Parameterizing the microbial loop: an experiment in reducing model complexity

The structure of the plankton food web in the upper mixed layer has important implications for the export of biogenic material from the euphotic zone. While the action of the microbial loop causes material to be recycled near the surface, activity of the larger zooplankton leads to a significant downward flux of material. The balance between these pathways must be properly represented in climate models to predict carbon export. However, the number of biogeochemical compartments available to represent the food web is limited by the need to couple biogeochemical models with general circulation models. A structurally simple model is therefore sought, with a number of free parameters, which can be constrained by available observations to produce reliable estimates of export.A step towards addressing this aim is described: an attempt is made to emulate the behavior of an 11 compartment model with an explicit microbial loop, using a 4 compartment model. The latter, incorporating a basic microbial loop parameterization, is derived directly from the 'true' model. The results are compared with equivalent results for a 4 compartment model with no representation of the microbial loop. These non-identical twin experiments suggest that export estimates from 4 compartment models are prone to serious biases in regions where the action of the microbial loop is significant. The basic parameterization shows some promise in addressing the problem but a more sophisticated parameterization would be needed to produce reliable estimates. Some recommendations are made for future research

Reducing Model Complexity for DNN Based Large-Scale Audio Classification

Audio classification is the task of identifying the sound categories that are associated with a given audio signal. This paper presents an investigation on large-scale audio classification based on the recently released AudioSet database. AudioSet comprises 2 millions of audio samples from YouTube, which are human-annotated with 527 sound category labels. Audio classification experiments with the balanced training set and the evaluation set of AudioSet are carried out by applying different types of neural network models. The classification performance and the model complexity of these models are compared and analyzed. While the CNN models show better performance than MLP and RNN, its model complexity is relatively high and undesirable for practical use. We propose two different strategies that aim at constructing low-dimensional embedding feature extractors and hence reducing the number of model parameters. It is shown that the simplified CNN model has only 1/22 model parameters of the original model, with only a slight degradation of performance.Comment: Accepted by ICASSP 201

Model Complexity-Accuracy Trade-off for a Convolutional Neural Network

Convolutional Neural Networks(CNN) has had a great success in the recent past, because of the advent of faster GPUs and memory access. CNNs are really powerful as they learn the features from data in layers such that they exhibit the structure of the V-1 features of the human brain. A huge bottleneck, in this case, is that CNNs are very large and have a very high memory footprint, and hence they cannot be employed on devices with limited storage such as mobile phone, IoT etc. In this work, we study the model complexity versus accuracy trade-off on MNSIT dataset, and give a concrete framework for handling such a problem, given the worst case accuracy that a system can tolerate. In our work, we reduce the model complexity by 236 times, and memory footprint by 19.5 times compared to the base model while achieving worst case accuracy threshold

Evaluation of an evaluation list for model complexity

This study (‘WOt-werkdocument’) builds on the project ‘Evaluation model complexity’, in which a list has been developed to assess the ‘equilibrium’ of a model or database. This list compares the complexity of a model or database with the availability and quality of data and the application area. A model or database is said to be in equilibrium if the uncertainty in the predictions by the model or database is appropriately small for the intended application, while the data availability supports this complexity. In this study the prototype of the list is reviewed and tested by applying it to test cases. The review has been performed by modelling experts from within and outside Wageningen University & Research centre (Wageningen UR). The test cases have been selected form the scientific literature in order to evaluate the various elements of the list. The results are used to update the list to a new version

Quantifying Model Complexity via Functional Decomposition for Better Post-Hoc Interpretability

Post-hoc model-agnostic interpretation methods such as partial dependence plots can be employed to interpret complex machine learning models. While these interpretation methods can be applied regardless of model complexity, they can produce misleading and verbose results if the model is too complex, especially w.r.t. feature interactions. To quantify the complexity of arbitrary machine learning models, we propose model-agnostic complexity measures based on functional decomposition: number of features used, interaction strength and main effect complexity. We show that post-hoc interpretation of models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity

Cadmium transport in sediments by tubificid bioturbation: An assessment of model complexity

Biogeochemistry of metals in aquatic sediments is strongly influenced by bioturbation. To determine the effects of biological transport on cadmium distribution in freshwater sediments, a bioturbation model is explored that describes the conveyor-belt feeding of tubificid oligochaetes. A stepwise modelling strategy was adopted to constrain the many parameters of the model: (i) the tubificid transport model was first calibrated on four sets of microspheres (inert solid tracer) profiles to constrain tubificid transport; (ii) the resulting transport coefficients were subsequently applied to simulate the distribution of both particulate and dissolved cadmium. Firstly, these simulations provide quantitative insight into the mechanism of tubificid bioturbation. Values of transport coefficients compare very well with the literature, and based on this, a generic model of tubificid bioturbation is proposed. Secondly, the application of the model to cadmium dataset sheds a light on the behaviour of cadmium under tubificid bioturbation. Cadmium enters the sediment in two ways. In one pathway, cadmium enters the sediment in the dissolved phase, is rapidly absorbed onto solid particles, which are then rapidly transported to depth by the tubificids. In the other pathway, cadmium is adsorbed to particles in suspension in the overlying water, which then settle on the sediment surface, and are transported downwards by bioturbation. In a final step, we assessed the optimal model complexity for the present dataset. To this end, the two-phase conveyor-belt model was compared to two simplified versions. A solid phase-only conveyorbelt model also provides good results: the dissolved phase should not be explicitly incorporated because cadmium adsorption is fast and bioirrigation is weak. Yet, a solid phase-only biodiffusive model does not perform adequately, as it does not mechanistically capture the conveyor-belt transport at short time-scales

Model Complexity of Program Phases

In resource limited computing systems, sequence prediction models must operate under tight constraints. Various models are available that cater to prediction under these conditions that in some way focus on reducing the cost of implementation. These resource constrained sequence prediction models, in practice, exhibit a fundamental tradeoff between the cost of implementation and the quality of its predictions. This fundamental tradeoff seems to be largely unexplored for models for different tasks. Here we formulate the necessary theory and an associated empirical procedure to explore this tradeoff space for a particular family of machine learning models such as deep neural networks. We anticipate that the knowledge of the behavior of this tradeoff may be beneficial in understanding the theoretical and practical limits of creation and deployment of models for resource constrained tasks