While many approaches exist in the literature to learn representations for data collections in multiple modalities, the generalizability of the learnt representations to previously unseen data is a largely overlooked subject. In this work, we first present a theoretical analysis of learning multi-modal nonlinear embeddings in a supervised setting. Our performance bounds indicate that for successful generalization in multi-modal classification and retrieval problems, the regularity of the interpolation functions extending the embedding to the whole data space is as important as the between-class separation and cross-modal alignment criteria. We then propose a multi-modal nonlinear representation learning algorithm that is motivated by these theoretical findings, where the embeddings of the training samples are optimized jointly with the Lipschitz regularity of the interpolators. Experimental comparison to recent multi-modal and single-modal learning algorithms suggests that the proposed method yields promising performance in multi-modal image classification and cross-modal image-text retrieval applications.

Then this course is for you! This course is designed in a very simple and easily understandable content. You might have seen lots of buzz on deep learning and you want to figure out where to start and explore. This course is designed exactly for people like you! If basics are strong, we can do bigger things with ease.

A virtual edition of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) conference was held on 9-13 May. Videos of the talks are now available for public viewing, and you can also see the sessions from the various workshops. Alison is interested in how cities work and builds spatial agent-based models (ABMs) to study how people move around and how behaviour plays out in space and time. There are a number of challenges with these kinds of models and they need to be really robust if they are to be adopted by policy makers. So, why should we be interested in modelling cities?

Deliveroo and EduMe today announced an exclusive new global partnership that will drive the success of the food delivery giant with effective onboarding, training and continuous learning by using EduMe's platform. The initiative is being rolled out to Deliveroo's entire global network of riders. It will take advantage of EduMe's experience as the training provider of choice by other leading technology companies. This will help facilitate effective onboarding at scale for new riders. Furthermore, an integration with hiring platform Fountain will be leveraged to present a seamless engagement and onboarding experience for new riders.

It's been two years since Mckinsey invented the term analytics translator, called it the'new must-have role' and predicted we'd need around 5 million of them. For the past ten years, we've struggled with the ambiguous title'data scientist', then'citizen data scientist'. Although I've seen many'data scientists' change their Linkedin titles to'analytics translator', the problem remains that no one knows what'analytics translator' really means. Mckinsey seems to have slipped this term into a Harvard Business Review article, and it has somehow taken root. What's more, people seem truly excited by the term.

Having the right inductive biases can be crucial in many tasks or scenarios where data or computing resources are a limiting factor, or where training data is not perfectly representative of the conditions at test time. However, defining, designing and efficiently adapting inductive biases is not necessarily straightforward. In this paper, we explore the power of knowledge distillation for transferring the effect of inductive biases from one model to another. We consider families of models with different inductive biases, LSTMs vs. Transformers and CNNs vs. MLPs, in the context of tasks and scenarios where having the right inductive biases is critical. We study how the effect of inductive biases is transferred through knowledge distillation, in terms of not only performance but also different aspects of converged solutions.

We have developed an AI-powered socio-technical system for making online learning in higher education more accessible, affordable and achievable. In particular, we have developed four novel and intertwined AI technologies: (1) VERA, a virtual experimentation research assistant for supporting inquiry-based learning of scientific knowledge, (2) Jill Watson Q&A, a virtual teaching assistant for answering questions based on educational documents including the VERA user reference guide, (3) Jill Watson SA, a virtual social agent that promotes online interactions, and (4) Agent Smith, that helps generate a Jill Watson Q&A agent for new documents such as class syllabi. The results are positive: (i) VERA enhances ecological knowledge and is freely available online; (ii) Jill Watson Q&A has been used by >4,000 students in >12 online classes and saved teachers >500 hours of work; (iii) Jill Q&A and Jill Watson SA promote learner engagement, interaction, and community; and (iv). Agent Smith helps generate Jill Watson Q&A for a new syllabus within ~25 hours. Put together, these innovative technologies help make online learning simultaneously more accessible (by making materials available online), affordable (by saving teacher time), and achievable (by providing learning assistance and fostering student engagement).

Learning classifier systems (LCSs) originated from cognitive-science research but migrated such that LCS became powerful classification techniques. Modern LCSs can be used to extract building blocks of knowledge to solve more difficult problems in the same or a related domain. Recent works on LCSs showed that the knowledge reuse through the adoption of Code Fragments, GP-like tree-based programs, into LCSs could provide advances in scaling. However, since solving hard problems often requires constructing high-level building blocks, which also results in an intractable search space, a limit of scaling will eventually be reached. Inspired by human problem-solving abilities, XCSCF* can reuse learned knowledge and learned functionality to scale to complex problems by transferring them from simpler problems using layered learning. However, this method was unrefined and suited to only the Multiplexer problem domain. In this paper, we propose improvements to XCSCF* to enable it to be robust across multiple problem domains. This is demonstrated on the benchmarks Multiplexer, Carry-one, Majority-on, and Even-parity domains. The required base axioms necessary for learning are proposed, methods for transfer learning in LCSs developed and learning recast as a decomposition into a series of subordinate problems. Results show that from a conventional tabula rasa, with only a vague notion of what subordinate problems might be relevant, it is possible to capture the general logic behind the tested domains, so the advanced system is capable of solving any individual n-bit Multiplexer, n-bit Carry-one, n-bit Majority-on, or n-bit Even-parity problem.

We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environment reward signals. The classical inverse reinforcement learning (IRL) solution involves bi-level optimization and is of high computational cost. Recent generative adversarial methods formulate the IL problem as occupancy measure matching, which, however, suffer from the notorious training instability and mode-dropping problems. Inspired by recent progress in energy-based model (EBM), in this paper, we propose a novel IL framework named Energy-Based Imitation Learning (EBIL), solving the IL problem via directly estimating the expert energy as the surrogate reward function through score matching. EBIL combines the idea of both EBM and occupancy measure matching, which enjoys: (1) high model flexibility for expert policy distribution estimation; (2) efficient computation that avoids the previous alternate training fashion. Though motivated by matching the policy between the expert and the agent, we surprisingly find a nontrivial connection between EBIL and Max-Entropy IRL (MaxEnt IRL) approaches, and further show that EBIL can be seen as a simpler and more efficient solution of MaxEnt IRL, which support flexible and general candidates on training the expert's EBM. Extensive experiments show that EBIL can always achieve comparable or better performance against SoTA IL methods.

Measures for evaluation of model performance play an important role in Machine Learning. However, the most common performance measures share several limitations. The difference in performance for two models has no probabilistic interpretation and there is no reference point to indicate whether they represent a significant improvement. What is more, it makes no sense to compare such differences between data sets. In this article, we introduce a new meta-measure for performance assessment named Elo-based Predictive Power (EPP). The differences in EPP scores have probabilistic interpretation and can be directly compared between data sets. We prove the mathematical properties of EPP and support them with empirical results of a large scale benchmark on 30 classification data sets. Finally, we show applications of EPP to the selected meta-learning problems and challenges beyond ML benchmarks.