Incorporating prior knowledge into deep neural networks without handcrafted features

Deep learning (DL) is currently the largest area of research within artificial intelligence (AI). This success can largely be attributed to the data-driven nature of the DL algorithms themselves: unlike previous approaches in AI which required handcrafting and significant human intervention, DL models can be implemented and trained with little to no human involvement. The lack of handcrafting, however, can be a two-edged sword. DL algorithms are notorious for producing uninterpretable features, generalising badly to new tasks and relying on extraordinarily large datasets for training. In this thesis, on the assumption that these shortcomings are symptoms of the under-constrained training setup of deep networks, we address the question of how to incorporate knowledge into DL algorithms without reverting to complete handcrafting in order to train more data efficient algorithms. % In this thesis we consider different alternatives to this problem. We start by motivating this line of work with an example of a DL architecture which, inspired by symbolic AI, aims at extracting symbols from a simple environment and using those for quickly learning downstream tasks. Our proof-of-concept model shows that it is possible to address some of the data efficiency issues as well as obtaining more interpretable representations by reasoning at this higher level of abstraction. Our second approach for data-efficiency is based on pre-training: the idea is to pre-train some parts of the DL network on a different, but related, task to first learn useful feature extractors. For our experiments we pre-train the encoder of a reinforcement learning agent on a 3D scene prediction task and then use the features produced by the encoder to train a simulated robot arm on a reaching task. Crucially, unlike previous approaches that could only learn from fixed view-points, we are able to train an agent using observations captured from randomly changing positions around the robot arm, without having to train a separate policy for each observation position. Lastly, we focus on how to build in prior knowledge through the choice of dataset. To this end, instead of training DL models on a single dataset, we train them on a distribution over datasets that captures the space of tasks we are interested in. This training regime produces models that can flexibly adapt to any dataset within the distribution at test time. Crucially they only need a small number of observations in order to adapt their predictions, thus addressing the data-efficiency challenge at test time. We call this family of meta-learning models for few-shot prediction Neural Processes (NPs). In addition to successfully learning how to carry out few-shot regression and classification, NPs produce uncertainty estimates and can generate coherent samples at arbitrary resolutions.Open Acces

Open-ended Learning in Symmetric Zero-sum Games

Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'. If the game is approximately transitive, then self-play generates sequences of agents of increasing strength. However, nontransitive games, such as rock-paper-scissors, can exhibit strategic cycles, and there is no longer a clear objective -- we want agents to increase in strength, but against whom is unclear. In this paper, we introduce a geometric framework for formulating agent objectives in zero-sum games, in order to construct adaptive sequences of objectives that yield open-ended learning. The framework allows us to reason about population performance in nontransitive games, and enables the development of a new algorithm (rectified Nash response, PSRO_rN) that uses game-theoretic niching to construct diverse populations of effective agents, producing a stronger set of agents than existing algorithms. We apply PSRO_rN to two highly nontransitive resource allocation games and find that PSRO_rN consistently outperforms the existing alternatives.Comment: ICML 2019, final versio

Otra forma de pensar el campo de la comunicación: una aproximación a la metodología artística aplicada a las disciplinas de la comunicación

La investigación artística y el debate que se ha desarrollado en los últimos años tiene un amplio calado en tanto establecen o ponen en duda una serie de aspectos metodológicos (que afectan a todas las ciencias sociales), hasta no hace mucho tomados como garantía. Las comparaciones o diferencias entre ciencia y arte con los esquemas propuestos tradicionalmente para separar estas prácticas de conocimiento son un tema frecuentemente debatido dentro de la reflexión sobre las discrepancias entre la creación artística y la investigación en ciencias puras. En los últimos años, sin embargo, se ha asistido a un esfuerzo de acercamiento entre arte y ciencia, aunque no sin eliminar la tensión epistemológica existente. Tradicionalmente, lo científico se define por su carácter metodológico, alejado de la reflexión y de la subjetividad; mientras que el arte pone el acento en el proceso reflexivo y en la mirada del artista no como un componente más, sino como el más importante

A Neural Architecture for Designing Truthful and Efficient Auctions

Auctions are protocols to allocate goods to buyers who have preferences over them, and collect payments in return. Economists have invested significant effort in designing auction rules that result in allocations of the goods that are desirable for the group as a whole. However, for settings where participants' valuations of the items on sale are their private information, the rules of the auction must deter buyers from misreporting their preferences, so as to maximize their own utility, since misreported preferences hinder the ability for the auctioneer to allocate goods to those who want them most. Manual auction design has yielded excellent mechanisms for specific settings, but requires significant effort when tackling new domains. We propose a deep learning based approach to automatically design auctions in a wide variety of domains, shifting the design work from human to machine. We assume that participants' valuations for the items for sale are independently sampled from an unknown but fixed distribution. Our system receives a data-set consisting of such valuation samples, and outputs an auction rule encoding the desired incentive structure. We focus on producing truthful and efficient auctions that minimize the economic burden on participants. We evaluate the auctions designed by our framework on well-studied domains, such as multi-unit and combinatorial auctions, showing that they outperform known auction designs in terms of the economic burden placed on participants