This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger state-of-the-art prediction models for autonomous driving continue to emerge, an important open challenge is the problem of how to combine the strengths of these big models without the need for costly re-training. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (without retraining or fine-tuning) with a simple confidence-weighted average method can enhance the overall prediction. Indeed, while combining trajectory prediction models is not straightforward, this simple approach enhances performance by 10% over the best prediction model, especially in the long-tailed metrics. We show that this performance improvement holds on both the NuScenes and Argoverse datasets, and that these improvements are made across the dataset distribution. The code for our work is open source.

In the world of analytics,modeling is a general term used to refer to the use of data mining (machine learning) methods to develop predictions. If you want to know what ad a particular user is more likely to click on, or which customers are likely to leave you for a competitor, you develop a predictive model. There are a lot of models to choose from: Regression, Decision Trees, K Nearest Neighbor, Neural Nets, etc. They all will provide you with a prediction, but some will do better than others depending on the data you are working with. While there are certain tricks and tweaks one can do to improve the accuracy of these models, it never hurts to remember the fact that there is wisdom to be found in the masses.

Meteorology has always grappled with the problem of big data. Due to the multivariate and chaotic ... [ ] nature of weather, for more than half of a century meteorologists have dealt with terabytes of data and modeling variables to produce an accurate forecast. Today, we are still processing data – now on the scale of petabytes – thanks to the Internet of Things, more sensors, and ensemble modeling. There is much discussion about artificial intelligence and the benefits of its application from dating, marketing and social media to space exploration and medical advances. There isn't an industry that hasn't been affected by this dynamic tool, including weather.

While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions with no gauges, or predictions in ungauged regions (PUR) problems. However, softer data such as the flow duration curve (FDC) may be already available from nearby stations, or may become available. Here we demonstrate that sparse FDC data can be migrated and assimilated by an LSTM-based network, via an encoder. A stringent region-based holdout test showed a median Kling-Gupta efficiency (KGE) of 0.62 for a US dataset, substantially higher than previous state-of-the-art global-scale ungauged basin tests. The baseline model without FDC was already competitive (median KGE 0.56), but integrating FDCs had substantial value. Because of the inaccurate representation of inputs, the baseline models might sometimes produce catastrophic results. However, model generalizability was further meaningfully improved by compiling an ensemble based on models with different input selections.

Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.

Markets are, in my view, mostly random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation - transaction costs and overhead can easily exceed the expected profits offered. But when we are able to combine many such small edges together, the rewards can be great. In this article, I'll present a framework for blending together outputs from multiple models using a type of ensemble modeling known as stacked generalization. This approach excels at creating models which "generalize" well to unknown future data, making them an excellent choice for the financial domain, where overfitting to past data is a major challenge.

Markets are, in my view, mostly random. Many small inefficiencies and patterns exist in markets which can be identified and used to gain slight edge on the market. These edges are rarely large enough to trade in isolation - transaction costs and overhead can easily exceed the expected profits offered. But when we are able to combine many such small edges together, the rewards can be great. In this article, I'll present a framework for blending together outputs from multiple models using a type of ensemble modeling known as stacked generalization. This approach excels at creating models which "generalize" well to unknown future data, making them an excellent choice for the financial domain, where overfitting to past data is a major challenge.

With all the hype around artificial intelligence, many of our customers are asking for some proof that AI can get them better results in areas where other kinds of analytics are already in use, such as credit risk assessment. With 25 years of experience with AI and machine learning under our belt, we can certainly provide that proof. My colleague Scott Zoldi blogged recently about how we use AI to build credit risk models. In this post, I'd like to drill into one of the examples he gave, to show some of the explorations we're doing to make sure we get the full power of machine learning without losing the transparency that's important in the credit risk arena. A traditional credit risk scorecard model generates a score reflecting probability of default, using various customer characteristics as inputs to the model.

Big data analytics is gaining massive momentum in the last few years. Applying machine learning models to big data has become an implicit requirement or an expectation for most analysis tasks, especially on high-stakes applications.Typical applications include sentiment analysis against reviews for analyzing on-line products, image classification in food logging applications for monitoring user's daily intake and stock movement prediction. Extending traditional database systems to support the above analysis is intriguing but challenging. First, it is almost impossible to implement all machine learning models in the database engines. Second, expertise knowledge is required to optimize the training and inference procedures in terms of efficiency and effectiveness, which imposes heavy burden on the system users. In this paper, we develop and present a system, called Rafiki, to provide the training and inference service of machine learning models, and facilitate complex analytics on top of cloud platforms. Rafiki provides distributed hyper-parameter tuning for the training service, and online ensemble modeling for the inference service which trades off between latency and accuracy. Experimental results confirm the efficiency, effectiveness, scalability and usability of Rafiki.