GIBBONFINDR: An R package for the detection and classification of acoustic signals. (arXiv:1906.02572v2 [eess.AS] UPDATED)

The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions. One of the main obstacles in implementing wide scale acoustic monitoring programs in terrestrial environments is the lack of user-friendly, open source programs for processing…

An “outside the box” solution for imbalanced data classification. (arXiv:1911.06965v1 [cs.LG])

A common problem of the real-world data sets is the class imbalance, which can significantly affect the classification abilities of classifiers. Numerous methods have been proposed to cope with this problem; however, even state-of-the-art methods offer a limited improvement (if any) for data sets with critically under-represented minority classes. For such problematic cases, an “outside…

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry. (arXiv:1906.11286v3 [cs.LG] UPDATED)

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases — an important component of human decision making…

Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift. (arXiv:1911.06970v1 [cs.LG])

Off-policy deep reinforcement learning (RL) algorithms are incapable of learning solely from batch offline data without online interactions with the environment, due to the phenomenon known as \textit{extrapolation error}. This is often due to past data available in the replay buffer that may be quite different from the data distribution under the current policy. We…

Parametric Graph-based Separable Transforms for Video Coding. (arXiv:1911.06981v1 [cs.MM])

In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction. This paper proposes a parametric approach to build graph-based separable transforms (GBSTs) for video coding. Specifically, a GBST is derived from a pair of line graphs, whose weights are determined based on two…

Selective sampling for accelerating training of deep neural networks. (arXiv:1911.06996v1 [cs.LG])

We present a selective sampling method designed to accelerate the training of deep neural networks. To this end, we introduce a novel measurement, the minimal margin score (MMS), which measures the minimal amount of displacement an input should take until its predicted classification is switched. For multi-class linear classification, the MMS measure is a natural…

A spatio-temporal multi-scale model for Geyer saturation point process: application to forest fire occurrences. (arXiv:1911.06999v1 [stat.AP])

Since most natural phenomena exhibit dependence at multiple scales (e.g. earthquake and forest fire occurrences), single-scale spatio-temporal Gibbs models are unrealistic in many applications. This motivates statisticians to construct the multi-scale generalizations of the classical Gibbs models and to develop new Gibbs point process models. In this paper, we extend the spatial multi-scale Geyer point…

General Regression Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, and Feedforward Neural Networks. (arXiv:1911.07115v1 [eess.SY])

The aim of this project is to develop a code to discover the optimal sigma value that maximum the F1 score and the optimal sigma value that maximizes the accuracy and to find out if they are the same. Four algorithms which can be used to solve this problem are: Genetic Regression Neural Networks (GRNNs),…

Graph Topological Aspects of Granger Causal Network Learning. (arXiv:1911.07121v1 [math.ST])

We study Granger causality in the context of wide-sense stationary time series, where our focus is on the topological aspects of the underlying causality graph. We establish sufficient conditions (in particular, we develop the notion of a “strongly causal” graph topology) under which the true causality graph can be recovered via pairwise causality testing alone,…