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Found 8 papers, 4 papers with code
Prospective Learning: Learning for a Dynamic Future
We show that standard ERM as done in PAC learning, without incorporating time, can result in failure to learn when distributions are dynamic.
Approximately optimal domain adaptation with Fisher's Linear Discriminant
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation.
The Value of Out-of-Distribution Data
In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples.
Deep Discriminative to Kernel Density Graph for In- and Out-of-distribution Calibrated Inference
However, these methods are not calibrated for the entire feature space, leading to overconfidence in the case of out-of-distribution (OOD) samples.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
Towards Accurate Cross-Domain In-Bed Human Pose Estimation
Majority of the contactless human pose estimation algorithms are based on RGB modality, causing ineffectiveness in in-bed pose estimation due to occlusions by blankets and varying illumination conditions.
A Thickness Sensitive Vessel Extraction Framework for Retinal and Conjunctival Vascular Tortuosity Analysis
In addition, the proposed framework was utilized to determine the association of diabetes with retinal and conjunctival vascular tortuosity.
A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping
Phase unwrapping is a classical ill-posed problem which aims to recover the true phase from wrapped phase.
