Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss

Recent works on deep conditional random fields (CRF) have set new records on many vision tasks involving structured predictions. Here we propose a fully-connected deep continuous CRF model for both discrete and continuous labelling problems. We exemplify the usefulness of the proposed model on multi-class semantic labelling (discrete) and the robust depth estimation (continuous) problems. In our framework, we model both the unary and the pairwise potential functions as deep convolutional neural networks (CNN), which are jointly learned in an end-to-end fashion. The proposed method possesses the main advantage of continuously-valued CRF, which is a closed-form solution for the Maximum a posteriori (MAP) inference. To better adapt to different tasks, instead of using the commonly employed maximum likelihood CRF parameter learning protocol, we propose task-specific loss functions for learning the CRF parameters. It enables direct optimization of the quality of the MAP estimates during the course of learning. Specifically, we optimize the multi-class classification loss for the semantic labelling task and the Turkey's biweight loss for the robust depth estimation problem. Experimental results on the semantic labelling and robust depth estimation tasks demonstrate that the proposed method compare favorably against both baseline and state-of-the-art methods. In particular, we show that although the proposed deep CRF model is continuously valued, with the equipment of task-specific loss, it achieves impressive results even on discrete labelling tasks

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Veblen goods and neighbourhoods: endogenising consumption reference groups

One of the significant developments in the last four decades of economics is the growing empirical evidence that individual consumption preferences, as mea- sured by self-reported life satisfaction, are neither fixed nor self-centred but are instead overwhelmingly dominated by externalities, partly in the form of reference levels set by others and by one’s own experience. Welfare analysis recognising this fact is likely to indicate enormous revisions for macroeconomic policy and social objectives as well as for what is taught in economics at all levels. Yet the task of constructing general equilibrium models based on this microeconomic re- ality is still in its infancy. In this work I take the conventional stance that decision makers understand their own utility function. Therefore, they can choose the mi- lieu in which they immerse themselves with the sophisticated understanding that it will affect their own consumption reference levels and therefore the degree of satisfaction they derive from their private consumption. At the same time, their private consumption will help to set the reference level for others in their chosen group. I treat theoretically the problem of such endogenous formation of consump- tion reference groups in the context of a simultaneous choice of neighbourhoods and home consumption amongst a heterogenous population. For both discrete and continuous distributions of types, I find general equilibrium outcomes in which differentiation of neighbourhoods occurs endogenously and I compare the welfare implications of growth in such economies.reference income; veblen goods; consumption reference groups; club goods

Face Detection with the Faster R-CNN

The Faster R-CNN has recently demonstrated impressive results on various object detection benchmarks. By training a Faster R-CNN model on the large scale WIDER face dataset, we report state-of-the-art results on two widely used face detection benchmarks, FDDB and the recently released IJB-A.Comment: technical repor