Constrained Image Generation Using Binarized Neural Networks with Decision Procedures
Stanford University（斯坦福大学）；VMware Research（威睿研究院）；University of Sassari（萨萨里大学）；University of Genoa（热那亚大学）；Microsoft Research（微软研究院）；Tel Aviv University（特拉维夫大学）
We consider the problem of binary image generation with given properties. This problem arises in a number of practical applications, including generation of artificial porous medium for an electrode of lithium-ion batteries, for composed materials, etc. A generated image represents a porous medium and, as such, it is subject to two sets of constraints: topological constraints on the structure and process constraints on the physical process over this structure. To perform image generation we need to define a mapping from a porous medium to its physical process parameters. For a given geometry of a porous medium, this mapping can be done by solving a partial differential equation (PDE). However, embedding a PDE solver into the search procedure is computationally expensive. We use a binarized neural network to approximate a PDE solver. This allows us to encode the entire problem as a logical formula. Our main contribution is that, for the first time, we show that this problem can be tackled using decision procedures. Our experiments show that our model is able to produce random constrained images that satisfy both topological and process constraints.
CLAD: Constrained Latent Action Diffusion for Vision-Language Procedure Planning
Machine Perception and Interaction group, Örebro University（机器感知与交互组，奥雷布罗大学）；Collaborative Artificial Intelligence group, University of Stuttgart（协作人工智能组，斯图加特大学）
State Key Lab of Computer Science, ISCAS & University of Chinese Academy of Sciences（中国科学院计算技术研究所计算机科学国家重点实验室 & 中国科学院大学）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Alibaba Group（阿里巴巴集团）；ByteDance Inc.（字节跳动有限公司）；University of Macau（澳门大学）
Tsinghua University（清华大学）；State Key Lab of Intelligent Technologies and Systems（智能技术与系统国家重点实验室）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
Shahid Beheshti University（沙希德·贝赫什提大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；CerCo UMR 5549, CNRS Université Toulouse 3（法国国家科学研究中心图卢兹第三大学 CerCo UMR 5549 实验室）
Projection Convolutional Neural Networks for 1-bit CNNs via Discrete Back Propagation
Beihang University（北京航空航天大学）；China University of Mining and Technology, Beijing（中国矿业大学（北京））；Lancaster University（兰卡斯特大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University at Buffalo（布法罗大学）
National Special Education Resource Center for Children with Autism, Zhejiang Normal University（浙江师范大学国家孤独症儿童特殊教育资源中心）；Institute of Cyber-Systems and Control, Zhejiang University（浙江大学 cyber 系统与控制研究所）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）
Robust Technique for Representative Volume Element Identification in Noisy Microtomography Images of Porous Materials Based on Pores Morphology and Their Spatial Distribution
Institute of Microelectronics Technology and High-Purity Materials RAS（俄罗斯科学院微电子技术与高纯材料研究所）；National Research Nuclear University MEPhI（国家研究型核大学MEPhI）；FSRC “Crystallography and Photonics” RAS（俄罗斯科学院晶体学与光子学联邦研究中心）；Smart Engines Services LLC（Smart Engines服务有限责任公司）
