OpenRaiser/Intern-Atlas huggingface dataset:
Improving On-policy Learning with Statistical Reward Accumulation
Venue: arXiv cs.LG 2018
Year: 2018

Deep reinforcement learning has obtained significant breakthroughs in recent years. Most methods in deep-RL achieve good results via the maximization of the reward signal provided by the environment, typically in the form of discounted cumulative returns. Such reward signals represent the immediate feedback of a particular action performed by an agent. However, tasks with sparse reward signals are still challenging to on-policy methods. In this paper, we introduce an effective characterization of past reward statistics (which can be seen as long-term feedback signals) to supplement this immediate reward feedback. In particular, value functions are learned with multi-critics supervision, enabling complex value functions to be more easily approximated in on-policy learning, even when the reward signals are sparse. We also introduce a novel exploration mechanism called "hot-wiring" that can give a boost to seemingly trapped agents. We demonstrate the effectiveness of our advantage actor multi-critic (A2MC) method across the discrete domains in Atari games as well as continuous domains in the MuJoCo environments. A video demo is provided at https://youtu.be/zBmpf3Yz8tc.
Deep Feature Learning of Multi-Network Topology for Node Classification
Venue: arXiv cs.LG 2018
Year: 2018

Networks are ubiquitous structure that describes complex relationships between different entities in the real world. As a critical component of prediction task over nodes in networks, learning the feature representation of nodes has become one of the most active areas recently. Network Embedding, aiming to learn non-linear and low-dimensional feature representation based on network topology, has been proved to be helpful on tasks of network analysis, especially node classification. For many real-world systems, multiple types of relations are naturally represented by multiple networks. However, existing network embedding methods mainly focus on single network embedding and neglect the information shared among different networks. In this paper, we propose a novel multiple network embedding method based on semisupervised autoencoder, named DeepMNE, which captures complex topological structures of multi-networks and takes the correlation among multi-networks into account. We evaluate DeepMNE on the task of node classification with two real-world datasets. The experimental results demonstrate the superior performance of our method over four state-of-the-art algorithms.
Performance Analysis of MRC under Spatially Correlated Interference using Mixture-based Method
Venue: arXiv cs.IT 2018
Year: 2018

Effect of interference correlation on wireless systems is often studied by modeling the locations of interferers as a Poisson Point Process (PPP). However, in many cases, the complicated nature of this correlation limits the analytical tractability of the PPP-based approach. For example, for an interference-aware $N$-antenna Maximum-Ratio Combining (MRC) receiver, the analytical expression for outage probability is available only for $N=2$. For $N>2$, the exact analysis using standard PPP-based approach becomes intractable because of which one has to either resort to bounds or to simulations. In this letter, we overcome this issue and derive the MRC outage probability for an arbitrary $N$ by employing the mixture-based method of modeling the correlation in the interference powers. This method offers a much simpler analytical structure to the correlated interference, thereby lending analytical tractability to such analyses. The mixture parameter ($q$) is tuned based on the matching of joint Signal-to-Interference Ratio (SIR) statistics, which results in a very accurate mixture-based result. The tightness of these approximations is verified using Monte Carlo simulations.
Where Do All These Search Terms Come From? - Two Experiments in Domain-Specific Search
Venue: arXiv cs.IR 2018
Year: 2018

Within a search session users often apply different search terms, as well as different variations and combinations of them. This way, they want to make sure that they find relevant information for different stages and aspects of their information task. Research questions which arise from this search ap- proach are: Where do users get all the ideas, hints and suggestions for new search terms or their variations from? How many ideas come from the user? How many from outside the IR system? What is the role of the used search sys- tem? To investigate these questions we used data from two experiments: first, from a user study with eye tracking data; second, from a large-scale log analy- sis. We found that in both experiments a large part of the search terms has been explicitly seen or shown before on the interface of the search system.
Sparse Kernel PCA for Outlier Detection
Venue: arXiv cs.LG 2018
Year: 2018

