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Neutralizing Self-Selection Bias in Sampling for Sortition
BaileyFlanigan, PaulGoelz, AnupamGupta....
Published date-12/01/2020
fairness
Sortition is a political system in which decisions are made by panels of randomly selected citizens. The process for selecting a sortition panel is traditionally thought of as uniform sampling …
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 …
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 …
Learning efficient task-dependent representations with synaptic plasticity
ColinBredenberg, EeroSimoncelli, CristinaSavin....
Published date-12/01/2020
Neural populations encode the sensory world imperfectly: their capacity is limited by the number of neurons, availability of metabolic and other biophysical resources, and intrinsic noise. The brain is presumably …
Counting People by Estimating People Flows
WeizheLiu, MathieuSalzmann, PascalFua....
Published date-12/01/2020
ActiveLearning, CrowdCounting, OpticalFlowEstimation
Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in …
Improving model calibration with accuracy versus uncertainty optimization
RanganathKrishnan, OmeshTickoo....
Published date-12/01/2020
ImageClassification, VariationalInference
Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications. A well-calibrated model should be accurate when it is certain about its prediction …