pwc-archive/papers-with-abstracts huggingface dataset:
Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets
Authors: Afiya Ayman, Michael Wilbur, Amutheezan Sivagnanam, Philip Pugliese, Abhishek Dubey, Aron Laszka
Date: 2020-04-10 00:00:00
Tasks: Scheduling

Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs). However, because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. Making the best use of these mixed fleets presents a challenge for agencies since optimizing the assignment of vehicles to transit routes, scheduling charging, etc. require accurate predictions of electricity and fuel use. Recent advances in sensor-based technologies, data analytics, and machine learning enable remedying this situation; however, to the best of our knowledge, there exists no framework that would integrate all relevant data into a route-level prediction model for public transit. In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN. We present a data collection and storage framework, which we use to capture system-level data, including traffic and weather conditions, and high-frequency vehicle-level data, including location traces, fuel or electricity use, etc. We present domain-specific methods and algorithms for integrating and cleansing data from various sources, including street and elevation maps. Finally, we train and evaluate machine learning models, including deep neural networks, decision trees, and linear regression, on our integrated dataset. Our results show that neural networks provide accurate estimates, while other models can help us discover relations between energy use and factors such as road and weather conditions.
A Survey on Impact of Transient Faults on BNN Inference Accelerators
Authors: Navid Khoshavi, Connor Broyles, Yu Bi
Date: 2020-04-10 00:00:00
Tasks: Philosophy

Over past years, the philosophy for designing the artificial intelligence algorithms has significantly shifted towards automatically extracting the composable systems from massive data volumes. This paradigm shift has been expedited by the big data booming which enables us to easily access and analyze the highly large data sets. The most well-known class of big data analysis techniques is called deep learning. These models require significant computation power and extremely high memory accesses which necessitate the design of novel approaches to reduce the memory access and improve power efficiency while taking into account the development of domain-specific hardware accelerators to support the current and future data sizes and model structures.The current trends for designing application-specific integrated circuits barely consider the essential requirement for maintaining the complex neural network computation to be resilient in the presence of soft errors. The soft errors might strike either memory storage or combinational logic in the hardware accelerator that can affect the architectural behavior such that the precision of the results fall behind the minimum allowable correctness. In this study, we demonstrate that the impact of soft errors on a customized deep learning algorithm called Binarized Neural Network might cause drastic image misclassification. Our experimental results show that the accuracy of image classifier can drastically drop by 76.70% and 19.25% in lfcW1A1 and cnvW1A1 networks,respectively across CIFAR-10 and MNIST datasets during the fault injection for the worst-case scenarios
Exposing Hardware Building Blocks to Machine Learning Frameworks
Authors: Yash Akhauri
Date: 2020-04-10 00:00:00
Tasks: BIG-bench Machine Learning, Quantization

There are a plethora of applications that demand high throughput and low latency algorithms leveraging machine learning methods. This need for real time processing can be seen in industries ranging from developing neural network based pre-distortors for enhanced mobile broadband to designing FPGA-based triggers in major scientific efforts by CERN for particle physics. In this thesis, we explore how niche domains can benefit vastly if we look at neurons as a unique boolean function of the form $f:B^{I} \rightarrow B^{O}$, where $B = \{0,1\}$. We focus on how to design topologies that complement such a view of neurons, how to automate such a strategy of neural network design, and inference of such networks on Xilinx FPGAs. Major hardware borne constraints arise when designing topologies that view neurons as unique boolean functions. Fundamentally, realizing such topologies on hardware asserts a strict limit on the 'fan-in' bits of a neuron due to the doubling of permutations possible with every increment in input bit-length. We address this limit by exploring different methods of implementing sparsity and explore activation quantization. Further, we develop a library that supports training a neural network with custom sparsity and quantization. This library also supports conversion of trained Sparse Quantized networks from PyTorch to VERILOG code which is then synthesized using Vivado, all of which is part of the LogicNet tool-flow. To aid faster prototyping, we also support calculation of the worst-case hardware cost of any given topology. We hope that our insights into the behavior of extremely sparse quantized neural networks are of use to the research community and by extension allow people to use the LogicNet design flow to deploy highly efficient neural networks.
Stacked Convolutional Deep Encoding Network for Video-Text Retrieval
Authors: Rui Zhao, Kecheng Zheng, Zheng-Jun Zha
Date: 2020-04-10 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval, Text Retrieval, Video-Text Retrieval

