Inductive learning, or induction, is the process of creating generalizations from individual instances.
Human learning comes in two forms, a fast and a slow one. The slow one requires a lot of repetition which seems to be necessary to conquer a new cognitive field such as learning a new language. But once a field is mastered, learning new facts within this field requires very few, possibly even only one example. It appears, that the brain regions involved in processing this field have been pre wired to the regions they depend on. So once a new fact needs to be learned, this pre wiring is used to speed up the training of the neurons involved in processing this new fact.
Unsupervised learning is an old and well-understood problem in machine learning; LeCun's choice to replace it as the star in his cake analogy is not something he should take lightly! If you dive into the definition of self-supervised learning, you'll begin to see that it's really just an approach to unsupervised learning. Since many of the breakthroughs in machine learning this decade have been based on supervised learning techniques, successes in unsupervised problems tend to emerge when researchers re-frame an unsupervised problem as a supervised problem. Specifically, in self-supervised learning, we find a clever way to generate labels without human annotators. An easy example is a technique called next-step prediction.
Supervised learning – A blessing we have in this machines era. It helps to depict inputs to outputs. It uses labelled training data to deduce a function which has a set of training examples. The majority of practical machine learning uses supervised learning as on date. AILabPage defines Machine Learning as "A focal point where business, data and experience meets emerging technology and decides to work together".
As a member of a research group involved in computer vision, I wanted to write this short article to briefly present what we call "Zero-shot learning" (ZSL), an interesting variant of transfer learning, and the current research related to it. Today, many machine learning methods focus on classifying instances whose classes have already been seen in training. Concretely, many applications require classifying instances whose classes have not been seen before. Zero-shot learning is a promising learning method, in which the classes covered by training instances and the classes we aim to classify are disjoint. In other words, Zero-shot learning is about leveraging supervised learning with no additional training data.
Neural Structured Learning (NSL) is a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation. Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Therefore, leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adding adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model's prediction or classification.
We are excited to introduce Neural Structured Learning in TensorFlow, an easy-to-use framework that both novice and advanced developers can use for training neural networks with structured signals. Neural Structured Learning (NSL) can be applied to construct accurate and robust models for vision, language understanding, and prediction in general. Many machine learning tasks benefit from using structured data which contains rich relational information among the samples. For example, modeling citation networks, Knowledge Graph inference and reasoning on linguistic structure of sentences, and learning molecular fingerprints all require a model to learn from structured inputs, as opposed to just individual samples. These structures can be explicitly given (e.g., as a graph), or implicitly inferred (e.g., as an adversarial example).
Google today introduced Neural Structured Learning (NSL), an open source framework that uses the Neural Graph Learning method for training neural networks with graphs and structured data. NSL works with with the TensorFlow machine learning platform and is made to work for both experienced and inexperienced machine learning practitioners. NSL can make models for computer vision, perform NLP, and run predictions from graphical datasets like medical records or knowledge graphs. "Leveraging structured signals during training allows developers to achieve higher model accuracy, particularly when the amount of labeled data is relatively small," TensorFlow engineers said in a blog post today. "Training with structured signals also leads to more robust models. These techniques have been widely used in Google for improving model performance, such as learning image semantic embedding."
As you might know, supervised machine learning is one of the most commonly used and successful types of machine learning. In this article, we will describe supervised learning in more detail and explain several popular supervised learning algorithms. Remember that supervised learning is used whenever we want to predict a certain outcome from a given input, and we have examples of input/output pairs. We build a machine learning model from these input/output pairs, which comprise our training set. Our goal is to make accurate predictions for new, never-before-seen data. Supervised learning often requires human effort to build the training set, but afterwards automates and often speeds up an otherwise laborious or infeasible task. There are two major types of supervised machine learning problems, called classification and regression. In classification, the goal is to predict a class label, which is a choice from a predefined list of possibilities.
At Hugging Face, we experienced first-hand the growing popularity of these models as our NLP library -- which encapsulates most of them -- got installed more than 400,000 times in just a few months. However, as these models were reaching a larger NLP community, an important and challenging question started to emerge. How should we put these monsters in production? How can we use such large models under low latency constraints? Do we need (costly) GPU servers to serve at scale?