A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Focal loss improves classification but not class-posterior probability estimation.
Focal loss reduces model curvature for better calibration.
Study of focal-entropy for class-imbalanced classification.
Focal loss improves deep neural networks' accuracy and calibration.
Enhanced loss function boosts fraud detection in auto insurance claims.
This paper proposes a Residual Convolutional Neural Network (ResNet) based on speech features and trained under Focal Loss to recognize emotion in speech. Speech features such as Spectrogram and Mel-frequency Cepstral Coefficients (MFCCs) have shown the ability to characterize emotion better than just plain text. Furth…
This research improves deep neural network calibration using a new loss function.
Paper proposes D3M to improve anti-spoofing detection by balancing loss function and using complementary features.
In this paper, we propose a Dual Focal Loss (DFL) function, as a replacement for the standard cross entropy (CE) function to achieve a better treatment of the unbalanced classes in a dataset. Our DFL method is an improvement on the recently reported Focal Loss (FL) cross-entropy function, which proposes a scaling metho…
Paper improves deep point cloud compression techniques.
The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks. Though a small-scale program in terms of size, the package is, to the best of the authors' knowledge, the first of its kind which prov…
A framework for multi-label sentiment analysis in 100 languages with dynamic weighting.
Study recommends PLM choices for minimizing calibration error in NLP tasks.
Paper proposes a new anomaly detection method using Random Forest with Mallows-like criterion.
The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture is used along with dense connection at encoder part to propagate the information from coarse layer to deep layers. This architecture is used to train…
In medical real-world study (RWS), how to fully utilize the fragmentary and scarce information in model training to generate the solid diagnosis results is a challenging task. In this work, we introduce a novel multi-instance neural network, AMI-Net+, to train and predict from the incomplete and extremely imbalanced da…
Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a central problem of small-molecule based drug discovery. Molecules can be represented as an undirected graph, and we can utilize graph convolution networks to predication molecular properties. However, graph convolutional network…
Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood. This study analyzes overfitting by examining how the distribution of logits alters in relation to how much the model overfits. Specifically, we find that when trai…
This paper introduces 'General Cyclical Training' for neural networks.
SIGTRON improves classification accuracy for imbalanced datasets.
This paper examines how different loss functions affect neural network features and performance.