OpenRaiser/Intern-Atlas huggingface dataset:
Ridge Regression with Frequent Directions: Statistical and Optimization Perspectives
Venue: arXiv cs.LG 2020
Year: 2020

Despite its impressive theory \& practical performance, Frequent Directions (\acrshort{fd}) has not been widely adopted for large-scale regression tasks. Prior work has shown randomized sketches (i) perform worse in estimating the covariance matrix of the data than \acrshort{fd}; (ii) incur high error when estimating the bias and/or variance on sketched ridge regression. We give the first constant factor relative error bounds on the bias \& variance for sketched ridge regression using \acrshort{fd}. We complement these statistical results by showing that \acrshort{fd} can be used in the optimization setting through an iterative scheme which yields high-accuracy solutions. This improves on randomized approaches which need to compromise the need for a new sketch every iteration with speed of convergence. In both settings, we also show using \emph{Robust Frequent Directions} further enhances performance.
Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial
Venue: arXiv cs.LG 2020
Year: 2020

Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have led to multiple successes in solving sequential decision-making problems in various domains, particularly in wireless communications. The future sixth-generation (6G) networks are expected to provide scalable, low-latency, ultra-reliable services empowered by the application of data-driven Artificial Intelligence (AI). The key enabling technologies of future 6G networks, such as intelligent meta-surfaces, aerial networks, and AI at the edge, involve more than one agent which motivates the importance of multi-agent learning techniques. Furthermore, cooperation is central to establishing self-organizing, self-sustaining, and decentralized networks. In this context, this tutorial focuses on the role of DRL with an emphasis on deep Multi-Agent Reinforcement Learning (MARL) for AI-enabled 6G networks. The first part of this paper will present a clear overview of the mathematical frameworks for single-agent RL and MARL. The main idea of this work is to motivate the application of RL beyond the model-free perspective which was extensively adopted in recent years. Thus, we provide a selective description of RL algorithms such as Model-Based RL (MBRL) and cooperative MARL and we highlight their potential applications in 6G wireless networks. Finally, we overview the state-of-the-art of MARL in fields such as Mobile Edge Computing (MEC), Unmanned Aerial Vehicles (UAV) networks, and cell-free massive MIMO, and identify promising future research directions. We expect this tutorial to stimulate more research endeavors to build scalable and decentralized systems based on MARL.
Differential Advising in Multi-Agent Reinforcement Learning
Venue: arXiv cs.MA 2020
Year: 2020

Agent advising is one of the main approaches to improve agent learning performance by enabling agents to share advice. Existing advising methods have a common limitation that an adviser agent can offer advice to an advisee agent only if the advice is created in the same state as the advisee's concerned state. However, in complex environments, it is a very strong requirement that two states are the same, because a state may consist of multiple dimensions and two states being the same means that all these dimensions in the two states are correspondingly identical. Therefore, this requirement may limit the applicability of existing advising methods to complex environments. In this paper, inspired by the differential privacy scheme, we propose a differential advising method which relaxes this requirement by enabling agents to use advice in a state even if the advice is created in a slightly different state. Compared with existing methods, agents using the proposed method have more opportunity to take advice from others. This paper is the first to adopt the concept of differential privacy on advising to improve agent learning performance instead of addressing security issues. The experimental results demonstrate that the proposed method is more efficient in complex environments than existing methods.
An approach to define Very High Capacity Networks with improved quality at an affordable cost
Venue: arXiv cs.PF 2020
Year: 2020

This paper aims to propose one possible approach in the setting of VHCNs (Very High Capacity Networks) performance targets that should be capable of promoting efficient investments for operators and, at the same time, improving the benefits for end-users. To this aim, we suggest relying on some specific KPIs (Key Performance Indicators), especially throughput - i.e., the bandwidth as perceived by the customer - valid at the application layer, instead of the physical layer data-rate. In this regard, the paper underlines that the bandwidth perceived is strictly linked to the latency. The most important implication is that some of the most demanding services envisaged for the future (e.g., mobile virtual and augmented reality, tactile internet) cannot be met by merely increasing the low-level protocol data-rate. Therefore, for the VHCNs reducing latency through Edge Cloud Computing (ECC) is a mandatory pre-requisite.
Coarse- and Fine-grained Attention Network with Background-aware Loss for Crowd Density Map Estimation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present a novel method Coarse- and Fine-grained Attention Network (CFANet) for generating high-quality crowd density maps and people count estimation by incorporating attention maps to better focus on the crowd area. We devise a from-coarse-to-fine progressive attention mechanism by integrating Crowd Region Recognizer (CRR) and Density Level Estimator (DLE) branch, which can suppress the influence of irrelevant background and assign attention weights according to the crowd density levels, because generating accurate fine-grained attention maps directly is normally difficult. We also employ a multi-level supervision mechanism to assist the backpropagation of gradient and reduce overfitting. Besides, we propose a Background-aware Structural Loss (BSL) to reduce the false recognition ratio while improving the structural similarity to groundtruth. Extensive experiments on commonly used datasets show that our method can not only outperform previous state-of-the-art methods in terms of count accuracy but also improve the image quality of density maps as well as reduce the false recognition ratio.
Reasoning about Temporary Coalitions and LTL-definable Ordered Objectives in Infinite Concurrent Multiplayer Games
Venue: arXiv cs.LO 2020
Year: 2020

