Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide insights into how to improve them. Current inspection methods, however, fail to link modules to their functionality. In this paper, we present a novel method based on learning binary weight masks to identify individual weights and subnets responsible for specific functions. Using this powerful tool, we contribute an extensive study of emerging modularity in NNs that covers several standard architectures and datasets. We demonstrate how common NNs fail to reuse submodules and offer new insights into the related issue of systematic generalization on language tasks.

Most of existing deep learning models rely on excessive amounts of labeled training data in order to achieve state-of-the-art results, even though these data can be hard or costly to get in practice. One attractive alternative is to learn with little supervision, commonly referred to as few-shot learning (FSL), and, in particular, meta-learning that learns to learn with few data from related tasks. Despite the practical success of meta-learning, many of its algorithmic solutions proposed in the literature are based on sound intuitions, but lack a solid theoretical analysis of the expected performance on the test task. In this paper, we review the recent advances in meta-learning theory and show how they can be used in practice both to better understand the behavior of popular meta-learning algorithms and to improve their generalization capacity. This latter is achieved by integrating the theoretical assumptions ensuring efficient meta-learning in the form of regularization terms into several popular meta-learning algorithms for which we provide a large study of their behavior on classic few-shot classification benchmarks. To the best of our knowledge, this is the first contribution that puts the most recent learning bounds of meta-learning theory into practice for the popular task of few-shot classification.

There was a time when Artificial Intelligence (AI) was often portrayed as robots. Machines that exhibited human-like characteristics (learning and decision making) with an artificial brain. Today, AI encompasses anything and everything. Be it vehicles, entertainment, corporations, smart homes, google search algorithms, education, law, or medical services, AI has transformed all the sectors for the better. Until today, artificial intelligence has outperformed humans in specific tasks. The worldwide AI market is expected to grow by $120 billion by the end of 2025.

The COVID-19 crisis has forced millions of teachers around the world to rapidly learn how to use technology to effectively support student learning and assessment, stay connected with their students, experiment with teaching models, and reduce the workload so they can focus on teaching. There are many promising solutions that are helping teachers become more effective, including new technologies such as machine learning (ML), artificial intelligence (AI) and optimised workflows. For example, Revisely is an education company that helps teachers give better feedback on students' writing assignments, such as essays and papers. It saves teachers time by offering built-in comment sets and doing a plagiarism check on student work, among other features. In addition, teachers can track the performance of students on all assignments throughout their learning journey.

The University of Helsinki (https://www.helsinki.fi/en) is an international scientific community of 40,000 students and researchers. It is one of the leading multidisciplinary research universities in Europe and ranks among the top 100 international universities in the world. The research themes of the Department cover machine learning and algorithms, computer networks and distributed systems, software systems and bioinformatics. A fixed-term postdoctoral researcher position is available in the research group of Professor Aapo Hyvärinen at the Department of Computer Science, University of Helsinki. The project is broadly defined as unsupervised deep learning, including frameworks such as nonlinear independent component analysis, energy-based modelling, and causal discovery, which are at the cutting edge of unsupervised deep learning.

Hi! I am working on a CNN project and I have some confusions. I didn't understand the importance of using bias during training a model. What are the effects of bias usage? And if we don't use bias what are the challenges that we are going to face with?

As a grad student working on artificial intelligence, Mohamed Abdalla could probably walk into a number of well-paid industry jobs. Instead, he wants to draw attention to how Big Tech's big bucks may be warping the perspective of his field. Abdalla, who is finishing his PhD at the University of Toronto, has coauthored a paper highlighting the number of top AI researchers--including those who study the ethical challenges raised by the technology--who receive funding from tech companies. That can be a particular problem, he says, when corporate AI systems raise ethical issues, such as algorithmic bias, military use, or questions about the fairness and accuracy of face recognition programs. Abdalla found that more than half of tenure-track AI faculty at four prominent universities who disclose their funding sources have received some sort of backing from Big Tech.

Machine learning is one of the buzzwords recently and this can be attributed to its predictive behavior. SLA Institute is one of the Best Machine Learning Training Institutes in Chennai. It refers to how a computer is taught to solve complex problems without any explicit programming. The key is that it works in an optimum manner from experience. Machine learning is associated with computational statistics that concentrate on making a prediction with the help of computers.

Manifold learning is a fundamental problem in machine learning with numerous applications. Most of the existing methods directly learn the low-dimensional embedding of the data in some high-dimensional space, and usually lack the flexibility of being directly applicable to down-stream applications. In this paper, we propose the concept of implicit manifold learning, where manifold information is implicitly obtained by learning the associated heat kernel. A heat kernel is the solution of the corresponding heat equation, which describes how "heat" transfers on the manifold, thus containing ample geometric information of the manifold. We provide both practical algorithm and theoretical analysis of our framework. The learned heat kernel can be applied to various kernel-based machine learning models, including deep generative models (DGM) for data generation and Stein Variational Gradient Descent for Bayesian inference. Extensive experiments show that our framework can achieve state-of-the-art results compared to existing methods for the two tasks.

There has been an increasingly popular trend in Universities for curriculum transformation to make teaching more interactive and suitable for online courses. An increase in the popularity of online courses would result in an increase in the number of course-related queries for academics. This, coupled with the fact that if lectures were delivered in a video on demand format, there would be no fixed time where the majority of students could ask questions. When questions are asked in a lecture there is a negligible chance of having similar questions repeatedly, but asynchronously this is more likely. In order to reduce the time spent on answering each individual question, clustering them is an ideal choice. There are different unsupervised models fit for text clustering, of which the Latent Dirichlet Allocation model is the most commonly used. We use the Hierarchical Dirichlet Process to determine an optimal topic number input for our LDA model runs. Due to the probabilistic nature of these topic models, the outputs of them vary for different runs. The general trend we found is that not all the topics were being used for clustering on the first run of the LDA model, which results in a less effective clustering. To tackle probabilistic output, we recursively use the LDA model on the effective topics being used until we obtain an efficiency ratio of 1. Through our experimental results we also establish a reasoning on how Zeno's paradox is avoided.