On The Specialization of Neural Modules

A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of previous experiences. These models leverage compositional architectures which aim to learn specialized modules dedicated to structures in a task that can be composed to solve novel problems with similar structures. While the compositionality of these architectures is guaranteed by design, the modules specializing is not. Here we theoretically study the ability of network modules to specialize to useful structures in a dataset and achieve systematic generalization. To this end we introduce a minimal space of datasets motivated by practical systematic generalization benchmarks. From this space of datasets we present a mathematical definition of systematicity and study the learning dynamics of linear neural modules when solving components of the task. Our results shed light on the difficulty of module specialization, what is required for modules to successfully specialize, and the necessity of modular architectures to achieve systematicity. Finally, we confirm that the theoretical results in our tractable setting generalize to more complex datasets and non-linear architectures

Dynamics Generalisation in Reinforcement Learning via Adaptive Context-Aware Policies

While reinforcement learning has achieved remarkable successes in several domains, its real-world application is limited due to many methods failing to generalise to unfamiliar conditions. In this work, we consider the problem of generalising to new transition dynamics, corresponding to cases in which the environment's response to the agent's actions differs. For example, the gravitational force exerted on a robot depends on its mass and changes the robot's mobility. Consequently, in such cases, it is necessary to condition an agent's actions on extrinsic state information and pertinent contextual information reflecting how the environment responds. While the need for context-sensitive policies has been established, the manner in which context is incorporated architecturally has received less attention. Thus, in this work, we present an investigation into how context information should be incorporated into behaviour learning to improve generalisation. To this end, we introduce a neural network architecture, the Decision Adapter, which generates the weights of an adapter module and conditions the behaviour of an agent on the context information. We show that the Decision Adapter is a useful generalisation of a previously proposed architecture and empirically demonstrate that it results in superior generalisation performance compared to previous approaches in several environments. Beyond this, the Decision Adapter is more robust to irrelevant distractor variables than several alternative methods.Comment: Accepted to NeurIPS 202

Constraints on dark matter to dark radiation conversion in the late universe with DES-Y1 and external data

