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.
EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.
Develops a method to estimate average hazard under non-proportional hazards without relying on proportional hazards assumption.
Study explains mortgage burnout using Cox hazard models.
New method estimates hazard ratios without bias in observational studies.
The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.
Novel approach to compute hazard ratios from observational studies using SCMs and backdoor adjustment.
Unified model estimates landslide hazard combining susceptibility, intensity, and frequency.
EdgeLite detects hazardous supermarket floors, improving safety.
Advanced Dropout improves DNN performance without requiring model-specific dropout techniques.
Dropout is a simple but efficient regularization technique for achieving better generalization of deep neural networks (DNNs); hence it is widely used in tasks based on DNNs. During training, dropout randomly discards a portion of the neurons to avoid overfitting. This paper presents an enhanced dropout technique, whic…
Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes \textit{random drop} of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the tra…
Recent work on follow the perturbed leader (FTPL) algorithms for the adversarial multi-armed bandit problem has highlighted the role of the hazard rate of the distribution generating the perturbations. Assuming that the hazard rate is bounded, it is possible to provide regret analyses for a variety of FTPL algorithms f…
Dropout regularizes against high-order interactions by canceling interaction rates.
Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors. Current explanations of dropout include bagging, naive Bayes, regularization, and sex in evolution. According to the activation pattern…
We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output i…
Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of wei…
Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager,…
Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training. Inspired by dropout, this paper presents GI-Dropout, a novel dropout method integrating with global information to improve neural networks for text classification. Unlike the traditional dropout method in which the…
Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of conver…
Rate-In dynamically adjusts dropout rates during inference to improve uncertainty estimation in neural networks.
Spatially-aware model improves earthquake hazard assessment accuracy.
QSD enhances deep network performance through biologically plausible dropout.
Analyzes dropout in neural networks using differential equations.
Develops a multilevel Monte Carlo framework with dropout for efficient uncertainty quantification.
Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques for RNNs were introduced to respond to these demands, but we conjecture that the dropout on RNNs could have been improved by adopting the ad…
QSurv models survival data without discretization, achieving high accuracy.
Dropout introduces both explicit and implicit regularization effects.
Dropout technique is analyzed using information geometry.
The paper analyzes SGD with dropout regularization in linear models, proving asymptotic properties and providing inference tools.
Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition. This learning uses a large number of layers and a huge number of units and connections. Therefore, overfitting is a serious problem with it, and the dropout which is a kind of regularization tool is used. However, …
Dropout has proven to be an effective technique for regularization and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability during the training stage of DNN. Dropout also provides a way of approximately combining exponentially many different neural networ…
Study optimal reinsurance contracts to prevent moral hazard under non-concave premium principles.
Dropout training improves neural networks' performance.
The paper improves dropout's utility by reducing interactions in deep neural networks.
Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences. We propose a new orthogonal score method for treatment effect estimation and inference that results in asymptotically valid confidence int…
Study compares dropout and l2 regularization in linear models.
Study detects anomalies in robot vision data to predict hazards.
Qualitative analysis of MC dropout for NN model uncertainty.
Dropout is typically interpreted as bagging a large number of models sharing parameters. We show that using dropout in a network can also be interpreted as a kind of data augmentation in the input space without domain knowledge. We present an approach to projecting the dropout noise within a network back into the input…
Dropout controls model capacity in deep learning and matrix completion.
BoXHED boosts hazard estimation for dynamic health risk scores.
Flexible DNN for survival data, avoiding proportional hazards assumption.
In this paper, we provide a solution to two problems which have been open in default time modeling in credit risk. We first show that if is an arbitrary random (default) time such that its Azéma's supermartingale $Z_t^τ=¶(τ>t|\F_t)$ is continuous, then avoids stopping times. We then disprove a conjecture about …
Continuum Dropout improves neural differential equations by preventing overfitting.
Dropout and other feature noising schemes control overfitting by artificially corrupting the training data. For generalized linear models, dropout performs a form of adaptive regularization. Using this viewpoint, we show that the dropout regularizer is first-order equivalent to an L2 regularizer applied after scaling t…
Study on dropout in neural networks using percolation theory.
Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means an…
In classification applications, we often want probabilistic predictions to reflect confidence or uncertainty. Dropout, a commonly used training technique, has recently been linked to Bayesian inference, yielding an efficient way to quantify uncertainty in neural network models. However, as previously demonstrated, conf…