Home /
Research
Showing 79 - 84 / 897
Fair Multiple Decision Making Through Soft Interventions
YaoweiHu, YongkaiWu, LuZhang....
Published date-12/01/2020
DecisionMaking, fairness
Previous research in fair classification mostly focuses on a single decision model. In reality, there usually exist multiple decision models within a system and all of which may contain a …
Bayesian Pseudocoresets
DionysisManousakas, ZuhengXu, CeciliaMascolo....
Published date-12/01/2020
BayesianInference
Standard Bayesian inference algorithms are prohibitively expensive in the regime of modern large-scale data. Recent work has found that a small, weighted subset of data (a coreset) may be used …
Rethinking Learnable Tree Filter for Generic Feature Transform
LinSong, YanweiLi, ZhengkaiJiang....
Published date-12/01/2020
InstanceSegmentation, ObjectDetection, SemanticSegmentation
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces it to focus on the regions with close spatial …
RaP-Net: A Region-wise and Point-wise Weighting Network to Extract Robust Keypoints for Indoor Localization
DongjiangLi, JinyuMiao, XuesongShi....
Published date-12/01/2020
IndoorLocalization, VisualLocalization
Image keypoint extraction is an important step for visual localization. The localization in indoor environment is challenging for that there may be many unreliable features on dynamic or repetitive objects. …
Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation
IsabellaPozzi, SanderBohte, PieterRoelfsema....
Published date-12/01/2020
ImageClassification
Much recent work has focused on biologically plausible variants of supervised learning algorithms. However, there is no teacher in the motor cortex that instructs the motor neurons and learning in …
Almost Surely Stable Deep Dynamics
NathanLawrence, PhilipLoewen, MichaelForbes....
Published date-12/01/2020
We introduce a method for learning provably stable deep neural network based dynamic models from observed data. Specifically, we consider discrete-time stochastic dynamic models, as they are of particular interest …