Existing dominant approaches for cross-modal video-text retrieval task are to learn a joint embedding space to measure the cross-modal similarity. However, these methods rarely explore long-range dependency inside video frames or textual words leading to insufficient textual and visual details. In this paper, we propose a stacked convolutional deep encoding network for video-text retrieval task, which considers to simultaneously encode long-range and short-range dependency in the videos and texts. Specifically, a multi-scale dilated convolutional (MSDC) block within our approach is able to encode short-range temporal cues between video frames or text words by adopting different scales of kernel size and dilation size of convolutional layer. A stacked structure is designed to expand the receptive fields by repeatedly adopting the MSDC block, which further captures the long-range relations between these cues. Moreover, to obtain more robust textual representations, we fully utilize the powerful language model named Transformer in two stages: pretraining phrase and fine-tuning phrase. Extensive experiments on two different benchmark datasets (MSR-VTT, MSVD) show that our proposed method outperforms other state-of-the-art approaches.
An implementation of an imitation game with ASD children to learn nursery rhymes
Authors: Sao Mai Nguyen, Nathalie Collot-Lavenne, Christophe Lohr, Sébastien Guillon, Patricio Tula, Alvaro Paez, Mouad Bouaida, Arthus Anin, Saad El Qacemi
Date: 2020-04-10 00:00:00

Previous studies have suggested that being imitated by an adult is an effective intervention with children with autism and developmental delay. The purpose of this study is to investigate if an imitation game with a robot can arise interest from children and constitute an effective tool to be used in clinical activities. In this paper, we describe the design of our nursery rhyme imitation game, its implementation based on RGB image pose recognition and the preliminary tests we performed.
A Simple Approach to Learning Unsupervised Multilingual Embeddings
Authors: Pratik Jawanpuria, Mayank Meghwanshi, Bamdev Mishra
Date: 2020-04-10 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Bilingual Lexicon Induction, Dependency Parsing, Document Classification, Word Alignment, Word Similarity

Recent progress on unsupervised learning of cross-lingual embeddings in bilingual setting has given impetus to learning a shared embedding space for several languages without any supervision. A popular framework to solve the latter problem is to jointly solve the following two sub-problems: 1) learning unsupervised word alignment between several pairs of languages, and 2) learning how to map the monolingual embeddings of every language to a shared multilingual space. In contrast, we propose a simple, two-stage framework in which we decouple the above two sub-problems and solve them separately using existing techniques. The proposed approach obtains surprisingly good performance in various tasks such as bilingual lexicon induction, cross-lingual word similarity, multilingual document classification, and multilingual dependency parsing. When distant languages are involved, the proposed solution illustrates robustness and outperforms existing unsupervised multilingual word embedding approaches. Overall, our experimental results encourage development of multi-stage models for such challenging problems.
Training few-shot classification via the perspective of minibatch and pretraining
Authors: Meiyu Huang, Xueshuang Xiang, Yao Xu
Date: 2020-04-10 00:00:00
Tasks: Classification, General Classification, Meta-Learning

Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machine learning. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained to learn the ability of handling classification tasks on extremely large or infinite episodes representing different classification task, each with a small labeled support set and its corresponding query set. In this work, we advance this few-shot classification paradigm by formulating it as a supervised classification learning problem. We further propose multi-episode and cross-way training techniques, which respectively correspond to the minibatch and pretraining in classification problems. Experimental results on a state-of-the-art few-shot classification method (prototypical networks) demonstrate that both the proposed training strategies can highly accelerate the training process without accuracy loss for varying few-shot classification problems on Omniglot and miniImageNet.
Towards GANs' Approximation Ability
Authors: Xuejiao Liu, Yao Xu, Xueshuang Xiang
Date: 2020-04-10 00:00:00

