Deep learning has improved state-of-the-art results in many important fields, and has been the subject of much research in recent years, leading to the development of several systems for facilitating deep learning. Current systems, however, mainly focus on model building and training phases, while the issues of data management, model sharing, and lifecycle management are largely ignored. Deep learning modeling lifecycle generates a rich set of data artifacts, such as learned parameters and training logs, and comprises of several frequently conducted tasks, e.g., to understand the model behaviors and to try out new models. Dealing with such artifacts and tasks is cumbersome and largely left to the users. This paper describes our vision and implementation of a data and lifecycle management system for deep learning. First, we generalize model exploration and model enumeration queries from commonly conducted tasks by deep learning modelers, and propose a high-level domain specific language (DSL), inspired by SQL, to raise the abstraction level and accelerate the modeling process. To manage the data artifacts, especially the large amount of checkpointed float parameters, we design a novel model versioning system (dlv), and a read-optimized parameter archival storage system (PAS) that minimizes storage footprint and accelerates query workloads without losing accuracy. PAS archives versioned models using deltas in a multi-resolution fashion by separately storing the less significant bits, and features a novel progressive query (inference) evaluation algorithm. Third, we show that archiving versioned models using deltas poses a new dataset versioning problem and we develop efficient algorithms for solving it. We conduct extensive experiments over several real datasets from computer vision domain to show the efficiency of the proposed techniques.
Stability Analysis Results Stability analysis of is studied in the literature @cite_5 @cite_30 @cite_10 , where the problem setting is perturbation analysis regardless specific data, rather focus on statistical measures of stability. uses basic perturbation analysis techniques and focus on novel progressive query answering in a segmented float storage.
@cite_30 @cite_5 @cite_10
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Many scientific and economic problems involve the analysis of highdimensional time series datasets. However, theoretical studies in highdimensional statistics to date rely primarily on the assumption of independent and identically distributed (i.i.d.) samples. In this work, we focus on stable Gaussian processes and investigate the theoretical properties of � 1-regularized estimates in two important statistical problems in the context of high-dimensional time series: (a) stochastic regression with serially correlated errors and (b) transition matrix estimation in vector autoregressive (VAR) models. We derive nonasymptotic upper bounds on the estimation errors of the regularized estimates and establish that consistent estimation under high-dimensional scaling is possible via � 1-regularization for a large class of stable processes under sparsity constraints. A key technical contribution of the work is to introduce a measure of stability for stationary processes using their spectral properties that provides insight into the effect of dependence on the accuracy of the regularized estimates. With this proposed stability measure, we establish some useful deviation bounds for dependent data, which can be used to study several important regularized estimates in a time series setting. Abstract Several robustness problems such as stability and performance robustness analysis of feedback systems and robust design of control systems in the presence of mixed nonlinear parametric and nonparametric perturbations can be solved by means of algorithms based on interval-arithmetic computation. Some of the main algorithms available in the literature are presented, and their efficiency is tested on some examples of robustness analysis and design of control systems. © 1997 Elsevier Science Ltd. We consider the model introduced by Bilu and Linial (2010), who study problems for which the optimal clustering does not change when distances are perturbed. They show that even when a problem is NP-hard, it is sometimes possible to obtain efficient algorithms for instances resilient to certain multiplicative perturbations, e.g. on the order of @math for max-cut clustering. (2010) consider center-based objectives, and Balcan and Liang (2011) analyze the @math -median and min-sum objectives, giving efficient algorithms for instances resilient to certain constant multiplicative perturbations. Here, we are motivated by the question of to what extent these assumptions can be relaxed while allowing for efficient algorithms. We show there is little room to improve these results by giving NP-hardness lower bounds for both the @math -median and min-sum objectives. On the other hand, we show that constant multiplicative resilience parameters can be so strong as to make the clustering problem trivial, leaving only a narrow range of resilience parameters for which clustering is interesting. We also consider a model of additive perturbations and give a correspondence between additive and multiplicative notions of stability. Our results provide a close examination of the consequences of assuming stability in data. To extract reasonable support relations from "RGB+depth" (RGBD) images, it is very important to achieve good scene understanding. This paper proposes a novel approach to extracting accurate support relationships by analyzing the RGBD images of indoor scenes. Noting that the support relations and structure classes of indoor images are inherently related to physical stability, we construct an improved energy function that embodies this stability. We then infer the support relations and structure classes from indoor RGBD images by minimizing this energy function. Moreover, the authors succeed in improving the segmentation quality of RGBD images using the inferred results as input. Compared with previous methods, our approach produces more reasonable support relations and structure classes, where physical stability function is taken into account for resolving the optimization problem. We use the NYU-Depth2 dataset as the training data, and experimental results show that the proposed RGBD image segmentation method based on support relation abstraction produces more accurate results than segmentation methods based on ground-truth support relations.