Web-Scale Training for Face Identification

Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web. We study face recognition and show that three distinct properties have surprising effects on the transferability of deep convolutional networks (CNN): (1) The bottleneck of the network serves as an important transfer learning regularizer, and (2) in contrast to the common wisdom, performance saturation may exist in CNN's (as the number of training samples grows); we propose a solution for alleviating this by replacing the naive random subsampling of the training set with a bootstrapping process. Moreover, (3) we find a link between the representation norm and the ability to discriminate in a target domain, which sheds lights on how such networks represent faces. Based on these discoveries, we are able to improve face recognition accuracy on the widely used LFW benchmark, both in the verification (1:1) and identification (1:N) protocols, and directly compare, for the first time, with the state of the art Commercially-Off-The-Shelf system and show a sizable leap in performance

Innovation Systems, Radical Transformation, Step-by-Step: India in Light of China

The paper introduces a reform trajectory we call ?revolutionary incrementalism? in which partial and incremental measures add up to profound transformation. Recent advances in economic theory demonstrate that growth is not hard to start: it almost starts itself, somewhere, sometimes. But keeping it going is not easy: doing so requires attention to the context of growth binding constraints and situation-specific ways to resolve them. The same goes for institutions: it is almost always possible to find some that are working. The issue is using the ones that work to improve those that don?t. The thrust of the proposal is to rely on variation within existing institutions as the ?Archimedean lever? with which to leverage reform and change. India?s public sector record for implementing and coordinating innovation efforts can be notoriously fragmented and inefficient but there are some parts that perform better than others, and there are recognized pockets of excellence virtually within every ministry or public sector organization. The same internal diversity is even more visible in the private sector. Importantly from a policy perspective, better performing segments of public sector and better performing segments of productive sector are beginning to join forces in a variety of search ...innovation systems, heterogeneity of institutions, radical incrementalism, search networks, open economy industrial policy

Domain adaptation of weighted majority votes via perturbed variation-based self-labeling

In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thus the design of algorithms able to generalize on a new distribution, for which we have no label information. We focus on learning classification models defined as a weighted majority vote over a set of real-val ued functions. In this context, Germain et al. (2013) have shown that a measure of disagreement between these functions is crucial to control. The core of this measure is a theoretical bound--the C-bound (Lacasse et al., 2007)--which involves the disagreement and leads to a well performing majority vote learning algorithm in usual non-adaptative supervised setting: MinCq. In this work, we propose a framework to extend MinCq to a domain adaptation scenario. This procedure takes advantage of the recent perturbed variation divergence between distributions proposed by Harel and Mannor (2012). Justified by a theoretical bound on the target risk of the vote, we provide to MinCq a target sample labeled thanks to a perturbed variation-based self-labeling focused on the regions where the source and target marginals appear similar. We also study the influence of our self-labeling, from which we deduce an original process for tuning the hyperparameters. Finally, our framework called PV-MinCq shows very promising results on a rotation and translation synthetic problem

JIDT: An information-theoretic toolkit for studying the dynamics of complex systems

Complex systems are increasingly being viewed as distributed information processing systems, particularly in the domains of computational neuroscience, bioinformatics and Artificial Life. This trend has resulted in a strong uptake in the use of (Shannon) information-theoretic measures to analyse the dynamics of complex systems in these fields. We introduce the Java Information Dynamics Toolkit (JIDT): a Google code project which provides a standalone, (GNU GPL v3 licensed) open-source code implementation for empirical estimation of information-theoretic measures from time-series data. While the toolkit provides classic information-theoretic measures (e.g. entropy, mutual information, conditional mutual information), it ultimately focusses on implementing higher-level measures for information dynamics. That is, JIDT focusses on quantifying information storage, transfer and modification, and the dynamics of these operations in space and time. For this purpose, it includes implementations of the transfer entropy and active information storage, their multivariate extensions and local or pointwise variants. JIDT provides implementations for both discrete and continuous-valued data for each measure, including various types of estimator for continuous data (e.g. Gaussian, box-kernel and Kraskov-Stoegbauer-Grassberger) which can be swapped at run-time due to Java's object-oriented polymorphism. Furthermore, while written in Java, the toolkit can be used directly in MATLAB, GNU Octave, Python and other environments. We present the principles behind the code design, and provide several examples to guide users.Comment: 37 pages, 4 figure

Multi-task Deep Reinforcement Learning with PopArt

The reinforcement learning community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequential-decision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent's updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab

Hierarchical evolution of robotic controllers for complex tasks

A robótica evolucionária é uma metodologia que permite que robôs aprendam a efetuar uma tarefa através da afinação automática dos seus “cérebros” (controladores). Apesar do processo evolutivo ser das formas de aprendizagem mais radicais e abertas, a sua aplicação a tarefas de maior complexidade comportamental não é fácil. Visto que os controladores são habitualmente evoluídos através de simulação computacional, é incontornável que existam diferenças entre os sensores e atuadores reais e as suas versões simuladas. Estas diferenças impedem que os controladores evoluídos alcancem um desempenho em robôs reais equivalente ao da simulação. Nesta dissertação propomos uma abordagem para ultrapassar tanto o problema da complexidade comportamental como o problema da transferência para a realidade. Mostramos como um controlador pode ser evoluído para uma tarefa complexa através da evolução hierárquica de comportamentos. Experimentamos também combinar técnicas evolucionárias com comportamentos pré-programados. Demonstramos a nossa abordagem numa tarefa em que um robô tem que encontrar e salvar um colega. O robô começa numa sala com obstáculos e o colega está localizado num labirinto ligado à sala. Dividimos a tarefa de salvamento em diferentes sub-tarefas, evoluímos controladores para cada sub-tarefa, e combinamos os controladores resultantes através de evoluções adicionais. Testamos os controladores em simulação e comparamos o desempenho num robô real. O controlador alcançou uma taxa de sucesso superior a 90% tanto na simulação como na realidade. As contribuições principais do nosso estudo são a introdução de uma metodologia inovadora para a evolução de controladores para tarefas complexas, bem como a sua demonstração num robô real.Evolutionary robotics is a methodology that allows for robots to learn how perform a task by automatically fine-tuning their “brain” (controller). Evolution is one of the most radical and open-ended forms of learning, but it has proven difficult for tasks where complex behavior is necessary (know as the bootstrapping problem). Controllers are usually evolved through computer simulation, and differences between real sensors and actuators and their simulated implementations are unavoidable. These differences prevent evolved controllers from crossing the reality gap, that is, achieving similar performance in real robotic hardware as they do in simulation. In this dissertation, we propose an approach to overcome both the bootstrapping problem and the reality gap. We demonstrate how a controller can be evolved for a complex task through hierarchical evolution of behaviors. We further experiment with combining evolutionary techniques and preprogrammed behaviors. We demonstrate our approach in a task in which a robot has to find and rescue a teammate. The robot starts in a room with obstacles and the teammate is located in a double T-maze connected to the room. We divide the rescue task into different sub-tasks, evolve controllers for each sub-task, and then combine the resulting controllers in a bottom-up fashion through additional evolutionary runs. The controller achieved a task completion rate of more than 90% both in simulation and on real robotic hardware. The main contributions of our study are the introduction of a novel methodology for evolving controllers for complex tasks, and its demonstration on real robotic hardware