6,153 research outputs found

    Multistart Methods for Quantum Approximate Optimization

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    Hybrid quantum-classical algorithms such as the quantum approximate optimization algorithm (QAOA) are considered one of the most promising approaches for leveraging near-term quantum computers for practical applications. Such algorithms are often implemented in a variational form, combining classical optimization methods with a quantum machine to find parameters to maximize performance. The quality of the QAOA solution depends heavily on quality of the parameters produced by the classical optimizer. Moreover, the presence of multiple local optima in the space of parameters makes it harder for the classical optimizer. In this paper we study the use of a multistart optimization approach within a QAOA framework to improve the performance of quantum machines on important graph clustering problems. We also demonstrate that reusing the optimal parameters from similar problems can improve the performance of classical optimization methods, expanding on similar results for MAXCUT

    Reuse of Neural Modules for General Video Game Playing

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    A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain. Networks trained for a new domain can improve their performance by routing activation selectively through previously learned neural structure, regardless of how or for what it was learned. A neuroevolution implementation of this approach is presented with application to high-dimensional sequential decision-making domains. This approach is more general than previous approaches to neural transfer for reinforcement learning. It is domain-agnostic and requires no prior assumptions about the nature of task relatedness or mappings. The method is analyzed in a stochastic version of the Arcade Learning Environment, demonstrating that it improves performance in some of the more complex Atari 2600 games, and that the success of transfer can be predicted based on a high-level characterization of game dynamics.Comment: Accepted at AAAI 1

    Right for the Right Reason: Training Agnostic Networks

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    We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

    Path tracing multivue adaptatif

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    International audienceRendering photo-realistic image sequences using path tracing and Monte Carlo integration often requires sampling a large number of paths to get converged results. In the context of rendering multiple views or animated sequences, such sampling can be highly redundant. Several methods have been developed to share sampled paths between spatially or temporarily similar views. However, such sharing is challenging since it can lead to bias in the final images. Our contribution is a Monte Carlo sampling technique which generates paths, taking into account several cameras. First, we sample the scene from all the cameras to generate hit points. Then, an importance sampling technique generates bouncing directions which are shared by a subset of cameras. This set of hit points and bouncing directions is then used within a regular path tracing solution. For animated scenes, paths remain valid for a fixed time only, but sharing can still occur between cameras as long as their exposure time intervals overlap. We show that our technique generates less noise than regular path tracing and does not introduce noticeable bias.Le rendu de séquences d'images photoréalistes en lancer de rayons nécessite souvent l'échantillonnage d'un grand nombre de chemins pour obtenir des résultats convergés. Dans le contexte du rendu multi-vue ou de séquences animées, cet échantillonnage peut être redondant. Plusieurs méthodes ont été développées pour réutiliser des chemins échantillonnés entre des vues proches spatialement ou temporellement. Cependant, un telle réutilisation est complexe car elle peut mener à du biais dans les images. Notre contribution est une technique d'échantillonnage qui génère des chemins en prenant en compte plusieurs caméras. Tout d'abord, nous échantillonnons la scène depuis toutes les caméras pour générer des points visibles. Ensuite, nous générons des directions par importance qui sont partagées par un sous-ensemble de caméras. Cet ensemble de points et de directions est ensuite utilisé dans une solution de lancer de rayons classique. Pour les scènes animées, les chemins ne sont réutilisables qu'à temps fixe, mais un partage peut toujours avoir lieu entre les caméras si leurs intervalles d'exposition se recouvrent. Notre technique génère moins de bruit que le path tracing classique à temps de calcul équivalent et n’introduit pas de biais perceptible
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