Generative adversarial networks (GANs) have attracted intense interest in the field of generative models. However, few investigations focusing either on the theoretical analysis or on algorithm design for the approximation ability of the generator of GANs have been reported. This paper will first theoretically analyze GANs' approximation property. Similar to the universal approximation property of the fully connected neural networks with one hidden layer, we prove that the generator with the input latent variable in GANs can universally approximate the potential data distribution given the increasing hidden neurons. Furthermore, we propose an approach named stochastic data generation (SDG) to enhance GANs'approximation ability. Our approach is based on the simple idea of imposing randomness through data generation in GANs by a prior distribution on the conditional probability between the layers. SDG approach can be easily implemented by using the reparameterization trick. The experimental results on synthetic dataset verify the improved approximation ability obtained by this SDG approach. In the practical dataset, four GANs using SDG can also outperform the corresponding traditional GANs when the model architectures are smaller.
Blind Adversarial Pruning: Balance Accuracy, Efficiency and Robustness
Authors: Haidong Xie, Lixin Qian, Xueshuang Xiang, Naijin Liu
Date: 2020-04-10 00:00:00

With the growth of interest in the attack and defense of deep neural networks, researchers are focusing more on the robustness of applying them to devices with limited memory. Thus, unlike adversarial training, which only considers the balance between accuracy and robustness, we come to a more meaningful and critical issue, i.e., the balance among accuracy, efficiency and robustness (AER). Recently, some related works focused on this issue, but with different observations, and the relations among AER remain unclear. This paper first investigates the robustness of pruned models with different compression ratios under the gradual pruning process and concludes that the robustness of the pruned model drastically varies with different pruning processes, especially in response to attacks with large strength. Second, we test the performance of mixing the clean data and adversarial examples (generated with a prescribed uniform budget) into the gradual pruning process, called adversarial pruning, and find the following: the pruned model's robustness exhibits high sensitivity to the budget. Furthermore, to better balance the AER, we propose an approach called blind adversarial pruning (BAP), which introduces the idea of blind adversarial training into the gradual pruning process. The main idea is to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to modify the AEs used during pruning, thus ensuring that the strengths of AEs are dynamically located within a reasonable range at each pruning step and ultimately improving the overall AER of the pruned model. The experimental results obtained using BAP for pruning classification models based on several benchmarks demonstrate the competitive performance of this method: the robustness of the model pruned by BAP is more stable among varying pruning processes, and BAP exhibits better overall AER than adversarial pruning.
Blind Adversarial Training: Balance Accuracy and Robustness
Authors: Haidong Xie, Xueshuang Xiang, Naijin Liu, Bin Dong
Date: 2020-04-10 00:00:00

Adversarial training (AT) aims to improve the robustness of deep learning models by mixing clean data and adversarial examples (AEs). Most existing AT approaches can be grouped into restricted and unrestricted approaches. Restricted AT requires a prescribed uniform budget to constrain the magnitude of the AE perturbations during training, with the obtained results showing high sensitivity to the budget. On the other hand, unrestricted AT uses unconstrained AEs, resulting in the use of AEs located beyond the decision boundary; these overestimated AEs significantly lower the accuracy on clean data. These limitations mean that the existing AT approaches have difficulty in obtaining a comprehensively robust model with high accuracy and robustness when confronting attacks with varying strengths. Considering this problem, this paper proposes a novel AT approach named blind adversarial training (BAT) to better balance the accuracy and robustness. The main idea of this approach is to use a cutoff-scale strategy to adaptively estimate a nonuniform budget to modify the AEs used in the training, ensuring that the strengths of the AEs are dynamically located in a reasonable range and ultimately improving the overall robustness of the AT model. The experimental results obtained using BAT for training classification models on several benchmarks demonstrate the competitive performance of this method.
Sleep Stage Scoring Using Joint Frequency-Temporal and Unsupervised Features
Authors: Mohamadreza Jafaryani, Saeed Khorram, Vahid Pourahmadi, Minoo Shahbazi
Date: 2020-04-10 00:00:00

Patients with sleep disorders can better manage their lifestyle if they know about their special situations. Detection of such sleep disorders is usually possible by analyzing a number of vital signals that have been collected from the patients. To simplify this task, a number of Automatic Sleep Stage Recognition (ASSR) methods have been proposed. Most of these methods use temporal-frequency features that have been extracted from the vital signals. However, due to the non-stationary nature of sleep signals, such schemes are not leading an acceptable accuracy. Recently, some ASSR methods have been proposed which use deep neural networks for unsupervised feature extraction. In this paper, we proposed to combine the two ideas and use both temporal-frequency and unsupervised features at the same time. To augment the time resolution, each standard epoch is segmented into 5 sub-epochs. Additionally, to enhance the accuracy, we employ three classifiers with different properties and then use an ensemble method as the ultimate classifier. The simulation results show that the proposed method enhances the accuracy of conventional ASSR methods.
Telling BERT's full story: from Local Attention to Global Aggregation
Authors: Damian Pascual, Gino Brunner, Roger Wattenhofer
Date: 2020-04-10 00:00:00
Proceeding: EACL 2021 2

