Learning Visual Reasoning Without Strong Priors

Achieving artificial visual reasoning - the ability to answer image-related questions which require a multi-step, high-level process - is an important step towards artificial general intelligence. This multi-modal task requires learning a question-dependent, structured reasoning process over images from language. Standard deep learning approaches tend to exploit biases in the data rather than learn this underlying structure, while leading methods learn to visually reason successfully but are hand-crafted for reasoning. We show that a general-purpose, Conditional Batch Normalization approach achieves state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4% error rate. We outperform the next best end-to-end method (4.5%) and even methods that use extra supervision (3.1%). We probe our model to shed light on how it reasons, showing it has learned a question-dependent, multi-step process. Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.Comment: Full AAAI 2018 paper is at arXiv:1709.07871. Presented at ICML 2017's Machine Learning in Speech and Language Processing Workshop. Code is at http://github.com/ethanjperez/fil

FiLM: Visual Reasoning with a General Conditioning Layer

We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network computation via a simple, feature-wise affine transformation based on conditioning information. We show that FiLM layers are highly effective for visual reasoning - answering image-related questions which require a multi-step, high-level process - a task which has proven difficult for standard deep learning methods that do not explicitly model reasoning. Specifically, we show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are robust to ablations and architectural modifications, and 4) generalize well to challenging, new data from few examples or even zero-shot.Comment: AAAI 2018. Code available at http://github.com/ethanjperez/film . Extends arXiv:1707.0301

On the scaling limits of Galton Watson processes in varying environment

We establish a general sufficient condition for a sequence of Galton Watson branching processes in varying environment to converge weakly. This condition extends previous results by allowing offspring distributions to have infinite variance, which leads to a new and subtle phenomena when the process goes through a bottleneck and also in terms of time scales. Our assumptions are stated in terms of pointwise convergence of a triplet of two real-valued functions and a measure. The limiting process is characterized by a backwards ordinary differential equation satisfied by its Laplace exponent, which generalizes the branching equation satisfied by continuous state branching processes. Several examples are discussed, namely branching processes in random environment, Feller diffusion in varying environment and branching processes with catastrophes.Comment: Tightness is now proved in a separate paper (arXiv 1409.5215

Inferring bounded evolution in phenotypic characters from phylogenetic comparative data

Our understanding of phenotypic evolution over macroevolutionary timescales largely relies on the use of stochastic models for the evolution of continuous traits over phylogenies. The two most widely used models, Brownian motion and the Ornstein–Uhlenbeck (OU) process, differ in that the latter includes constraints on the variance that a trait can attain in a clade. The OU model explicitly models adaptive evolution toward a trait optimum and has thus been widely used to demonstrate the existence of stabilizing selection on a trait. Here we introduce a new model for the evolution of continuous characters on phylogenies: Brownian motion between two reflective bounds, or Bounded Brownian Motion (BBM). This process also models evolutionary constraints, but of a very different kind. We provide analytical expressions for the likelihood of BBM and present a method to calculate the likelihood numerically, as well as the associated R code. Numerical simulations show that BBM achieves good performance: parameter estimation is generally accurate but more importantly BBM can be very easily discriminated from both BM and OU. We then analyze climatic niche evolution in diprotodonts and find that BBM best fits this empirical data set, suggesting that the climatic niches of diprotodonts are bounded by the climate available in Australia and the neighboring islands but probably evolved with little additional constraints. We conclude that BBM is a valuable addition to the macroevolutionary toolbox, which should enable researchers to elucidate whether the phenotypic traits they study are evolving under hard constraints between bounds

The Afghan local police – closing the security gap?

The Afghan Local Police (ALP) was designed as an international counterinsurgency programme that works by raising small, village-level defence forces from within rural Afghan communities. Despite being driven by counterinsurgency objectives – that is, seeking to defeat insurgents - its emphasis upon harnessing local populations reflects broader fashions in development and security policy circles. Such policies, in turn, are commonly seen as emerging from a body of theoretical literature that is rethinking the nature of political order in conflict-torn spaces. At face value the range of well-documented controversies surrounding the ALP suggests, however, that the practice is much more ‘messy’. Using the case study of the ALP in the district of Andar, we make two main arguments. First, the mess and ambiguity surrounding the ALP reveal a gap between objectives and practices, suggesting that interventions that work by seeking to harness the ‘local’ introduce problems that have yet to be fully recognised. Second, however, in explaining the ‘mess’ of the ALP we argue that the theoretically-driven work that is commonly taken to justify ‘bottom-up’ interventions, if taken seriously, is well-suited to understanding and even anticipating the supposedly unexpected consequences of intervenors seeking to tap local dynamics

Analyse du programme de développement durable Proambiente à Juina-MT

Rapport de terrainCe texte est le rapport d'études de l'analyse du programme de développement durable Proambiente implanté dans la commune de Juina au Mato Grosso, Brésil