In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA. We formulate SKPCA as a constrained optimization problem with elastic net regularization (Hastie et al.) in kernel feature space and solve it. We consider outlier detection (where KPCA is employed) as an application for SKPCA, using the RBF kernel. We test it on 5 real-world datasets and show that by using just 4% (or even less) of the principal components (PCs), where each PC has on average less than 12% non-zero elements in the worst case among all 5 datasets, we are able to nearly match and in 3 datasets even outperform KPCA. We also compare the performance of our method with a recently proposed method for SKPCA by Wang et al. and show that our method performs better in terms of both accuracy and sparsity. We also provide a novel probabilistic proof to justify the existence of sparse solutions for KPCA using the RBF kernel. To the best of our knowledge, this is the first attempt at theoretically analyzing the validity of SKPCA.
MixUp as Locally Linear Out-Of-Manifold Regularization
Venue: arXiv cs.LG 2018
Year: 2018

MixUp is a recently proposed data-augmentation scheme, which linearly interpolates a random pair of training examples and correspondingly the one-hot representations of their labels. Training deep neural networks with such additional data is shown capable of significantly improving the predictive accuracy of the current art. The power of MixUp, however, is primarily established empirically and its working and effectiveness have not been explained in any depth. In this paper, we develop an understanding for MixUp as a form of "out-of-manifold regularization", which imposes certain "local linearity" constraints on the model's input space beyond the data manifold. This analysis enables us to identify a limitation of MixUp, which we call "manifold intrusion". In a nutshell, manifold intrusion in MixUp is a form of under-fitting resulting from conflicts between the synthetic labels of the mixed-up examples and the labels of original training data. Such a phenomenon usually happens when the parameters controlling the generation of mixing policies are not sufficiently fine-tuned on the training data. To address this issue, we propose a novel adaptive version of MixUp, where the mixing policies are automatically learned from the data using an additional network and objective function designed to avoid manifold intrusion. The proposed regularizer, AdaMixUp, is empirically evaluated on several benchmark datasets. Extensive experiments demonstrate that AdaMixUp improves upon MixUp when applied to the current art of deep classification models.
A Deeper Look at 3D Shape Classifiers
Venue: arXiv cs.CV 2018
Year: 2018

We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial transformations. By varying the number of training examples and employing cross-modal transfer learning we study the role of initialization of existing deep architectures for 3D shape classification. Our analysis shows that multiview methods continue to offer the best generalization even without pretraining on large labeled image datasets, and even when trained on simplified inputs such as binary silhouettes. Furthermore, the performance of voxel-based 3D convolutional networks and point-based architectures can be improved via cross-modal transfer from image representations. Finally, we analyze the robustness of 3D shape classifiers to adversarial transformations and present a novel approach for generating adversarial perturbations of a 3D shape for multiview classifiers using a differentiable renderer. We find that point-based networks are more robust to point position perturbations while voxel-based and multiview networks are easily fooled with the addition of imperceptible noise to the input.
Accelerating Deep Neural Networks with Spatial Bottleneck Modules
Venue: arXiv cs.CV 2018
Year: 2018

This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks. The core idea is to decompose convolution into two stages, which first reduce the spatial resolution of the feature map, and then restore it to the desired size. This operation decreases the sampling density in the spatial domain, which is independent yet complementary to network acceleration approaches in the channel domain. Using different sampling rates, we can tradeoff between recognition accuracy and model complexity. As a basic building block, spatial bottleneck can be used to replace any single convolutional layer, or the combination of two convolutional layers. We empirically verify the effectiveness of spatial bottleneck by applying it to the deep residual networks. Spatial bottleneck achieves 2x and 1.4x speedup on the regular and channel-bottlenecked residual blocks, respectively, with the accuracies retained in recognizing low-resolution images, and even improved in recognizing high-resolution images.
Exploiting SNMP-MIB Data to Detect Network Anomalies using Machine Learning Techniques
Venue: arXiv cs.NI 2018
Year: 2018

The exponential increase in the number of malicious threats on computer networks and Internet services due to a large number of attacks makes the network security at continuous risk. One of the most prevalent network attacks that threaten networks is Denial of Service (DoS) flooding attack. DoS attacks have recently become the most attractive type of attacks to attackers and these have posed devastating threats to network services. So, there is a need for effective approaches, which can efficiently detect any intrusion in the network. This paper presents an efficient mechanism for network attacks detection and types of attack classification using the Management Information Base (MIB) database associated with the Simple Network Management Protocol (SNMP) through machine learning techniques. This paper also investigates the impact of SNMP-MIB data on network anomalies detection. Three classifiers, namely, Random Forest, AdaboostM1 and MLP are used to build the detection model. The use of different classifiers presents a comprehensive study on the effectiveness of SNMP-MIB data in detecting different types of attack. Empirical results show that the machine learning techniques were quite successful in detecting and classifying the attacks with a high detection rate.
Are You Sure You Want To Do That? Classification with Verification
Venue: arXiv cs.LG 2018
Year: 2018

