Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting has not yet been fully exploited in terms of the predictive power of spatial-temporal data and the capability of handling missing data. In this paper, we focus on RNN-based models and attempt to reformulate the way to incorporate RNN and its variants into traffic prediction models. A stacked bidirectional and unidirectional LSTM network architecture (SBU-LSTM) is proposed to assist the design of neural network structures for traffic state forecasting. As a key component of the architecture, the bidirectional LSTM (BDLSM) is exploited to capture the forward and backward temporal dependencies in spatiotemporal data. To deal with missing values in spatial-temporal data, we also propose a data imputation mechanism in the LSTM structure (LSTM-I) by designing an imputation unit to infer missing values and assist traffic prediction. The bidirectional version of LSTM-I is incorporated in the SBU-LSTM architecture. Two real-world network-wide traffic state datasets are used to conduct experiments and published to facilitate further traffic prediction research. The prediction performance of multiple types of multi-layer LSTM or BDLSTM models is evaluated. Experimental results indicate that the proposed SBU-LSTM architecture, especially the two-layer BDLSTM network, can achieve superior performance for the network-wide traffic prediction in both accuracy and robustness. Further, comprehensive comparison results show that the proposed data imputation mechanism in the RNN-based models can achieve outstanding prediction performance when the model's input data contains different patterns of missing values.

Of recent interest in the deep learning community, generative models have proved to be powerful tools for many tasks including synthetic data generation and style transfer [1]. Geometric deep learning is a new field interested in extending such success of deep learning to non-Euclidean structured data [2]. The development of this field is timely given the recent proliferation of point cloud and mesh structured data obtained from sources such as laserscanners [3] and CAD software [4]. Particularly, mesh based convolutional autoencoders (MeshVAEs) are now a popular tool for generating surfaces [5, 6, 7, 8]. These models process a surface via geometric convolutions that respect its intrinsic geometry. With these VAEs achieving state-of-the-art performance on tasks such as reconstruction, more attention is being given towards tasks such as latent space interpretability. Geometric disentanglement, where the latent variables controlling intrinsic (properties independent of surface embedding) and extrinsic (properties dependent on surface embedding) geometry are separated [9], is an important open problem related to such interpretability. Applications include graphics, where a typical example is a disentangled latent space separating identity and pose in the case of human body generation [10, 11].

A deep-learning based hybrid strategy for short-term load forecasting is presented. The strategy proposes a novel tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB). Current strategies either ensemble submodels of a single type, which fail to take advantage of statistical strengths of different inference models. Or they simply sum the outputs from completely different inference models, which doesn't maximize the potential of ensemble. WGTB is thus proposed and tailored to the great disparity among different inference models in accuracy, volatility and linearity. The complete strategy integrates four different inference models (i.e., auto-regressive integrated moving average, nu support vector regression, extreme learning machine and long short-term memory neural network), both linear and nonlinear models. WGTB then ensembles their outputs by hybridizing linear estimator ElasticNet and nonlinear estimator ExtraTree via boosting algorithm. It is validated on the real historical data of a grid from State Grid Corporation of China of hourly resolution. The result demonstrates the effectiveness of the proposed strategy that hybridizes statistical strengths of both linear and nonlinear inference models.

In a data-scarce field such as healthcare, where models often deliver predictions on patients with rare conditions, the ability to measure the uncertainty of a model's prediction could potentially lead to improved effectiveness of decision support tools and increased user trust. This work advances the understanding of uncertainty estimation for classification and risk prediction on medical tabular data, in a two-fold way. First, we expand and refine the set of heuristics to select an uncertainty estimation technique, introducing tests for clinically-relevant scenarios such as generalization to uncommon pathologies, changes in clinical protocol and simulations of corrupted data. We furthermore differentiate these heuristics depending on the clinical use-case. Second, we observe that ensembles and related techniques perform poorly when it comes to detecting out-of-domain examples, a critical task which is carried out more successfully by auto-encoders. These remarks are enriched by considerations of the interplay of uncertainty estimation with class imbalance, post-modeling calibration and other modeling procedures. Our findings are supported by an array of experiments on toy and real-world data.

This article presents the development process of a Machine Learning model to gain understanding from Digital Magnetic Resonance Images (MRI) of the Human Knee and label the corresponding pixels of the image to the Tibia bone, using a Deep Learning network and image segmentation. Deep Convolutional networks have outperformed the state of the art in many visual recognition tasks, the image semantic segmentation challenge consists in classifying each pixel of an image into an instance corresponding to an object or a part of the image. The data set used, consisting of a total of 90 cases of the Human knee medical images, also known as Magnetic resonance Imaging MRI. Each case consists of a set of 160 medical images of the knee in format type Digital Imaging and Communications in Medicine or DICOM. In order to extract the area of interest in each DICOM image, the Tibia bone was labeled with a software called BML BaseLine, this software is used to mark the bounds of the bone on each DICOM image for each case.

The virtual International Conference on Learning Representations (ICLR) was held on 26-30 April and included eight keynote talks. Courtesy of the conference organisers you can watch the talks in full and see the question and answer sessions. The aim of Mihaela's research is to contribute to the transformation of healthcare by rigorous formulation and development of diverse new tools in machine learning and AI. Her group has worked on many problems in medicine and healthcare, including risk prognosis, modelling disease trajectories, adaptive clinical trials, individualised treatment, early-warning systems in hospitals, and personalised screening. They needed to develop a variety of machine learning methods to carry out this work.

It contains lots of pre-trained machine learning models that data scientists use rather than creating their own models. Obviously, it depends on what ML model you need to use. If you are looking for something very specific for your intent, maybe it's better to create your own model. Theano uses NumPy's syntax to optimize and evaluate mathematical expressions. It uses the GPU to speed up its processes.

There is a big step from machine learning to Deep Learning (DL) in that DL requires much more data than ML.

Intra-driver and inter-driver heterogeneity has been confirmed to exist in human driving behaviors by many studies. In this study, a joint model of the two types of heterogeneity in car-following behavior is proposed as an approach of driver profiling and identification. It is assumed that all drivers share a pool of driver states; under each state a car-following data sequence obeys a specific probability distribution in feature space; each driver has his/her own probability distribution over the states, called driver profile, which characterize the intradriver heterogeneity, while the difference between the driver profile of different drivers depict the inter-driver heterogeneity. Thus, the driver profile can be used to distinguish a driver from others. Based on the assumption, a stochastic car-following model is proposed to take both intra-driver and inter-driver heterogeneity into consideration, and a method is proposed to jointly learn parameters in behavioral feature extractor, driver states and driver profiles. Experiments demonstrate the performance of the proposed method in driver identification on naturalistic car-following data: accuracy of 82.3% is achieved in an 8-driver experiment using 10 car-following sequences of duration 15 seconds for online inference. The potential of fast registration of new drivers are demonstrated and discussed.

In order to keep trace of information, the brain has to resolve the problem where information is and how to index new ones. We propose that the neural mechanism used by the prefrontal cortex (PFC) to detect structure in temporal sequences, based on the temporal order of incoming information, has served as second purpose to the spatial ordering and indexing of brain networks. We call this process, apparent to the manipulation of neural 'addresses' to organize the brain's own network, the 'digitalization' of information. Such tool is important for information processing and preservation, but also for memory formation and retrieval.