**Econometrics**

Econometrics is the application of mathematics, statistical methods, and, more recently, computer science, to economic data and is described as the branch of economics that aims to give empirical content to economic relations. More precisely, it is “the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference.” An introductory economics textbook describes econometrics as allowing economists “to sift through mountains of data to extract simple relationships.” The first known use of the term “econometrics” (in cognate form) was by Polish economist Pawel Ciompa in 1910. Ragnar Frisch is credited with coining the term in the sense in which it is used today. Econometrics is the intersection of economics, mathematics, and statistics. Econometrics adds empirical content to economic theory allowing theories to be tested and used for forecasting and policy evaluation. … **Generative Query Network (CQN)**

A framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes. Much like infants and animals, the GQN learns by trying to make sense of its observations of the world around it. In doing so, the GQN learns about plausible scenes and their geometrical properties, without any human labelling of the contents of scenes. The GQN model is composed of two parts: a representation network and a generation network. The representation network takes the agent’s observations as its input and produces a representation (a vector) which describes the underlying scene. The generation network then predicts (‘imagines’) the scene from a previously unobserved viewpoint. … **Confidence Trigger Detection (CTD)**

With deep learning based image analysis getting popular in recent years, a lot of multiple objects tracking applications are in demand. Some of these applications (e.g., surveillance camera, intelligent robotics, and autonomous driving) require the system runs in real-time. Though recent proposed methods reach fairly high accuracy, the speed is still slower than real-time application requirement. In order to increase tracking-by-detection system’s speed for real-time tracking, we proposed confidence trigger detection (CTD) approach which uses confidence of tracker to decide when to trigger object detection. Using this approach, system can safely skip detection of images frames that objects barely move. We had studied the influence of different confidences in three popular detectors separately. Though we found trade-off between speed and accuracy, our approach reaches higher accuracy at given speed. … **Bayesian Hypernetworks**

We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks. A Bayesian hypernetwork, $h$, is a neural network which learns to transform a simple noise distribution, $p(\epsilon) = \mathcal{N}(0,I)$, to a distribution $q(\theta) \doteq q(h(\epsilon))$ over the parameters $\theta$ of another neural network (the ‘primary network’). We train $q$ with variational inference, using an invertible $h$ to enable efficient estimation of the variational lower bound on the posterior $p(\theta | \mathcal{D})$ via sampling. In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap i.i.d. sampling of $q(\theta)$. We demonstrate these qualitative advantages of Bayesian hypernets, which also achieve competitive performance on a suite of tasks that demonstrate the advantage of estimating model uncertainty, including active learning and anomaly detection. …

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Apr 2022

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