S}, the training of ML models usually requires to find a parameter vector w∈Rd minimizing a loss function: minimizew∈RdF(w)≜1S∑l=1Sf(xl,w),where f(xl,w) is the loss of the model w on the datapoint xl. For example, in supervised learning, each point of the dataset is a ...
Our analytic platform delivers data-driven, holistic recommendations for your organization's casualty program. Using stochastic simulation, Dynamic Casualty Forecast provides an up-to-the-moment view of your loss potential for all casualty lines using ke
To deal with learning problems with only a few labeled data, a novel semi-supervised learning method combined with dynamic graph learning with self-paced learning mechanism is present in this work, namely SS-GSELM. Firstly, according to the loss value of labeled samples in each training, the ...
Graph learningMetric learningMini-batchRecently, batch-based image data representation has been demonstrated to be effective for context-enhanced image representation. The core issue for this task is capturing the dependences of image samples within each mini-batch and conducting message communication among...
Our method ensures robust learning amidst outliers, influenced by tissue deformation, smoke, and surgical instruments, by utilizing a unique loss function. This function adjusts the selection and weighting of depth data for learning based on their given confidence. We trained the model using the ...
One potential way to mitigate this large aspect ratios would be to use a higher penalty value for the width and height loss, but this could influence the balance of bounding box position accuracy without careful tuning. By comparing with our ground truth annotation, we found these predicted ...
We extend the idea of automated debiased machine learning to the dynamic treatment regime. We show that the multiply robust formula for the dynamic treatment regime with discrete treatments can be re-stated in terms of a recursive Riesz representer characterization of nested mean regressions. We the...
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Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the...
The online reconfiguration based on the reinforcement learning has been proposed in [4]. The objective function for this research include the minimizing power loss of the network and improving voltage profile. In this research, the renewable energy resources and the reliability of the distribution ...