Abstract is missing.
- Exploiting the Earth's Spherical Geometry to Geolocate ImagesMike Izbicki, Evangelos E. Papalexakis, Vassilis J. Tsotras. 3-19 [doi]
- Continual Rare-Class Recognition with Emerging Novel SubclassesHung Nguyen, Xuejian Wang, Leman Akoglu. 20-36 [doi]
- Unjustified Classification Regions and Counterfactual Explanations in Machine LearningThibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki. 37-54 [doi]
- Shift Happens: Adjusting ClassifiersTheodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull. 55-70 [doi]
- Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled DataJessa Bekker, Pieter Robberechts, Jesse Davis. 71-85 [doi]
- Cost Sensitive Evaluation of Instance Hardness in Machine LearningRicardo B. C. Prudêncio. 86-102 [doi]
- Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary ClassificationMari-Liis Allikivi, Meelis Kull. 103-120 [doi]
- PP-PLL: Probability Propagation for Partial Label LearningKai-wei Sun, Zijian Min, Jin Wang. 123-137 [doi]
- Neural Message Passing for Multi-label ClassificationJack Lanchantin, Arshdeep Sekhon, Yanjun Qi. 138-163 [doi]
- Assessing the Multi-labelness of Multi-label DataLaurence A. F. Park, Yi Guo, Jesse Read. 164-179 [doi]
- Synthetic Oversampling of Multi-label Data Based on Local Label DistributionBin Liu, Grigorios Tsoumakas. 180-193 [doi]
- Distributed Learning of Non-convex Linear Models with One Round of CommunicationMike Izbicki, Christian R. Shelton. 197-212 [doi]
- SLSGD: Secure and Efficient Distributed On-device Machine LearningCong Xie, Oluwasanmi Koyejo, Indranil Gupta. 213-228 [doi]
- Trade-Offs in Large-Scale Distributed Tuplewise Estimation And LearningRobin Vogel, Aurélien Bellet, Stéphan Clémençon, Ons Jelassi, Guillaume Papa. 229-245 [doi]
- Importance Weighted Generative NetworksMaurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson. 249-265 [doi]
- Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural NetworksTakuro Kutsuna. 266-282 [doi]
- LYRICS: A General Interface Layer to Integrate Logic Inference and Deep LearningGiuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori. 283-298 [doi]
- Deep Eyedentification: Biometric Identification Using Micro-movements of the EyeLena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, Tobias Scheffer. 299-314 [doi]
- Adversarial Invariant Feature Learning with Accuracy Constraint for Domain GeneralizationKei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo. 315-331 [doi]
- Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement BiometricsAhmed AbdelWahab, Niels Landwehr. 332-348 [doi]
- Multitask Hopfield NetworksMarco Frasca 0001, Giuliano Grossi, Giorgio Valentini. 349-365 [doi]
- Meta-Learning for Black-Box OptimizationVishnu TV, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, Gautam M. Shroff. 366-381 [doi]
- Training Discrete-Valued Neural Networks with Sign Activations Using Weight DistributionsWolfgang Roth, Günther Schindler, Holger Fröning, Franz Pernkopf. 382-398 [doi]
- Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural NetworksMatthias Kissel, Klaus Diepold. 399-414 [doi]
- Hyper-Parameter-Free Generative Modelling with Deep Boltzmann TreesNico Piatkowski. 415-431 [doi]
- 0-ARM: Network Sparsification via Stochastic Binary OptimizationYang Li, Shihao Ji. 432-448 [doi]
- Learning with Random Learning RatesLéonard Blier, Pierre Wolinski, Yann Ollivier. 449-464 [doi]
- FastPoint: Scalable Deep Point ProcessesAli Caner Türkmen, Yuyang Wang, Alexander J. Smola. 465-480 [doi]
- Single-Path NAS: Designing Hardware-Efficient ConvNets in Less Than 4 HoursDimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, Diana Marculescu. 481-497 [doi]
- Scalable Large Margin Gaussian Process ClassificationMartin Wistuba, Ambrish Rawat. 501-516 [doi]
- Integrating Learning and Reasoning with Deep Logic ModelsGiuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori. 517-532 [doi]
- Neural Control Variates for Monte Carlo Variance ReductionRuosi Wan, Mingjun Zhong, Haoyi Xiong, Zhanxing Zhu. 533-547 [doi]
- Data Association with Gaussian ProcessesMarkus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek. 548-564 [doi]
- Incorporating Dependencies in Spectral Kernels for Gaussian ProcessesKai Chen, Twan van Laarhoven, Jinsong Chen, Elena Marchiori. 565-581 [doi]
- Deep Convolutional Gaussian ProcessesKenneth Blomqvist, Samuel Kaski, Markus Heinonen. 582-597 [doi]
- Bayesian Generalized Horseshoe Estimation of Generalized Linear ModelsDaniel F. Schmidt, Enes Makalic. 598-613 [doi]
- Fine-Grained Explanations Using Markov LogicKhan Mohammad Al Farabi, Somdeb Sarkhel, Sanorita Dey, Deepak Venugopal. 614-629 [doi]
- Unsupervised Sentence Embedding Using Document Structure-Based ContextTaesung Lee, Youngja Park. 633-647 [doi]
- Copy Mechanism and Tailored Training for Character-Based Data-to-Text GenerationMarco Roberti, Giovanni Bonetta, Rossella Cancelliere, Patrick Gallinari. 648-664 [doi]
- NSEEN: Neural Semantic Embedding for Entity NormalizationShobeir Fakhraei, Joel Mathew, José Luis Ambite. 665-680 [doi]
- Beyond Bag-of-Concepts: Vectors of Locally Aggregated ConceptsMaarten Grootendorst, Joaquin Vanschoren. 681-696 [doi]
- A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small DatasetCornelia Ferner, Stefan Wegenkittl. 697-710 [doi]
- Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced ModelPrashanth Vijayaraghavan, Deb Roy. 711-726 [doi]