We propose enhancing the use of propositions for denoting decisions and strategies as established in temporal languages such as CTL*, if interpreted on concurrent game models. The enhancement enables specifying varying coalition structure. In quantified CTL* this technique also enables quantifying over coalition structure, and we use it to quantify over an extended form of strategy profiles which capture temporary coalitions. We also extend CTL* by a temporal form of a binary preference operator that can be traced back to the work of Von Wright. The resulting extension of quantified CTL* can be used to spell out conditions on the rationality of behaviour in concurrent multiplayer games such as what appear in solution concepts, with players having multiple individual objectives and preferences on them, and with the possibility to form temporary coalitions taken in account. We propose complete axiomatisations for the extension of CTL* by the temporal preference operator. The decidability of the logic is not affected by that extension.
Identifying interception possibilities for WhatsApp communication
Venue: arXiv cs.CR 2020
Year: 2020

On a daily basis, law enforcement officers struggle with suspects using mobile communication applications for criminal activities. These mobile applications replaced SMS-messaging and evolved the last few years from plain-text data transmission and storage to an encrypted version. Regardless of the benefits for all law abiding citizens, this is considered to be the downside for criminal investigations. Normal smartphone, computer or network investigations do no longer provide the contents of the communication in real-time when suspects are using apps like WhatsApp, Signal or Telegram. Among them, WhatsApp is one of the most common smartphone applications for communication, both criminal as well as legal activities. Early 2016 WhatsApp introduced end-to-end encryption for all users, immediately keeping law enforcement officers around the world in the dark. Existing research to recuperate the position of law enforcement is limited to a single field of investigation and often limited to post mortem research on smartphone or computer while wiretapping is limited to metadata information. Therefore, it provides only historical data or metadata while law enforcement officers want a continuous stream of live and substantive information. This paper identified that gap in available scenarios for law enforcement investigations and identified a gap in methods available for forensic acquiring and processing these scenarios. In this paper, we propose a forensic approach to create real-time insight in the WhatsApp communication. Our approach is based on the wiretapping, decrypting WhatsApp databases, open source intelligence and WhatsApp Web communication analysis. We also evaluate our method with different scenarios in WhatsApp forensics to prove its feasibility and efficiency.
Interventional Domain Adaptation
Venue: arXiv cs.LG 2020
Year: 2020

Domain adaptation (DA) aims to transfer discriminative features learned from source domain to target domain. Most of DA methods focus on enhancing feature transferability through domain-invariance learning. However, source-learned discriminability itself might be tailored to be biased and unsafely transferable by spurious correlations, \emph{i.e.}, part of source-specific features are correlated with category labels. We find that standard domain-invariance learning suffers from such correlations and incorrectly transfers the source-specifics. To address this issue, we intervene in the learning of feature discriminability using unlabeled target data to guide it to get rid of the domain-specific part and be safely transferable. Concretely, we generate counterfactual features that distinguish the domain-specifics from domain-sharable part through a novel feature intervention strategy. To prevent the residence of domain-specifics, the feature discriminability is trained to be invariant to the mutations in the domain-specifics of counterfactual features. Experimenting on typical \emph{one-to-one} unsupervised domain adaptation and challenging domain-agnostic adaptation tasks, the consistent performance improvements of our method over state-of-the-art approaches validate that the learned discriminative features are more safely transferable and generalize well to novel domains.
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads
Venue: arXiv cs.CL 2020
Year: 2020