We take a deep look into the behavior of self-attention heads in the transformer architecture. In light of recent work discouraging the use of attention distributions for explaining a model's behavior, we show that attention distributions can nevertheless provide insights into the local behavior of attention heads. This way, we propose a distinction between local patterns revealed by attention and global patterns that refer back to the input, and analyze BERT from both angles. We use gradient attribution to analyze how the output of an attention attention head depends on the input tokens, effectively extending the local attention-based analysis to account for the mixing of information throughout the transformer layers. We find that there is a significant discrepancy between attention and attribution distributions, caused by the mixing of context inside the model. We quantify this discrepancy and observe that interestingly, there are some patterns that persist across all layers despite the mixing.
Object classification from randomized EEG trials
Authors: Hamad Ahmed, Ronnie B Wilbur, Hari M Bharadwaj, Jeffrey Mark Siskind
Date: 2020-04-09 00:00:00
Proceeding: CVPR 2021 1
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Object

New results suggest strong limits to the feasibility of classifying human brain activity evoked from image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we again attempt to replicate these experiments with randomized trials on a far larger (20x) dataset of 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection without substantial improvement in classification accuracy suggests limits to the feasibility of this enterprise.
On the Language Neutrality of Pre-trained Multilingual Representations
Authors: Jindřich Libovický, Rudolf Rosa, Alexander Fraser
Date: 2020-04-09 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Language Identification, Transfer Learning, Word Alignment

Multilingual contextual embeddings, such as multilingual BERT and XLM-RoBERTa, have proved useful for many multi-lingual tasks. Previous work probed the cross-linguality of the representations indirectly using zero-shot transfer learning on morphological and syntactic tasks. We instead investigate the language-neutrality of multilingual contextual embeddings directly and with respect to lexical semantics. Our results show that contextual embeddings are more language-neutral and, in general, more informative than aligned static word-type embeddings, which are explicitly trained for language neutrality. Contextual embeddings are still only moderately language-neutral by default, so we propose two simple methods for achieving stronger language neutrality: first, by unsupervised centering of the representation for each language and second, by fitting an explicit projection on small parallel data. Besides, we show how to reach state-of-the-art accuracy on language identification and match the performance of statistical methods for word alignment of parallel sentences without using parallel data.
Game-theoretic applications of a relational risk model
Authors: Tatiana Urazaeva
Date: 2020-04-09 00:00:00
Tasks: Decision Making, model

The report suggests the concept of risk, outlining two mathematical structures necessary for risk genesis: the set of outcomes and, in a general case, partial order of preference on it. It is shown that this minimum partial order should constitute the structure of a semilattice. In some cases, there should be a system of semilattices nested in a certain way. On this basis, the classification of risk theory tasks is given in the context of specialization of mathematical knowledge. In other words, we are talking about the development of a new rela-tional risk theory. The problem of political decision making in game-theoretic formulation in terms of having partial order of preference on the set of outcomes for each par-ticipant of the game forming a certain system of nested semilattices is consid-ered as an example of a relational risk concept implementation. Solutions to the problem obtained through the use of various optimality principles are investi-gated.
CVPR 2019 WAD Challenge on Trajectory Prediction and 3D Perception
Authors: Sibo Zhang, Yuexin Ma, Ruigang Yang
Date: 2020-04-06 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection, Trajectory Prediction

This paper reviews the CVPR 2019 challenge on Autonomous Driving. Baidu's Robotics and Autonomous Driving Lab (RAL) providing 150 minutes labeled Trajectory and 3D Perception dataset including about 80k lidar point cloud and 1000km trajectories for urban traffic. The challenge has two tasks in (1) Trajectory Prediction and (2) 3D Lidar Object Detection. There are more than 200 teams submitted results on Leaderboard and more than 1000 participants attended the workshop.