5,571 research outputs found
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
Synopsis seriation:A computer music piece made with time–frequency scattering and information geometry
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
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