58,663 research outputs found
A note on: an empirical comparison of forgetting models
In the above paper, Nembhard and Osothsilp (2001) empirically compared several forgetting models against empirical data on production breaks. Among the models compared was the learnâforget curve model (LFCM) developed by Jaber and Bonney(1996). In previous research, several studies have shown that the LFCM is advantageous to some of the models being investigated, however, Nembhard and Osothsilp (2001) found that the LFCM showed the largest deviation from empirical data. In this commentary, we demonstrate that the poor performance of the LFCM in the study of Nembhard and Osothsilp (2001) might be attributed to an error on their part when fitting the LFCM to their empirical data
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Incremental learning (IL) has received a lot of attention recently, however,
the literature lacks a precise problem definition, proper evaluation settings,
and metrics tailored specifically for the IL problem. One of the main
objectives of this work is to fill these gaps so as to provide a common ground
for better understanding of IL. The main challenge for an IL algorithm is to
update the classifier whilst preserving existing knowledge. We observe that, in
addition to forgetting, a known issue while preserving knowledge, IL also
suffers from a problem we call intransigence, inability of a model to update
its knowledge. We introduce two metrics to quantify forgetting and
intransigence that allow us to understand, analyse, and gain better insights
into the behaviour of IL algorithms. We present RWalk, a generalization of
EWC++ (our efficient version of EWC [Kirkpatrick2016EWC]) and Path Integral
[Zenke2017Continual] with a theoretically grounded KL-divergence based
perspective. We provide a thorough analysis of various IL algorithms on MNIST
and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in
terms of accuracy, and also provides a better trade-off between forgetting and
intransigence
Large Time-Varying Parameter VARs
In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a time-varying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs. These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach
Using VARs and TVP-VARs with many macroeconomic variables
This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach
A new index of financial conditions
We use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key US macroeconomic variables. Time-variation in the modelâs parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for dynamic model averaging or selection which allow the financial variables entering into the financial conditions index to change over time. We discuss why such extensions of the existing literature are important and show them to be so in an empirical application involving a wide range of financial variables
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