Classification systems typically act in isolation, meaning they are required to implicitly memorize the characteristics of all candidate classes in order to classify. The cost of this is increased memory usage and poor sample efficiency. We propose a model which instead verifies using reference images during the classification process, reducing the burden of memorization. The model uses iterative nondifferentiable queries in order to classify an image. We demonstrate that such a model is feasible to train and can match baseline accuracy while being more parameter efficient. However, we show that finding the correct balance between image recognition and verification is essential to pushing the model towards desired behavior, suggesting that a pipeline of recognition followed by verification is a more promising approach.
Complexity of MLDP
Venue: arXiv cs.CC 2018
Year: 2018

We carry out an explicit examination of the NP-hardness of a bi- objective optimization problem to minimize distance and latency of a single-vehicle route designed to serve a set of client requests. In addition to being a Hamiltonian cycle the route is to minimize the traveled distance of the vehicle as well as the total waiting time of the clients along the route.
Migration as Submodular Optimization
Venue: arXiv cs.GT 2018
Year: 2018

Migration presents sweeping societal challenges that have recently attracted significant attention from the scientific community. One of the prominent approaches that have been suggested employs optimization and machine learning to match migrants to localities in a way that maximizes the expected number of migrants who find employment. However, it relies on a strong additivity assumption that, we argue, does not hold in practice, due to competition effects; we propose to enhance the data-driven approach by explicitly optimizing for these effects. Specifically, we cast our problem as the maximization of an approximately submodular function subject to matroid constraints, and prove that the worst-case guarantees given by the classic greedy algorithm extend to this setting. We then present three different models for competition effects, and show that they all give rise to submodular objectives. Finally, we demonstrate via simulations that our approach leads to significant gains across the board.
Torpid Mixing of Markov Chains for the Six-vertex Model on $\mathbb{Z}^2$
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we study the mixing time of two widely used Markov chain algorithms for the six-vertex model, Glauber dynamics and the directed-loop algorithm, on the square lattice $\mathbb{Z}^2$. We prove, for the first time that, on finite regions of the square lattice these Markov chains are torpidly mixing under parameter settings in the ferroelectric phase and the anti-ferroelectric phase.
What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks
Venue: arXiv cs.CL 2018
Year: 2018

Nature language inference (NLI) task is a predictive task of determining the inference relationship of a pair of natural language sentences. With the increasing popularity of NLI, many state-of-the-art predictive models have been proposed with impressive performances. However, several works have noticed the statistical irregularities in the collected NLI data set that may result in an over-estimated performance of these models and proposed remedies. In this paper, we further investigate the statistical irregularities, what we refer as confounding factors, of the NLI data sets. With the belief that some NLI labels should preserve under swapping operations, we propose a simple yet effective way (swapping the two text fragments) of evaluating the NLI predictive models that naturally mitigate the observed problems. Further, we continue to train the predictive models with our swapping manner and propose to use the deviation of the model's evaluation performances under different percentages of training text fragments to be swapped to describe the robustness of a predictive model. Our evaluation metrics leads to some interesting understandings of recent published NLI methods. Finally, we also apply the swapping operation on NLI models to see the effectiveness of this straightforward method in mitigating the confounding factor problems in training generic sentence embeddings for other NLP transfer tasks.
Optimal and Low-Complexity Dynamic Spectrum Access for RF-Powered Ambient Backscatter System with Online Reinforcement Learning
Venue: arXiv cs.NI 2018
Year: 2018