84siWe study a class of decaying dark matter models as a possible resolution to the observed discrepancies between early- and late-time probes of the universe. This class of models, dubbed DDM, characterizes the evolution of comoving dark matter density with two extra parameters. We investigate how DDM affects key cosmological observables such as the CMB temperature and matter power spectra. Combining 3x2pt data from Year 1 of the Dark Energy Survey,Planck-2018 CMB temperature and polarization data, Supernova (SN) Type Ia data from Pantheon, and BAO data from BOSS DR12, MGS and 6dFGS, we place new constraints on the amount of dark matter that has decayed and the rate with which it converts to dark radiation. The fraction of the decayed dark matter in units of the current amount of dark matter, $zeta$, is constrained at 68% confidence level to be <0.32 for DES-Y1 3x2pt data, <0.030 for CMB+SN+BAO data, and <0.037 for the combined dataset. The probability that the DES and CMB+SN+BAO datasets are concordant increases from 4% for the $Lambda$CDM model to 8% (less tension) for DDM. Moreover, tension in $S_8=sigma_8sqrt{Omega_m/0.3}$ between DES-Y1 3x2pt and CMB+SN+BAO is reduced from 2.3$sigma$ to 1.9$sigma$. We find no reduction in the Hubble tension when the combined data is compared to distance-ladder measurements in the DDM model. The maximum-posterior goodness-of-fit statistics of DDM and $Lambda$CDM are comparable, indicating no preference for the DDM cosmology over $Lambda$CDM....partially_openopenChen, Angela; Huterer, Dragan; Lee, Sujeong; Ferté, Agnès; Weaverdyck, Noah; Alonso Alves, Otavio; Leonard, C. Danielle; MacCrann, Niall; Raveri, Marco; Porredon, Anna; Di Valentino, Eleonora; Muir, Jessica; Lemos, Pablo; Liddle, Andrew; Blazek, Jonathan; Campos, Andresa; Cawthon, Ross; Choi, Ami; Dodelson, Scott; Elvin-Poole, Jack; Gruen, Daniel; Ross, Ashley; Secco, Lucas F.; Sevilla, Ignacio; Sheldon, Erin; Troxel, Michael A.; Zuntz, Joe; Abbott, Tim; Aguena, Michel; Allam, Sahar; Annis, James; Avila, Santiago; Bertin, Emmanuel; Bhargava, Sunayana; Bridle, Sarah; Brooks, David; Carnero Rosell, Aurelio; Carrasco Kind, Matias; Carretero, Jorge; Costanzi, Matteo; Crocce, Martin; da Costa, Luiz; Elidaiana da Silva Pereira, Maria; Davis, Tamara; Doel, Peter; Eifler, Tim; Ferrero, Ismael; Fosalba, Pablo; Frieman, Josh; Garcia-Bellido, Juan; Gaztanaga, Enrique; Gerdes, David; Gruendl, Robert; Gschwend, Julia; Gutierrez, Gaston; Hinton, Samuel; Hollowood, Devon L.; Honscheid, Klaus; Hoyle, Ben; James, David; Jarvis, Mike; Kuehn, Kyler; Lahav, Ofer; Maia, Marcio; Marshall, Jennifer; Menanteau, Felipe; Miquel, Ramon; Morgan, Robert; Palmese, Antonella; Paz-Chinchon, Francisco; Plazas Malagón, Andrés; Roodman, Aaron; Sanchez, Eusebio; Scarpine, Vic; Schubnell, Michael; Serrano, Santiago; Smith, Mathew; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; To, Chun-Hao; Varga, Tamas Norbert; Weller, Jochen; Wilkinson, ReeseChen, Angela; Huterer, Dragan; Lee, Sujeong; Ferté, Agnès; Weaverdyck, Noah; Alonso Alves, Otavio; Leonard, C. Danielle; Maccrann, Niall; Raveri, Marco; Porredon, Anna; Di Valentino, Eleonora; Muir, Jessica; Lemos, Pablo; Liddle, Andrew; Blazek, Jonathan; Campos, Andresa; Cawthon, Ross; Choi, Ami; Dodelson, Scott; Elvin-Poole, Jack; Gruen, Daniel; Ross, Ashley; Secco, Lucas F.; Sevilla, Ignacio; Sheldon, Erin; Troxel, Michael A.; Zuntz, Joe; Abbott, Tim; Aguena, Michel; Allam, Sahar; Annis, James; Avila, Santiago; Bertin, Emmanuel; Bhargava, Sunayana; Bridle, Sarah; Brooks, David; Carnero Rosell, Aurelio; Carrasco Kind, Matias; Carretero, Jorge; Costanzi, Matteo; Crocce, Martin; da Costa, Luiz; Elidaiana da Silva Pereira, Maria; Davis, Tamara; Doel, Peter; Eifler, Tim; Ferrero, Ismael; Fosalba, Pablo; Frieman, Josh; Garcia-Bellido, Juan; Gaztanaga, Enrique; Gerdes, David; Gruendl, Robert; Gschwend, Julia; Gutierrez, Gaston; Hinton, Samuel; Hollowood, Devon L.; Honscheid, Klaus; Hoyle, Ben; James, David; Jarvis, Mike; Kuehn, Kyler; Lahav, Ofer; Maia, Marcio; Marshall, Jennifer; Menanteau, Felipe; Miquel, Ramon; Morgan, Robert; Palmese, Antonella; Paz-Chinchon, Francisco; Plazas Malagón, Andrés; Roodman, Aaron; Sanchez, Eusebio; Scarpine, Vic; Schubnell, Michael; Serrano, Santiago; Smith, Mathew; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Chun-Hao, To; Varga, Tamas Norbert; Weller, Jochen; Wilkinson, Rees

Italian Synagogue, circa 1860, Galata Quarter of Istanbul

This synagogue, still in use, was built on land granted to the Jewish community by Sultan Abdul Azziz and serves the Italian community in IstanbulDigital imag

Synagogue in Ankara Interior: Tevah from rear - detail at steps

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Synagogue in Ankara Interior: Hechal - underside of canopy: medalion and light fixture

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Synagogue in Ankara Interior: Hechal - Hebrew on left-hand door

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Synagogue in Bergama Interior: ceiling detail at eastern end

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Synagogue in Bergama Exterior: View from north/east with entrance

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