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Robust Loss Functions under Label Noise for Deep Neural Networks
, 2017 In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks
Ghosh, Aritra +2 more
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Weather Radar Echo Extrapolation Method Based on Deep Learning
Atmosphere, 2022 In order to forecast some high intensity and rapidly changing phenomena, such as thunderstorms, heavy rain, and hail within 2 h, and reduce the influence brought by destructive weathers, this paper proposes a weather radar echo extrapolation method based
Fugui Zhang, Can Lai, Wanjun Chen
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Exponential penalty function control of loss networks
, 2005 We introduce penalty-function-based admission control policies to approximately maximize the expected reward rate in a loss network. These control policies are easy to implement and perform well both in the transient period as well as in steady state.
Iyengar, Garud, Sigman, Karl
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Applied Sciences, 2019 We propose a high-performance algorithm while using a promoted and modified form of the You Only Look Once (YOLO) model, which is based on the TensorFlow framework, to enhance the real-time monitoring of traffic-flow problems by an intelligent ...
Chang-Yu Cao +4 more
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Vehicle Re-Identification in Aerial Imagery Based on Normalized Virtual Softmax Loss
Applied Sciences, 2022 With the development and popularization of unmanned aerial vehicles (UAVs) and surveillance cameras, vehicle re-identification (ReID) task plays an important role in the field of urban safety.
Wenzuo Qiao, Wenjuan Ren, Liangjin Zhao
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Entropy, 2020 We consider selection of random predictors for a high-dimensional regression problem with a binary response for a general loss function. An important special case is when the binary model is semi-parametric and the response function is misspecified under
Mariusz Kubkowski, Jan Mielniczuk
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Using Physics-Informed Neural Networks (PINNs) for Tumor Cell Growth Modeling
MathematicsThis paper presents a comprehensive investigation into the applicability and performance of two prominent growth models, namely, the Verhulst model and the Montroll model, in the context of modeling tumor cell growth dynamics.
José Alberto Rodrigues
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Review of: "Interpreting the loss functions of Artificial neural networks in cancer research" [PDF]
, 2023 Shahab U. Ansari
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On surrogate loss functions and $f$-divergences
, 2008 The goal of binary classification is to estimate a discriminant function $\gamma$ from observations of covariate vectors and corresponding binary labels.
Jordan, Michael I. +2 more
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Developing Novel Robust Loss Functions-Based Classification Layers for DLLSTM Neural Networks
IEEE Access, 2023 In this paper, we suggest improving the performance of developed activation function-based Deep Learning Long Short-Term Memory (DLLSTM) structures by employing robust loss functions like Mean Absolute Error $(MAE)$ and Sum Squared Error $(SSE)$ to ...
Mohamad Abou Houran +5 more
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