Ambient backscatter has been introduced with a wide range of applications for low power wireless communications. In this article, we propose an optimal and low-complexity dynamic spectrum access framework for RF-powered ambient backscatter system. In this system, the secondary transmitter not only harvests energy from ambient signals (from incumbent users), but also backscatters these signals to its receiver for data transmission. Under the dynamics of the ambient signals, we first adopt the Markov decision process (MDP) framework to obtain the optimal policy for the secondary transmitter, aiming to maximize the system throughput. However, the MDP-based optimization requires complete knowledge of environment parameters, e.g., the probability of a channel to be idle and the probability of a successful packet transmission, that may not be practical to obtain. To cope with such incomplete knowledge of the environment, we develop a low-complexity online reinforcement learning algorithm that allows the secondary transmitter to "learn" from its decisions and then attain the optimal policy. Simulation results show that the proposed learning algorithm not only efficiently deals with the dynamics of the environment, but also improves the average throughput up to 50% and reduces the blocking probability and delay up to 80% compared with conventional methods.
Instance-based Deep Transfer Learning
Venue: arXiv cs.CV 2018
Year: 2018

Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.
Affine Sessions
Venue: arXiv cs.LO 2018
Year: 2018

Session types describe the structure of communications implemented by channels. In particular, they prescribe the sequence of communications, whether they are input or output actions, and the type of value exchanged. Crucial to any language with session types is the notion of linearity, which is essential to ensure that channels exhibit the behaviour prescribed by their type without interference in the presence of concurrency. In this work we relax the condition of linearity to that of affinity, by which channels exhibit at most the behaviour prescribed by their types. This more liberal setting allows us to incorporate an elegant error handling mechanism which simplifies and improves related works on exceptions. Moreover, our treatment does not affect the progress properties of the language: sessions never get stuck.
Performance Analysis of a System with Bursty Traffic and Adjustable Transmission Times
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we consider the case where a source with bursty traffic can adjust the transmission duration in order to increase the reliability. The source is equipped with a queue in order to store the arriving packets. We model the system with a discrete time Markov Chain, and we characterize the performance in terms of service probability and average delay per packet. The accuracy of the theoretical results is validated through simulations. This work serves as an initial step in order to provide a framework for systems with arbitrary arrivals and variable transmission durations and it can be utilized for the derivation of the delay distribution and the delay violation probability.
Identifying The Most Informative Features Using A Structurally Interacting Elastic Net
Venue: arXiv cs.LG 2018
Year: 2018

Feature selection can efficiently identify the most informative features with respect to the target feature used in training. However, state-of-the-art vector-based methods are unable to encapsulate the relationships between feature samples into the feature selection process, thus leading to significant information loss. To address this problem, we propose a new graph-based structurally interacting elastic net method for feature selection. Specifically, we commence by constructing feature graphs that can incorporate pairwise relationship between samples. With the feature graphs to hand, we propose a new information theoretic criterion to measure the joint relevance of different pairwise feature combinations with respect to the target feature graph representation. This measure is used to obtain a structural interaction matrix where the elements represent the proposed information theoretic measure between feature pairs. We then formulate a new optimization model through the combination of the structural interaction matrix and an elastic net regression model for the feature subset selection problem. This allows us to a) preserve the information of the original vectorial space, b) remedy the information loss of the original feature space caused by using graph representation, and c) promote a sparse solution and also encourage correlated features to be selected. Because the proposed optimization problem is non-convex, we develop an efficient alternating direction multiplier method (ADMM) to locate the optimal solutions. Extensive experiments on various datasets demonstrate the effectiveness of the proposed methods.
Outage Probability Analysis of Uplink NOMA over Ultra-High-Speed FSO-Backhauled Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider a relay-assisted uplink non-orthogonal multiple access (NOMA) system where two radio frequency (RF) users are grouped for simultaneous transmission, over each resource block, to an intermediate relay which forwards the amplified version of the users' aggregated signals in the presence of multiuser interference to a relatively far destination. In order to cope with the users' ever-increasing desire for higher data rates, a high-throughput free-space optics (FSO) link is employed as the relay-destination backhaul link. Dynamic-order decoding is employed at the destination to determine the priority of the users based on their instantaneous channel state information (CSI). Closed-form expressions for the individual- and sum-rate outage probability formulas are derived in the case of independent Rayleigh fading for the users-relay access links when the FSO backhaul link is subject to Gamma-Gamma turbulence with pointing error. This work can be regarded as an initial attempt to incorporate power-domain NOMA over ultra-high-speed FSO-backhauled systems, known as mixed RF-FSO systems.