Deep pre-trained Transformer models have achieved state-of-the-art results over a variety of natural language processing (NLP) tasks. By learning rich language knowledge with millions of parameters, these models are usually overparameterized and significantly increase the computational overhead in applications. It is intuitive to address this issue by model compression. In this work, we propose a method, called Single-Shot Meta-Pruning, to compress deep pre-trained Transformers before fine-tuning. Specifically, we focus on pruning unnecessary attention heads adaptively for different downstream tasks. To measure the informativeness of attention heads, we train our Single-Shot Meta-Pruner (SMP) with a meta-learning paradigm aiming to maintain the distribution of text representations after pruning. Compared with existing compression methods for pre-trained models, our method can reduce the overhead of both fine-tuning and inference. Experimental results show that our pruner can selectively prune 50% of attention heads with little impact on the performance on downstream tasks and even provide better text representations. The source code will be released in the future.
Dual Application of Speech Enhancement for Automatic Speech Recognition
Venue: arXiv cs.SD 2020
Year: 2020

In this work, we exploit speech enhancement for improving a recurrent neural network transducer (RNN-T) based ASR system. We employ a dense convolutional recurrent network (DCRN) for complex spectral mapping based speech enhancement, and find it helpful for ASR in two ways: a data augmentation technique, and a preprocessing frontend. In using it for ASR data augmentation, we exploit a KL divergence based consistency loss that is computed between the ASR outputs of original and enhanced utterances. In using speech enhancement as an effective ASR frontend, we propose a three-step training scheme based on model pretraining and feature selection. We evaluate our proposed techniques on a challenging social media English video dataset, and achieve an average relative improvement of 11.2% with speech enhancement based data augmentation, 8.3% with enhancement based preprocessing, and 13.4% when combining both.
Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering
Venue: arXiv cs.CL 2020
Year: 2020

Recent developments in pre-trained neural language modeling have led to leaps in accuracy on commonsense question-answering benchmarks. However, there is increasing concern that models overfit to specific tasks, without learning to utilize external knowledge or perform general semantic reasoning. In contrast, zero-shot evaluations have shown promise as a more robust measure of a model's general reasoning abilities. In this paper, we propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks. Guided by a set of hypotheses, the framework studies how to transform various pre-existing knowledge resources into a form that is most effective for pre-training models. We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks. Extending on prior work, we devise and compare four constrained distractor-sampling strategies. We provide empirical results across five commonsense question-answering tasks with data generated from five external knowledge resources. We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks. In addition, both preserving the structure of the task as well as generating fair and informative questions help language models learn more effectively.
Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective Genetic Optimization Guided By Deep Networks
Venue: arXiv cs.AI 2020
Year: 2020

We propose a novel genetic-algorithm technique that generates black-box adversarial examples which successfully fool neural network based text classifiers. We perform a genetic search with multi-objective optimization guided by deep learning based inferences and Seq2Seq mutation to generate semantically similar but imperceptible adversaries. We compare our approach with DeepWordBug (DWB) on SST and IMDB sentiment datasets by attacking three trained models viz. char-LSTM, word-LSTM and elmo-LSTM. On an average, we achieve an attack success rate of 65.67% for SST and 36.45% for IMDB across the three models showing an improvement of 49.48% and 101% respectively. Furthermore, our qualitative study indicates that 94% of the time, the users were not able to distinguish between an original and adversarial sample.
Adaptive Linear Span Network for Object Skeleton Detection
Venue: arXiv cs.CV 2020
Year: 2020

Conventional networks for object skeleton detection are usually hand-crafted. Although effective, they require intensive priori knowledge to configure representative features for objects in different scale granularity.In this paper, we propose adaptive linear span network (AdaLSN), driven by neural architecture search (NAS), to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN is formulated with the theory of linear span, which provides one of the earliest explanations for multi-scale deep feature fusion. AdaLSN is materialized by defining a mixed unit-pyramid search space, which goes beyond many existing search spaces using unit-level or pyramid-level features.Within the mixed space, we apply genetic architecture search to jointly optimize unit-level operations and pyramid-level connections for adaptive feature space expansion. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off compared with state-of-the-arts. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction. Code is available at \href{https://github.com/sunsmarterjie/SDL-Skeleton}{\color{magenta}github.com/sunsmarterjie/SDL-Skeleton}.
Extending the statistical software package Engine for Likelihood-Free Inference
Venue: arXiv cs.LG 2020
Year: 2020

