Recently me and my team shifted our approach to build our models in the docker containers rather than creating and running a python file on the system. For me, it was a completely new experience in learning docker. If you are also quite new to docker then with this post you will be able to create your own basic deep-learning model in docker. Docker provides an image-based deployment model. This makes it easy to share an application, with all of their dependencies across multiple environments.

Recent years have witnessed the success of deep neural networks in many research areas. The fundamental idea behind the design of most neural networks is to learn similarity patterns from data for prediction and inference, which lacks the ability of cognitive reasoning. However, the concrete ability of reasoning is critical to many theoretical and practical problems. On the other hand, traditional symbolic reasoning methods do well in making logical inference, but they are mostly hard rule-based reasoning, which limits their generalization ability to different tasks since difference tasks may require different rules. Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs. In this paper, we propose Logic-Integrated Neural Network (LINN) to integrate the power of deep learning and logic reasoning. LINN is a dynamic neural architecture that builds the computational graph according to input logical expressions. It learns basic logical operations such as AND, OR, NOT as neural modules, and conducts propositional logical reasoning through the network for inference. Experiments on theoretical task show that LINN achieves significant performance on solving logical equations and variables. Furthermore, we test our approach on the practical task of recommendation by formulating the task into a logical inference problem. Experiments show that LINN significantly outperforms state-of-the-art recommendation models in Top-K recommendation, which verifies the potential of LINN in practice.

Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of studies have been performed on detect correlation between a drug and an AE, limited studies have been conducted on personalized ADE risk prediction. Among treatment alternatives, avoiding the drug that has high likelihood of causing severe AE can help physicians to provide safer treatment to patients. Existing work on personalized ADE risk prediction uses the information obtained in the current medical visit. However, on the other hand, medical history reveals each patient's unique characteristics and comprehensive medical information. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claims codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that capture characteristics of claim codes and their relationship. The empirical evaluation show that the proposed HTNNR model substantially outperforms the comparison methods, especially for rare drugs.

For many applications, utilizing Deep Neural Networks (DNNs) requires their implementation on a target architecture in an optimized manner concerning energy consumption, memory requirement, throughput, etc. DNN compression is used to reduce the memory footprint and complexity of a DNN before its deployment on hardware. Recent efforts to understand and explain AI (Artificial Intelligence) methods have led to a new research area, termed as explainable AI. Explainable AI methods allow us to better understand the inner working of DNNs, such as the importance of different neurons and features. The concepts from explainable AI provide an opportunity to improve DNN compression methods such as quantization and pruning in several ways that have not been sufficiently explored so far. In this paper, we utilize explainable AI methods: mainly DeepLIFT method. We use DeepLIFT for (1) pruning of DNNs; this includes structured and unstructured pruning of Convolutional Neural Network (CNN) filters pruning as well as pruning weights of fully connected layers, (2) nonuniform quantization of DNN weights using clustering algorithm; this is also referred to as Weight Sharing, and (3) integer-based mixed-precision quantization; this is where each layer of a DNN may use a different number of integer bits. We use typical image classification datasets with common deep learning image classification models for evaluation. In all these three cases, we demonstrate significant improvements as well as new insights and opportunities from the use of explainable AI in DNN compression.

In biological learning, data are used to improve performance simultaneously on the current task, as well as previously encountered and as yet unencountered tasks. In contrast, classical machine learning starts from a blank slate, or tabula rasa, using data only for the single task at hand. While typical transfer learning algorithms can improve performance on future tasks, their performance on prior tasks degrades upon learning new tasks (called catastrophic forgetting). Many recent approaches have attempted to maintain performance given new tasks. But striving to avoid forgetting sets the goal unnecessarily low: the goal of progressive learning, whether biological or artificial, is to improve performance on all tasks (including past and future) with any new data. We propose representation ensembling, as opposed to learner ensembling (e.g., bagging), to address progressive learning. We show that representation ensembling -- including representations learned by decision forests or deep network -- uniquely demonstrates improved performance on both past and future tasks in a variety of simulated and real data scenarios, including vision, language, and adversarial tasks, with or without resource constraints. Beyond progressive learning, this work has immediate implications with regards to mitigating batch effects and federated learning applications. We expect a deeper understanding of the mechanisms underlying biological progressive learning to enable further improvements in machine progressive learning.

