Search Results for author:
Found 13 papers, 3 papers with code
Context-Aware Ensemble Learning for Time Series
Unlike all the literature in ensembling, for the first time, we introduce a new approach using a meta learner that effectively combines the base model predictions via using a superset of the features that is the union of the base models' feature vectors instead of the predictions themselves.
Crime Prediction with Graph Neural Networks and Multivariate Normal Distributions
Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions.
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy Gradients
We show that when the reinforcement signals received by the agents have a high variance, deep actor-critic approaches that overcome the overestimation bias lead to a substantial underestimation bias.
Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms
To this end, we introduce a novel deep learning architecture for forecasting high-resolution spatio-temporal weather data.
Markovian RNN: An Adaptive Time Series Prediction Network with HMM-based Switching for Nonstationary Environments
We investigate nonlinear regression for nonstationary sequential data.
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks
We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values.
Prediction with Spatio-temporal Point Processes with Self Organizing Decision Trees
We extend the formulations of a standard point process model that can represent time-series data to represent a spatio-temporal data.
Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels
We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process.
Accelerating Min-Max Optimization with Application to Minimal Bounding Sphere
We study the min-max optimization problem where each function contributing to the max operation is strongly-convex and smooth with bounded gradient in the search domain.
Sequential Outlier Detection based on Incremental Decision Trees
In order to construct our multi-modal density function, we use an incremental decision tree to construct a set of subspaces of the observation space.
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks
We investigate anomaly detection in an unsupervised framework and introduce Long Short Term Memory (LSTM) neural network based algorithms.
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
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Adaptive and Efficient Nonlinear Channel Equalization for Underwater Acoustic Communication
We investigate underwater acoustic (UWA) channel equalization and introduce hierarchical and adaptive nonlinear channel equalization algorithms that are highly efficient and provide significantly improved bit error rate (BER) performance.
A new robust adaptive algorithm for underwater acoustic channel equalization
We introduce a novel family of adaptive robust equalizers for highly challenging underwater acoustic (UWA) channel equalization.
