The last decade has witnessed a great success of deep learning in the domain of computer vision, natural language processing, robotics, etc. As the deep neural network is notoriously over-confident, an accurate estimation of uncertainty is important to avoid making confident mistakes and improve the robustness of the learning system. This is especially crucial when deploying the deep models in an embedded system, e.g., mobile phone, intelligent driving assistant, edge server, where a small mistake would cause intolerable fatality. As these systems have scarce computational resources, how to improve the efficiency of bulky uncertainty estimation is a challenging problem.

In this paper, we first present an application related to the active learning technique where the predictive uncertainty is important. Identifying rare categories is an important data management problem in many application fields including video surveillance, ecological environment monitoring and precision medicine. Previous solutions in literature require all data instances to be first delivered to the server. Then, the rare categories identification algorithms are executed on the pool of data to find informative instances for human annotators to label. This incurs large bandwidth consumption and high latency. To deal with the problems, we propose a light-weight rare categories identification framework. At the sensor side, the designed online algorithm filters the less informative data instances from the data stream and only sends the informative ones to human annotators. After labeling, the server only sends labels of the corresponding data instances in response. The sensor-side algorithm is extended to enable cooperation between embedded devices for the cases that data is collected in a distributed manner. Experiments are conducted to show our framework dramatically outperforms the baseline. The network traffic is reduced by 75% on average.

Secondly, we present a method that boosts the efficiency of predictive uncertainty estimation. Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting the adversarial attack, self-driving car. As the model posterior is almost always intractable, most efforts were made on finding an accurate approximation to the true posterior. Even though a decent estimation of the model posterior is obtained, another approximation is required to compute the predictive distribution over the desired output. A common accurate solution is to use Monte Carlo (MC) integration. However, it needs to maintain a large number of samples, evaluate the model repeatedly and average multiple model outputs. In many real-world cases, this is computationally prohibitive. In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by the model posterior with a parameterized model and in an amortized fashion. The aim is to approximate the predictive uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration at testing time. The proposed method is universally applicable to Bayesian classification models that allow for posterior sampling. Theoretically, we show that the idea of amortization incurs no additional costs on approximation performance. Empirical results validate the strong practical performance of our approach.

Thirdly, we present a method to generate a nested neural network that can switch the width for deployment on different resource limited platforms. Neural network compression and quantization are important tasks for fitting state-of-the-art models into the computational, memory and power constraints of mobile devices and embedded hardware. Recent approaches to model compression/quantization are based on reinforcement learning or search methods to compress/quantize the neural network for a specific hardware platform. However, these methods require multiple runs to compress/quantize the same base neural network to different hardware setups. In this work, we propose a fully nested neural network (FN3) that runs only once to build a nested set of compressed/quantized models, which is optimal for different resource constraints. Specifically, we exploit the additive characteristic in different levels of building blocks in neural network and propose an ordered dropout (ODO) operation that ranks the building blocks. Given a trained FN³, a fast heuristic search algorithm is run offline to find the optimal removal of components to maximize the accuracy under different constraints. Compared with the related works on adaptive neural network designed only for channels or bits, the proposed approach is unified for different levels of building blocks (bits, neurons, channels, residual paths and layers). Empirical results validate strong practical performance of the proposed approach.

Lastly, we improve the performance and uncertainty estimation of nested neural network by reformulate the problem under a Bayesian learning framework. Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of sub-networks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyper-parameter over different layers during the whole training process. Therefore, when nodes are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. Another drawback is the generated sub-networks are deterministic networks without well-calibrated uncertainty. To address these two problems, we develop a Bayesian approach to nested neural networks. We propose a variational ordering unit that draws samples for nested dropout at a low cost, from a proposed Downhill distribution, which provides useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the node distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related approach on uncertainty-critical tasks in computer vision.