During the past decade, Artificial Intelligence (AI), and by extension Machine Learning (ML), have seen an unprecedented rise in both industry and research. The progressive improvement of computer hardware associated with the need to process larger and larger amounts of data made these underestimated techniques shine under a new light. Reinforcement Learning (RL) focuses on learning how to map situations to actions, in order to maximize a numerical reward signal [102]. The learner is not told which actions to take, but instead must discover which actions are the most rewarding by trying them. Reinforcement learning addresses the problem of how agents should learn a policy that take actions to maximize the cumulative reward through interaction with the environment [31]. Recent progress in Deep Learning (DL) for learning feature representations has significantly impacted RL, and the combination of both methods (known as deep RL) has led to remarkable results in a lot of areas. Typically, RL is used to solve optimisation problems when the system has a very large number of states and has a complex stochastic structure. Notable examples include training agents to play Atari games based on raw pixels [75, 76], board games [96, 97], complex real-world robotics problems such as manipulation [8] or grasping [54] and other real-world applications such as resource management in computer clusters [72], network traffic signal control [9], chemical reactions optimization [117] or recommendation systems [116].

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further.

Biofouling is the accumulation of organisms on surfaces immersed in water. It is of particular concern to the international shipping industry because fouling increases the drag on vessels as they move through the water, resulting in higher fuel costs, and presents a biosecurity risk by providing a pathway for marine non-indigenous species (NIS) to establish in new areas. There is growing interest within jurisdictions to strengthen biofouling risk-management regulations, but it is expensive to conduct in-water inspections and assess the collected data to determine the biofouling state of vessel hulls. Machine learning is well suited to tackle the latter challenge, and here we apply so-called deep learning to automate the classification of images from in-water inspections for the presence and severity of biofouling. We combined images collected from in-water surveys conducted by the Australian Department of Agriculture, Water and the Environment, the New Zealand Ministry for Primary Industries and the California State Lands Commission, and annotated them using the Amazon Mechanical Turk (MTurk) crowdsourcing platform. We compared the annotations from three biofouling experts on a 120-sample subset of these images, and found that for two tasks, identifying images containing fouling, and identifying images containing heavy fouling, they showed 89% agreement (95% CI: 87-92%). It was found that the MTurk labelling approach achieved similar agreement with experts, which we defined as performing at most 5% worse than experts (p=0.004-0.020). Our deep learning model trained with the MTurk annotations also showed reasonable performance in comparison to expert agreement, although at a lower significance level (p=0.071-0.093). We also demonstrate that significantly better performance than expert agreement can be achieved if a classifier with high recall or precision was required.

As deep learning models are usually massive and complex, distributed learning is essential for increasing training efficiency. Moreover, in many real-world application scenarios like healthcare, distributed learning can also keep the data local and protect privacy. A popular distributed learning strategy is federated learning, where there is a central server storing the global model and a set of local computing nodes updating the model parameters with their corresponding data. The updated model parameters will be processed and transmitted to the central server, which leads to heavy communication costs. Recently, asynchronous decentralized distributed learning has been proposed and demonstrated to be a more efficient and practical strategy where there is no central server, so that each computing node only communicates with its neighbors. Although no raw data will be transmitted across different local nodes, there is still a risk of information leak during the communication process for malicious participants to make attacks. In this paper, we present a differentially private version of asynchronous decentralized parallel SGD (ADPSGD) framework, or A(DP)$^2$SGD for short, which maintains communication efficiency of ADPSGD and prevents the inference from malicious participants. Specifically, R{\'e}nyi differential privacy is used to provide tighter privacy analysis for our composite Gaussian mechanisms while the convergence rate is consistent with the non-private version. Theoretical analysis shows A(DP)$^2$SGD also converges at the optimal $\mathcal{O}(1/\sqrt{T})$ rate as SGD. Empirically, A(DP)$^2$SGD achieves comparable model accuracy as the differentially private version of Synchronous SGD (SSGD) but runs much faster than SSGD in heterogeneous computing environments.

For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy. Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the invertibility of raw data from intermediary representations. This can prevent client entities that hold sensitive data from using distributed deep learning services. We demonstrate that our method is resilient to such reconstruction attacks and is based on reduction of distance correlation between raw data and learned representations during training and inference with image datasets. We prevent such reconstruction of raw data while maintaining information required to sustain good classification accuracies.