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Institution

Activision Blizzard

About: Activision Blizzard is a based out in . It is known for research contribution in the topics: Deep learning & Stability (learning theory). The organization has 19 authors who have published 11 publications receiving 120 citations. The organization is also known as: Activision Blizzard, Inc. & Activision|Blizzard.

Papers
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Proceedings ArticleDOI
01 Aug 2018
TL;DR: An approach to learn and deploy human-like playtesting in computer games based on deep learning from player data is presented and it is shown that this approach increases correlation with average level difficulty, giving more accurate predictions as well as requiring only a fraction of the computation time.
Abstract: We present an approach to learn and deploy human-like playtesting in computer games based on deep learning from player data. We are able to learn and predict the most "human" action in a given position through supervised learning on a convolutional neural network. Furthermore, we show how we can use the learned network to predict key metrics of new content — most notably the difficulty of levels. Our player data and empirical data come from Candy Crush Saga (CCS) and Candy Crush Soda Saga (CCSS). However, the method is general and well suited for many games, in particular where content creation is sequential. CCS and CCSS are non-deterministic match-3 puzzle games with multiple game modes spread over a few thousand levels, providing a diverse testbed for this technique. Compared to Monte Carlo Tree Search (MCTS) we show that this approach increases correlation with average level difficulty, giving more accurate predictions as well as requiring only a fraction of the computation time.

64 citations

Journal ArticleDOI
TL;DR: Two real‐time models for simulating subsurface scattering for a large variety of translucent materials, which need under 0.5 ms per frame to execute, are proposed, reducing both execution time and memory consumption, while delivering results comparable to techniques with higher cost.
Abstract: In this paper, we propose two real-time models for simulating subsurface scattering for a large variety of translucent materials, which need under 0.5 ms per frame to execute. This makes them a practical option for real-time production scenarios. Current state-of-the-art, real-time approaches simulate subsurface light transport by approximating the radially symmetric non-separable diffusion kernel with a sum of separable Gaussians, which requires multiple up to 12 1D convolutions. In this work we relax the requirement of radial symmetry to approximate a 2D diffuse reflectance profile by a single separable kernel. We first show that low-rank approximations based on matrix factorization outperform previous approaches, but they still need several passes to get good results. To solve this, we present two different separable models: the first one yields a high-quality diffusion simulation, while the second one offers an attractive trade-off between physical accuracy and artistic control. Both allow rendering of subsurface scattering using only two 1D convolutions, reducing both execution time and memory consumption, while delivering results comparable to techniques with higher cost. Using our importance-sampling and jittering strategies, only seven samples per pixel are required. Our methods can be implemented as simple post-processing steps without intrusive changes to existing rendering pipelines.

36 citations

Journal ArticleDOI
TL;DR: An end-to-end architecture ROTConvPCE-mv that performs tactile recognition using residual orthogonal tiling and pyramid convolution ensemble that outperforms several state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance.
Abstract: Tactile recognition enables robots identify target objects or environments from tactile sensory readings. The recent advancement of deep learning and biological tactile sensing inspire us proposing an end-to-end architecture ROTConvPCE-mv that performs tactile recognition using residual orthogonal tiling and pyramid convolution ensemble. Our approach uses stacks of raw frames and tactile flow as dual input, and incorporates the strength of multi-layer OTConvs (orthogonal tiling convolutions) organized in a residual learning paradigm. We empirically demonstrate that OTConvs have adjustable invariance capability to different input transformations such as translation, rotation, and scaling. To effectively capture multi-scale global context, a pyramid convolution structure is attached to the concatenated output of two residual OTConv pathways. The extensive experimental evaluations show that ROTConvPCE-mv outperforms several state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance. Practical suggestions and hints are summarized throughout this paper to facilitate the effective recognition using tactile sensory data.

9 citations

Journal ArticleDOI
TL;DR: A new measurement scale is explored that considers the widespread use of personal digital devices and examines gender differences in OSA and results show that the suggested scale is a reliable measurement of OSA.

8 citations


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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20204
20191
20182
20151
20142
20091