213 research outputs found
Neural Circuit Inference from Function to Structure
Advances in technology are opening new windows on the structural connectivity and functional dynamics of brain circuits. Quantitative frameworks are needed that integrate these data from anatomy and physiology. Here, we present a modeling approach that creates such a link. The goal is to infer the structure of a neural circuit from sparse neural recordings, using partial knowledge of its anatomy as a regularizing constraint. We recorded visual responses from the output neurons of the retina, the ganglion cells. We then generated a systematic sequence of circuit models that represents retinal neurons and connections and fitted them to the experimental data. The optimal models faithfully recapitulated the ganglion cell outputs. More importantly, they made predictions about dynamics and connectivity among unobserved neurons internal to the circuit, and these were subsequently confirmed by experiment. This circuit inference framework promises to facilitate the integration and understanding of big data in neuroscience
Regularized Evolution for Image Classifier Architecture Search
The effort devoted to hand-crafting neural network image classifiers has
motivated the use of architecture search to discover them automatically.
Although evolutionary algorithms have been repeatedly applied to neural network
topologies, the image classifiers thus discovered have remained inferior to
human-crafted ones. Here, we evolve an image classifier---AmoebaNet-A---that
surpasses hand-designs for the first time. To do this, we modify the tournament
selection evolutionary algorithm by introducing an age property to favor the
younger genotypes. Matching size, AmoebaNet-A has comparable accuracy to
current state-of-the-art ImageNet models discovered with more complex
architecture-search methods. Scaled to larger size, AmoebaNet-A sets a new
state-of-the-art 83.9% / 96.6% top-5 ImageNet accuracy. In a controlled
comparison against a well known reinforcement learning algorithm, we give
evidence that evolution can obtain results faster with the same hardware,
especially at the earlier stages of the search. This is relevant when fewer
compute resources are available. Evolution is, thus, a simple method to
effectively discover high-quality architectures.Comment: Accepted for publication at AAAI 2019, the Thirty-Third AAAI
Conference on Artificial Intelligenc
Population genetic analyses of the Powerplex Fusion kit in a cosmopolitan sample of Chubut province (Patagonia Argentina)
Allele frequencies and forensic parameters for 22 autosomal STR loci and DYS391 locus included in the PowerPlex® Fusion System kit were estimated in a sample of 770 unrelated individuals from Chubut province, southern Patagonia. No significant deviations from Hardy-Weinberg equilibrium were observed after Bonferroni?s correction. The combined power of discrimination and the combined probability of exclusion were >0.999999 and 0.999984, respectively. Comparisons with other worldwide populations were performed. The MDS obtained show a close biological relation between Chubut and Chile. The estimated interethnic admixture supports a high Native American contribution (46%) in the population sample of Chubut. These results enlarge the Argentine databases of autosomal STR and would provide a valuable contribution for identification tests and population genetic studies.Fil: Parolin, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Real, Luciano Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Martinazzo Giménez, Liza Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Basso, Nestor Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; Argentin
Multi-objective evolution for Generalizable Policy Gradient Algorithms
Performance, generalizability, and stability are three Reinforcement Learning
(RL) challenges relevant to many practical applications in which they present
themselves in combination. Still, state-of-the-art RL algorithms fall short
when addressing multiple RL objectives simultaneously and current human-driven
design practices might not be well-suited for multi-objective RL. In this paper
we present MetaPG, an evolutionary method that discovers new RL algorithms
represented as graphs, following a multi-objective search criteria in which
different RL objectives are encoded in separate fitness scores. Our findings
show that, when using a graph-based implementation of Soft Actor-Critic (SAC)
to initialize the population, our method is able to find new algorithms that
improve upon SAC's performance and generalizability by 3% and 17%,
respectively, and reduce instability up to 65%. In addition, we analyze the
graph structure of the best algorithms in the population and offer an
interpretation of specific elements that help trading performance for
generalizability and vice versa. We validate our findings in three different
continuous control tasks: RWRL Cartpole, RWRL Walker, and Gym Pendulum.Comment: 23 pages, 12 figures, 10 table
Evolving Reinforcement Learning Algorithms
We propose a method for meta-learning reinforcement learning algorithms by
searching over the space of computational graphs which compute the loss
function for a value-based model-free RL agent to optimize. The learned
algorithms are domain-agnostic and can generalize to new environments not seen
during training. Our method can both learn from scratch and bootstrap off known
existing algorithms, like DQN, enabling interpretable modifications which
improve performance. Learning from scratch on simple classical control and
gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm.
Bootstrapped from DQN, we highlight two learned algorithms which obtain good
generalization performance over other classical control tasks, gridworld type
tasks, and Atari games. The analysis of the learned algorithm behavior shows
resemblance to recently proposed RL algorithms that address overestimation in
value-based methods.Comment: ICLR 2021 Oral. See project website at
https://sites.google.com/view/evolvingr
El papel del valor de la marca propia en la composición del surtido del minorista: su influencia sobre la lealtad al establecimiento
El objetivo del presente trabajo es conocer la influencia del valor de la MdD, en diferentes tipos de surtido, sobre la lealtad de los consumidores hacia el establecimiento, tanto para una categoría de producto como para la cesta de la compra. Para ello, consideramos surtidos con diferente tamaño y estructura (solo MdD y mixtos) y desarrollamos un experimento online con una muestra de 1.400 consumidores en España. A partir de un análisis ANOVA, los resultados solo muestran diferencias significativas en surtidos mixtos donde la MF de alto valor tiene poco peso, no encontrándose diferencias significativas en surtidos con solo MdD, ni en surtidos mixtos con mayor proporción de MF de alto valor. Estos resultados sugieren diferentes recomendaciones para los minoristas, dependiendo del tipo de surtido por el que optenThe objective of this work is to know the influence of the PL equity, in different structures of the assortment, on the consumer’s loyalty towards the store, for both a product category and the shopping basket. Thus, we consider assortments with different sizes and composition (PL-only and mixed) and develop an online experiment with a sample of 1,400 consumers in Spain. Through an ANOVA analysis, the results only show significant differences in mixed assortments where NBs high equity represent a low ratio, not finding significant differences in PL-only assortments, nor in mixed assortments with a higher ratio of NBs high equity. These results suggest different suggestions for retailers, depending on the type of assortmen
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