Continual learning (CL) learns a sequence of tasks incrementally with the goal of achieving two main objectives: overcoming catastrophic forgetting (CF) and encouraging knowledge transfer (KT) across tasks. However, most existing techniques focus only on overcoming CF and have no mechanism to encourage KT, and thus do not do well in KT. Although several papers have tried to deal with both CF and KT, our experiments show that they suffer from serious CF when the tasks do not have much shared knowledge. Another observation is that most current CL methods do not use pre-trained models, but it has been shown that such models can significantly improve the end task performance. For example, in natural language processing, fine-tuning a BERT-like pre-trained language model is one of the most effective approaches. However, for CL, this approach suffers from serious CF. An interesting question is how to make the best use of pre-trained models for CL. This paper proposes a novel model called CTR to solve these problems. Our experimental results demonstrate the effectiveness of CTR.

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robustness analysis results are available publicly on the NL-Augmenter repository (\url{https://github.com/GEM-benchmark/NL-Augmenter}).

Description Deep Learning: Recurrent Neural Networks in Python is a Deep Learning and Artificial Intelligence training course focusing on the development of recursive neural networks (RNNs) published by Yodemi Academy. Among the most important topics covered in this course are GRU architecture, short-term long-term memory architecture (LSTM), time series forecasting, stock price forecasting, natural language processing (NLP) with artificial intelligence, and… Cited. At the beginning of this training course, you will get acquainted with the famous deep learning architectures in a brief and at the same time practical way. Recursive neural networks, or RNNs for short, are one of the most popular classes in the development of artificial intelligence-based systems used in modeling operations sequences. Among the most important applications of the RNN network are time series forecasting of various events, stock price forecasting, natural language processing, and so on.

KNN can easily be mapped to our real lives. If you want to learn about a person, of whom you have no information, you might like to find out about his close friends and the circles he moves in and gain access to his/her information!

Research in mathematics is a deeply imaginative and intuitive process. This might come as a surprise for those who are still recovering from high-school algebra. What does the world look like at the quantum scale? What shape would our universe take if we were as large as a galaxy? What would it be like to live in six or even 60 dimensions?

Mathematics for Machine Learning:Mathematics is essential to understanding the notations of machine learning. It also provides the basics to solve machine learning-related problems. This is a specialization on Coursera to develop mathematical intuition by Imperial College London named Mathematics for Machine Learning. This specialization contains three courses containing Linear Algebra, Calculus, and Principal Component Analysis. Python for everybody: The specialization Python for everybody contains five courses to learn python.

This article is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. It is an introductory article suitable for beginners with no previous knowledge of machine learning or artificial intelligence (AI). This is the first article on my series "Machine Learning with Python". I will introduce the fundamental concepts of Machine Learning, its applications and how to set up our working environment as well as a hands on practices on a simple project. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed.

Geneva, Switzerland, Dec 1, 2021 – STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across the spectrum of electronics applications, today announced the availability of Version 3 of NanoEdge AI Studio, the first major upgrade of the software tool for machine-learning applications that ST acquired with Cartesiam earlier this year. The new version of NanoEdge AI Studio comes as the shift of AI capabilities from the cloud to the edge offers manufacturers phenomenal potential to fundamentally improve industrial processes, optimize maintenance costs, and deliver innovative functions in equipment that can sense, process data, and act locally to improve latency and information security. Applications include connected devices, household appliances, and industrial automation. This new release also includes prediction capabilities such as regression and outliers libraries. The tool makes it easier for users to integrate such cutting-edge machine-learning capabilities quickly, easily, and cost-effectively into their equipment.

Existing online multi-label classification works cannot well handle the online label thresholding problem and lack the regret analysis for their online algorithms. This paper proposes a novel framework of adaptive label thresholding algorithms for online multi-label classification, with the aim to overcome the drawbacks of existing methods. The key feature of our framework is that both scoring and thresholding models are included as important components of the online multi-label classifier and are incorporated into one online optimization problem. Further, in order to establish the relationship between scoring and thresholding models, a novel multi-label classification loss function is derived, which measures to what an extent the multi-label classifier can distinguish between relevant labels and irrelevant ones for an incoming instance. Based on this new framework and loss function, we present a first-order linear algorithm and a second-order one, which both enjoy closed form update, but rely on different techniques for updating the multi-label classifier. Both algorithms are proved to achieve a sub-linear regret. Using Mercer kernels, our first-order algorithm has been extended to deal with nonlinear multi-label prediction tasks. Experiments show the advantage of our linear and nonlinear algorithms, in terms of various multi-label performance metrics.

Natural Language Inference (NLI) is considered a representative task to test natural language understanding (NLU). In this work, we propose an extensible framework to collectively yet categorically test diverse Logical reasoning capabilities required for NLI (and by extension, NLU). Motivated by behavioral testing, we create a semi-synthetic large test-bench (363 templates, 363k examples) and an associated framework that offers following utilities: 1) individually test and analyze reasoning capabilities along 17 reasoning dimensions (including pragmatic reasoning), 2) design experiments to study cross-capability information content (leave one out or bring one in); and 3) the synthetic nature enable us to control for artifacts and biases. The inherited power of automated test case instantiation from free-form natural language templates (using CheckList), and a well-defined taxonomy of capabilities enable us to extend to (cognitively) harder test cases while varying the complexity of natural language. Through our analysis of state-of-the-art NLI systems, we observe that our benchmark is indeed hard (and non-trivial even with training on additional resources). Some capabilities stand out as harder. Further fine-grained analysis and fine-tuning experiments reveal more insights about these capabilities and the models -- supporting and extending previous observations. Towards the end we also perform an user-study, to investigate whether behavioral information can be utilised to generalize much better for some models compared to others.