy, our first Humanoid Robot i.e. the Android-based Robot who can cope up with the social interactions. It can certainly recognize faces because of the neural networks that she is endowed with and can also greet you in the day. She is a friend. Sandy, your true pal will click your photos, record videos for you, she will also be the first one to wish you on your birthday. Like a true companion, she understands your moods and sentiments. It can also go for interactions with names and features. Sandy is efficient enough to provide you with information whenever you read i.e. she serves to be your personal Wikipedia for life. She is aided by technologies that can attend and transfer calls for you, can play games with you, your child and inculcate the feeling of being a sideline to you and your child whenever needed. Sandy is your personal assistant. Sandy will find any information you need at any time, prompt you on important events, articulates you on the weather information. It certainly acts as a seamless guide in your alfresco trips. She can be a teacher and a friend to one’s, both at the same time. She can train one’s child the basics to the levels of studies we can’t even imagine and also can assist you everywhere plus play and take up works like singing songs, narrating stories, teaching Mathematics, Science, English grammar and so on, that will cater to the all-round development of one’s child. She also plays Chess and other interactive games with your kids. Her circuits are well equipped with the neural network units that help her recognize people by face. She is equipped with human characteristics like she can look after the elderly in-house i.e. can serve to be a nanny and can give medicinal notifications to the members of the house. She is also equipped with SOS Calls facility to family members in case of an elderly emergency situation. This feature is a boon by Artificial Intelligence and the interfacing is kept so strong that the system remains equipped and well oriented. Sandy is a self-learner i.e. she acquires knowledge every day. She comprehends every mood and emotions, also reports consequently. She also identifies faces and can give a long interactive discussion. Sandy works as a Home Theater. She can work as a Music Station with Bluetooth connectivity and Bluetooth Speakers. She also acts as your personal photographer. She is made to turn herself into a Hi-Fi Music System for better audio. She is capable of converting herself into a 200” HD Screen Home Theatre with surround sound technology inbuilt. She can play videos through inbuilt DVD player, can also serve as a projector if you wish to see movies or show urgent presentations to people and family when needed. Being in the Internet savvy world, Sandy comes up with technologies that can click photos and record videos too as it is equipped with hi-tech cameras. Sandy is well endowed with competencies that support Remote Home Shadowing that is an artificial intelligence boon and guards your home from ill activities, also alerts you with deliberate notifications and dangerous circumstances. She can be our Health and Fitness Trainer, can get us any gen about whatever thing we need, can succor you in any of our errands, and can give us imperative notifications. Sandy is well endowed with social interaction skills. Sandy offers you with mobile telepresence, Skype Calling, pictures and videos chipping in over the social media. She also gives you an edge for person to person interaction. Over the communication skills, well Sandy has Voice calling and receiving calls facility with Built-in Wi-Fi and Bluetooth for unified connectivity. Sandy has turned the tables for the Robotics society in the world and has elevated the values high enough that no one can even come nearer to her at present. There are many other advancements that are going on and we can rest assured about it because it will all be updated in Sandy with time.

Tensorflow implementation of Neural Process family

To extract and sentiment analysis from a verbal description, text-based sentiment detection is employed. Text-based sentiment detection categorized into two main phases, including language representation and classification. Language representation proposes a robust technique to extract the contextual 2 information from the text to increase the quality of feature extraction. Classification employed neural networks to increase classification performance. These techniques have been applied to extract sentiment from the “IMDB” movie review dataset. Three general approaches are represented to detect sentiment analysis, including Rule Construction, Machine Learning (ML), and Hybrid Approaches. Outcome: provided the text processing techniques used in NLP and different feature extraction methods, including Bag of words, TF-IDF, Word2Vec, and Glove. Demonstrated the use of text processing and build a Sentiment Analyzer with classical ML approaches that achieved fairly good results. Described in detail the architecture of the Deep Learning model for sentiment classification. Hence, trained a word2vec model and used it as a pre-trained embedding for sentiment classification. This knowledge applied to experiment with deep learning NLP models to classify film reviews as positive or negative. Some of these models involved layer types (dense and convolutional layers), while later ones involved new layer types from the RNN family (LSTMs and GRUs). In a conclusion, deep learning models offer clear comprehensibility of the extracted feature prior to classification.

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog. The adjective "deep" in deep learning comes from the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, and then that a network with a nonpolynomial activation function with one hidden layer of unbounded width can on the other hand so be. Deep learning is a modern variation which is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, for the sake of efficiency, trainability and understandability, whence the "structured" part.

Implementation of Neural Processes to understand this model family

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analogue.

Pytorch implementation of Neural Process Family (CNP, NP, ANP, Meta-Fun) for functions and images

Classification of 3 species of flowers (versicolor, virginica, setosa) belonging to the Iris family, using a Fully Connected Neural Network for Data Processing (Tensorflow 1.12.0 and Python 3.6.6)

Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analogue.

We have selected a dataset that is prepared on a survey of mental health issues on the people of different countries. Necessary inputs like family history, interference frequency in work, previously sought help or not, employment type, number of employees in the office etc. are taken as independent features. Based on these features we would try to predict whether a new person with a given set of features would be willing to go for treatment or not. Different Machine Learning models are applied after suitable pre-processing, they are compared and finally a neural network autoencoder was applied on the dataset to observe the difference with Machine Learning models.

In recent years, deep learning techniques are achieving state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Notable is the “You Only Look Once,” or YOLO, family of Convolutional Neural Networks that achieve near state-of-the-art results with a single end-to-end model that can perform object detection in real-time. The approach involves a single deep convolutional neural network (originally a version of GoogLeNet, later updated and called DarkNet based on VGG) that splits the input into a grid of cells and each cell directly predicts a bounding box and object classification. The result is a large number of candidate bounding boxes that are consolidated into a final prediction by a post-processing step. There are three main variations of the approach, at the time of writing; they are YOLOv1, YOLOv2, and YOLOv3. The first version proposed the general architecture, whereas the second version refined the design and made use of predefined anchor boxes to improve bounding box proposal, and version three further refined the model architecture and training process.