Machine Learning Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applications (1907.07178v1) Rafael Brandão, Joel Carbonera, Clarisse de Souza, Juliana Ferreira, Bernardo Gonçalves,

Machine Learning Batch-Shaped Channel Gated Networks (1907.06627v1) Babak Ehteshami Bejnordi, Tijmen Blankevoort, Max Welling 2019-07-15 We present a method for gating deep-learning architectures on a

Machine Learning RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies (1907.04799v2) Hao-Tien Lewis Chiang, Jasmine Hsu, Marek Fiser, Lydia Tapia, Aleksandra Faust

Machine Learning Adversarial Objects Against LiDAR-Based Autonomous Driving Systems (1907.05418v1) Yulong Cao, Chaowei Xiao, Dawei Yang, Jing Fang, Ruigang Yang, Mingyan Liu, Bo Li 2019-07-11 Deep neural

Machine Learning Adversarial Objects Against LiDAR-Based Autonomous Driving Systems (1907.05418v1) Yulong Cao, Chaowei Xiao, Dawei Yang, Jing Fang, Ruigang Yang, Mingyan Liu, Bo Li 2019-07-11 Deep neural

Machine Learning Adversarial Objects Against LiDAR-Based Autonomous Driving Systems (1907.05418v1) Yulong Cao, Chaowei Xiao, Dawei Yang, Jing Fang, Ruigang Yang, Mingyan Liu, Bo Li 2019-07-11 Deep neural

Machine Learning Secure multi-party linear regression at plaintext speed (1901.09531v2) Jonathan M. Bloom 2019-01-28 We detail distributed algorithms for scalable, secure multiparty linear regression and

Machine Learning Positional Normalization (1907.04312v1) Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie 2019-07-09 A widely deployed method for reducing the training time of deep neural networks

Machine Learning Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning (1907.03741v1) Ivan Glasser, Ryan

Machine Learning High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction (1907.02959v1) Diego Valsesia, Enrico Magli 2019-07-05 Compression of hyperspectral images

Machine Learning Least Action Principles and Well-Posed Learning Problems (1907.02517v1) Alessandro Betti, Marco Gori 2019-07-04 Machine Learning algorithms are typically regarded as appropriate optimization

Machine Learning Least Action Principles and Well-Posed Learning Problems (1907.02517v1) Alessandro Betti, Marco Gori 2019-07-04 Machine Learning algorithms are typically regarded as appropriate optimization

Machine Learning Least Action Principles and Well-Posed Learning Problems (1907.02517v1) Alessandro Betti, Marco Gori 2019-07-04 Machine Learning algorithms are typically regarded as appropriate optimization

Machine Learning Rethinking Table Recognition using Graph Neural Networks (1905.13391v2) Shah Rukh Qasim, Hassan Mahmood, Faisal Shafait 2019-05-31 Document structure analysis, such as zone segmentation

Machine Learning Efficient Algorithms for Smooth Minimax Optimization (1907.01543v1) Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, Sewoong Oh 2019-07-02 This paper studies first order

Machine Learning ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs (1902.10298v3) Amir Gholami, Kurt Keutzer, George Biros 2019-02-27 Residual neural networks can be viewed as

Machine Learning MLFriend: Interactive Prediction Task Recommendation for Event-Driven Time-Series Data (1906.12348v1) Lei Xu, Shubhra Kanti Karmaker Santu, Kalyan Veeramachaneni 2019-06-28 Most automation

Machine Learning Power and limitations of conformal martingales (1906.09256v1) Vladimir Vovk 2019-06-21 This paper, accompanying my poster at ISIPTA 2019 (5 July 2019), poses the problem of investigating

Machine Learning A tutorial on recursive models for analyzing and predicting path choice behavior (1905.00883v1) Maëlle Zimmermann, Emma Frejinger 2019-05-02 The problem at the heart of this tutorial consists

Machine Learning Similarity of Neural Network Representations Revisited (1905.00414v1) Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton 2019-05-01 Recent work has sought to understand the