Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We deploy CNN networks for predicting out-of-sample stock movements for 200 high-volume European stocks from 1994 until 2014, and compare its overall performance with a modified LSTM model as in Fischer, Krauss (2017). Specifically, we compare empirical training and validation accuracies of both model architectures and reveal portfolio performance characteristics in terms of return and risk metrics for different portfolio sizes, trying to derive common patterns within the top and flop stocks. Thus, we unveil sources of long-term profitability and demonstrate, that both LSTM and CNN networks are able to extract meaningful information from such noisy financial time series.

While deep learning has shown promise in the domain of disease classification from medical images, models based on state-of-the-art convolutional neural network architectures often exhibit performance loss due to dataset shift. Models trained using data from one hospital system achieve high predictive performance when tested on data from the same hospital, but perform significantly worse when they are tested in different hospital systems. Furthermore, even within a given hospital system, deep learning models have been shown to depend on hospital- and patient-level confounders rather than meaningful pathology to make classifications. In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classification, and that they will work well even when tested on images from hospitals that were not included in the training data. We attempt to address this problem in the context of pneumonia classification from chest radiographs. We propose an approach based on adversarial optimization, which allows us to learn more robust models that do not depend on confounders. Specifically, we demonstrate improved out-of-hospital generalization performance of a pneumonia classifier by training a model that is invariant to the view position of chest radiographs (anterior-posterior vs. posterior-anterior). Our approach leads to better predictive performance on external hospital data than both a standard baseline and previously proposed methods to handle confounding, and also suggests a method for identifying models that may rely on confounders. Code available at https://github.com/suinleelab/cxr_adv.

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time-series datasets.

--Retail Product Image Classification is an important Computer Vision and Machine Learning problem for building real world systems like self-checkout stores and automated retail execution evaluation. In this work, we present various tricks to increase accuracy of Deep Learning models on different types of retail product image classification datasets. These tricks enable us to increase the accuracy of fine tuned convnets for retail product image classification by a large margin. As the most prominent trick, we introduce a new neural network layer called Local-Concepts-Accumulation (LCA) layer which gives consistent gains across multiple datasets. Two other tricks we find to increase accuracy on retail product identification are using an instagram-pretrained Convnet and using Maximum Entropy as an auxiliary loss for classification. Retail product image classification is the problem of deciphering a retail product from its image.

We consider the problem of learning a neural network classifier. Under the information bottleneck (IB) principle, we associate with this classification problem a representation learning problem, which we call "IB learning". We show that IB learning is, in fact, equivalent to a special class of the quantization problem. The classical results in rate-distortion theory then suggest that IB learning can benefit from a "vector quantization" approach, namely, simultaneously learning the representations of multiple input objects. Such an approach assisted with some variational techniques, result in a novel learning framework, "Aggregated Learning", for classification with neural network models. In this framework, several objects are jointly classified by a single neural network. The effectiveness of this framework is verified through extensive experiments on standard image recognition and text classification tasks. Introduction The revival of neural networks in the paradigm of deep learning (LeCun, Bengio, and Hinton 2015) has stimulated intense interest in understanding the networking of deep neural networks, e.g., (Shwartz-Ziv and Tishby 2017; Zhang et al. 2017). Among various efforts, an information-theoretic approach, information bottleneck (IB) (Tishby, Pereira, and Bialek 1999) stands out as a fundamental tool to theorize the learning of deep neural networks (Shwartz-Ziv and Tishby 2017; Saxe et al. 2018; Dai et al. 2018). Under the IB principle, the core of learning a neural network classifier is to find a representation T of the input example X, that contains as much as possible the information about X and as little as possible the information about the label Y .

--This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints. A convex optimization based algorithm is provided to obtain the optimal channel assignment, where the closed-form solution is obtained in each step. Due to high computational complexity in the convex optimization based algorithm, machine learning approaches are employed to obtain computational efficient solutions. More specifically, the data are generated by using convex optimization based algorithm and the original problem is converted to a regression problem which is addressed by the integration of convolutional neural networks (CNNs), feed-forward neural networks (FNNs), random forest and gated recurrent unit networks (GRUs). The results demonstrate that the machine learning method largely reduces the computation time with slightly compromising of prediction accuracy.

An essential problem in automated machine learning (AutoML) is that of model selection. A unique challenge in the sequential setting is the fact that the optimal model itself may vary over time, depending on the distribution of features and labels available up to each point in time. In this paper, we propose a novel Bayesian optimization (BO) algorithm to tackle the challenge of model selection in this setting. This is accomplished by treating the performance at each time step as its own black-box function. In order to solve the resulting multiple black-box function optimization problem jointly and efficiently, we exploit potential correlations among black-box functions using deep kernel learning (DKL). To the best of our knowledge, we are the first to formulate the problem of stepwise model selection (SMS) for sequence prediction, and to design and demonstrate an efficient joint-learning algorithm for this purpose. Using multiple real-world datasets, we verify that our proposed method outperforms both standard BO and multi-objective BO algorithms on a variety of sequence prediction tasks.

Learning optimal policies from sparse feedback is a known challenge in reinforcement learning. Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm that comes to solve such tasks. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode and then generalizes from that virtual goal to real goals. HER has known flaws and is limited to relatively simple tasks. In this thesis, we present three algorithms based on the existing HER algorithm that improves its performances. First, we prioritize virtual goals from which the agent will learn more valuable information. We call this property the \textit{instructiveness} of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals. Secondly, we designed a filtering process that detects and removes misleading samples that may induce bias throughout the learning process. Lastly, we enable the learning of complex, sequential, tasks using a form of curriculum learning combined with HER. We call this algorithm \textit{Curriculum HER}. To test our algorithms, we built three challenging manipulation environments with sparse reward functions. Each environment has three levels of complexity. Our empirical results show vast improvement in the final success rate and sample efficiency when compared to the original HER algorithm.

This paper studies structured sparse training of CNNs with a gradual pruning technique that leads to fixed, sparse weight matrices after a set number of epochs. We simplify the structure of the enforced sparsity so that it reduces overhead caused by regularization. The proposed training methodology Campfire explores pruning at granularities within a convolutional kernel and filter. We study various tradeoffs with respect to pruning duration, level of sparsity, and learning rate configuration. We show that our method creates a sparse version of ResNet-50 and ResNet-50 v1.5 on full ImageNet while remaining within a negligible <1% margin of accuracy loss. To ensure that this type of sparse training does not harm the robustness of the network, we also demonstrate how the network behaves in the presence of adversarial attacks. Our results show that with 70% target sparsity, over 75% top-1 accuracy is achievable.

The passage of time makes wizards of us all. Today, any dullard can make bells ring across the ocean by tapping out phone numbers, cause inanimate toys to march by barking an order, or activate remote devices by touching a wireless screen. Thomas Edison couldn't have managed any of this at his peak--and shortly before his time, such powers would have been considered the unique realm of God. Being a data scientist can sometimes feel like a race against software innovations. Every interesting and useful problem is bound to become a software commodity.