Bayesian inference is a principled framework for dealing with uncertainty. The practitioner can perform an initial assumption for the physical phenomenon they want to model (prior belief), collect some data and then adjust the initial assumption in the light of the new evidence (posterior belief). Approximate Bayesian Computation (ABC) methods, also known as likelihood-free inference techniques, are a class of models used for performing inference when the likelihood is intractable. The unique requirement of these models is a black-box sampling machine. Due to the modelling-freedom they provide these approaches are particularly captivating. Robust Optimisation Monte Carlo (ROMC) is one of the most recent techniques of the specific domain. It approximates the posterior distribution by solving independent optimisation problems. This dissertation focuses on the implementation of the ROMC method in the software package Engine for Likelihood-Free Inference (ELFI). In the first chapters, we provide the mathematical formulation and the algorithmic description of the ROMC approach. In the following chapters, we describe our implementation; (a) we present all the functionalities provided to the user and (b) we demonstrate how to perform inference on some real examples. Our implementation provides a robust and efficient solution to a practitioner who wants to perform inference on a simulator-based model. Furthermore, it exploits parallel processing for accelerating the inference wherever it is possible. Finally, it has been designed to serve extensibility; the user can easily replace specific subparts of the method without significant overhead on the development side. Therefore, it can be used by a researcher for further experimentation.
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
Venue: arXiv cs.LG 2020
Year: 2020

This paper provides a statistical analysis of high-dimensional batch Reinforcement Learning (RL) using sparse linear function approximation. When there is a large number of candidate features, our result sheds light on the fact that sparsity-aware methods can make batch RL more sample efficient. We first consider the off-policy policy evaluation problem. To evaluate a new target policy, we analyze a Lasso fitted Q-evaluation method and establish a finite-sample error bound that has no polynomial dependence on the ambient dimension. To reduce the Lasso bias, we further propose a post model-selection estimator that applies fitted Q-evaluation to the features selected via group Lasso. Under an additional signal strength assumption, we derive a sharper instance-dependent error bound that depends on a divergence function measuring the distribution mismatch between the data distribution and occupancy measure of the target policy. Further, we study the Lasso fitted Q-iteration for batch policy optimization and establish a finite-sample error bound depending on the ratio between the number of relevant features and restricted minimal eigenvalue of the data's covariance. In the end, we complement the results with minimax lower bounds for batch-data policy evaluation/optimization that nearly match our upper bounds. The results suggest that having well-conditioned data is crucial for sparse batch policy learning.
Adaptive Federated Dropout: Improving Communication Efficiency and Generalization for Federated Learning
Venue: arXiv cs.LG 2020
Year: 2020

With more regulations tackling users' privacy-sensitive data protection in recent years, access to such data has become increasingly restricted and controversial. To exploit the wealth of data generated and located at distributed entities such as mobile phones, a revolutionary decentralized machine learning setting, known as Federated Learning, enables multiple clients located at different geographical locations to collaboratively learn a machine learning model while keeping all their data on-device. However, the scale and decentralization of federated learning present new challenges. Communication between the clients and the server is considered a main bottleneck in the convergence time of federated learning. In this paper, we propose and study Adaptive Federated Dropout (AFD), a novel technique to reduce the communication costs associated with federated learning. It optimizes both server-client communications and computation costs by allowing clients to train locally on a selected subset of the global model. We empirically show that this strategy, combined with existing compression methods, collectively provides up to 57x reduction in convergence time. It also outperforms the state-of-the-art solutions for communication efficiency. Furthermore, it improves model generalization by up to 1.7%.
Distant Supervision for E-commerce Query Segmentation via Attention Network
Venue: arXiv cs.AI 2020
Year: 2020

The booming online e-commerce platforms demand highly accurate approaches to segment queries that carry the product requirements of consumers. Recent works have shown that the supervised methods, especially those based on deep learning, are attractive for achieving better performance on the problem of query segmentation. However, the lack of labeled data is still a big challenge for training a deep segmentation network, and the problem of Out-of-Vocabulary (OOV) also adversely impacts the performance of query segmentation. Different from query segmentation task in an open domain, e-commerce scenario can provide external documents that are closely related to these queries. Thus, to deal with the two challenges, we employ the idea of distant supervision and design a novel method to find contexts in external documents and extract features from these contexts. In this work, we propose a BiLSTM-CRF based model with an attention module to encode external features, such that external contexts information, which can be utilized naturally and effectively to help query segmentation. Experiments on two datasets show the effectiveness of our approach compared with several kinds of baselines.