Distributed Machine Learning Framework: New Algorithms and Theoretical Foundation

Machine learning is gaining fresh momentum, and has helped us to enhance not only many industrial and professional processes but also our everyday living. The recent success of machine learning relies heavily on the surge of big data, big models, and big computing. However, inefficient algorithms restrict the applications of machine learning to big data mining tasks. In terms of big data, serious concerns, such as communication overhead and data privacy, should be rigorously addressed when we train models using large amounts of data located on multiple devices. In terms of the big model, it is still an underexplored research area if a model is too big to train on a single device. To address these challenging problems, this thesis is focusing on designing new large-scale machine learning models, efficiently optimizing and training methods for big data mining, and studying new discoveries in both theory and applications. For the challenges raised by big data, we proposed several new asynchronous distributed stochastic gradient descent or coordinate descent methods for efficiently solving convex and non-convex problems. We also designed new large-batch training methods for deep learning models to reduce the computation time significantly with better generalization performance. For the challenges raised by the big model, We scaled up the deep learning models by parallelizing the layer-wise computations with a theoretical guarantee, which is the first algorithm breaking the lock of backpropagation such that the large model can be dramatically accelerated

Ego-Downward and Ambient Video based Person Location Association

Using an ego-centric camera to do localization and tracking is highly needed for urban navigation and indoor assistive system when GPS is not available or not accurate enough. The traditional hand-designed feature tracking and estimation approach would fail without visible features. Recently, there are several works exploring to use context features to do localization. However, all of these suffer severe accuracy loss if given no visual context information. To provide a possible solution to this problem, this paper proposes a camera system with both ego-downward and third-static view to perform localization and tracking in a learning approach. Besides, we also proposed a novel action and motion verification model for cross-view verification and localization. We performed comparative experiments based on our collected dataset which considers the same dressing, gender, and background diversity. Results indicate that the proposed model can achieve $18.32 \%$ improvement in accuracy performance. Eventually, we tested the model on multi-people scenarios and obtained an average $67.767 \%$ accuracy

Exploit Where Optimizer Explores via Residuals

In order to train the neural networks faster, many efforts have been devoted to exploring a better solution trajectory, but few have been put into exploiting the existing solution trajectory. To exploit the trajectory of (momentum) stochastic gradient descent (SGD(m)) method, we propose a novel method named SGD(m) with residuals (RSGD(m)), which leads to a performance boost of both the convergence and generalization. Our new method can also be applied to other optimizers such as ASGD and Adam. We provide theoretical analysis to show that RSGD achieves a smaller growth rate of the generalization error and the same (but empirically better) convergence rate compared with SGD. Extensive deep learning experiments on image classification, language modeling and graph convolutional neural networks show that the proposed algorithm is faster than SGD(m)/Adam at the initial training stage, and similar to or better than SGD(m) at the end of training with better generalization error