The normalized backpropagation and some experiments on speech recognition

In the paper we present the theoretical development of the normalized backpropagation, and we compare it with other algorithms that have been presented in the literature. The algorithm that we propose is based on the idea of normalizing the adaptation step in the gradient search by the variance of the input. This algorithm is simple and gives good results in comparison with other algorithms that accelerate the learning and has the additional advantage that the parameters are calculated by the algorithm, so the user does not have to make several trials in order to trim the adaptation step and the momentum until the best combination is found. The task which we have designed in order to compare the algorithms is the recognition of digits in the Catalan language, with a data base of 1000 items, spoken by 10 speakers. The algorithms that we have compared with the normalized back propagation are: D.E.Rumelhart and J .L. McCielland, Franzini, Suddhard, Fahlman, Monte.Peer ReviewedPostprint (published version

Convolutional Ladder Networks for Legal {NERC} and the Impact of Unsupervised Data in Better Generalizations

International audienceIn this paper we adapt the semi-supervised deep learning architecture known as "Convolutional Ladder Networks", from the domain of computer vision, and explore how well it works for a semi-supervised Named Entity Recognition and Classification task with legal data. The idea of exploring a semi-supervised technique is to assess the impact of large amounts of unsupervised data (cheap to obtain) in specific tasks that have little annotated data, in order to develop robust models that are less prone to overfitting. In order to achieve this, first we must check the impact on a task that is easier to measure. We are presenting some preliminary experiments, however, the results obtained foster further research in the topic

MERL: Multi-Head Reinforcement Learning

International audienceA common challenge in reinforcement learning is how to convert the agent's interactions with an environment into fast and robust learning. For instance, earlier work makes use of domain knowledge to improve existing reinforcement learning algorithms in complex tasks. While promising, previously acquired knowledge is often costly and challenging to scale up. Instead, we decide to consider problem knowledge with signals from quantities relevant to solve any task, e.g., self-performance assessment and accurate expectations. $\mathcal{V}^{ex}$ is such a quantity. It is the fraction of variance explained by the value function $V$ and measures the discrepancy between $V$ and the returns. Taking advantage of $\mathcal{V}^{ex}$, we propose MERL, a general framework for structuring reinforcement learning by injecting problem knowledge into policy gradient updates. As a result, the agent is not only optimized for a reward but learns using problem-focused quantities provided by MERL, applicable out-of-the-box to any task. In this paper: (a) We introduce and define MERL, the multi-head reinforcement learning framework we use throughout this work. (b) We conduct experiments across a variety of standard benchmark environments, including 9 continuous control tasks, where results show improved performance. (c) We demonstrate that MERL also improves transfer learning on a set of challenging pixel-based tasks. (d) We ponder how MERL tackles the problem of reward sparsity and better conditions the feature space of reinforcement learning agents