pwc-archive/papers-with-abstracts huggingface dataset:
Adapting User Interfaces with Model-based Reinforcement Learning
Authors: Kashyap Todi, Gilles Bailly, Luis A. Leiva, Antti Oulasvirta
Date: 2021-03-11 00:00:00
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user -- for example, due to surprise or relearning effort -- or "trap" the process to a suboptimal design immaturely. However, effects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It finds beneficial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning method plans sequences of adaptations and consults predictive HCI models to estimate their effects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy.
Improved Coherence Index-Based Bound in Compressive Sensing
Authors: Ljubisa Stankovic, Milos Brajovic, Danilo Mandic, Isidora Stankovic, Milos Dakovic
Date: 2021-03-11 00:00:00
Tasks: Compressive Sensing

Within the Compressive Sensing (CS) paradigm, sparse signals can be reconstructed based on a reduced set of measurements. Reliability of the solution is determined by the uniqueness condition. With its mathematically tractable and feasible calculation, coherence index is one of very few CS metrics with a considerable practical importance. In this paper, we propose an improvement of the coherence based uniqueness relation for the matching pursuit algorithms. Starting from a simple and intuitive derivation of the standard uniqueness condition based on the coherence index, we derive a less conservative coherence index-based lower bound for signal sparsity. The results are generalized to the uniqueness condition of the $l_0$-norm minimization for a signal represented in two orthonormal bases.
Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution
Authors: Xavier Garcia, Noah Constant, Ankur P. Parikh, Orhan Firat
Date: 2021-03-11 00:00:00
Proceeding: NAACL 2021 4
Tasks: Continual Learning, Machine Translation, Translation

We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models, paving the way towards efficient continual learning for multilingual machine translation. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts, incurs only minor degradation on the translation performance for the original language pairs and provides competitive performance even in the case where we only possess monolingual data for the new languages.
BODAME: Bilevel Optimization for Defense Against Model Extraction
Authors: Yuto Mori, Atsushi Nitanda, Akiko Takeda
Date: 2021-03-11 00:00:00
Tasks: Bilevel Optimization, model, Model extraction

Model extraction attacks have become serious issues for service providers using machine learning. We consider an adversarial setting to prevent model extraction under the assumption that attackers will make their best guess on the service provider's model using query accesses, and propose to build a surrogate model that significantly keeps away the predictions of the attacker's model from those of the true model. We formulate the problem as a non-convex constrained bilevel optimization problem and show that for kernel models, it can be transformed into a non-convex 1-quadratically constrained quadratic program with a polynomial-time algorithm to find the global optimum. Moreover, we give a tractable transformation and an algorithm for more complicated models that are learned by using stochastic gradient descent-based algorithms. Numerical experiments show that the surrogate model performs well compared with existing defense models when the difference between the attacker's and service provider's distributions is large. We also empirically confirm the generalization ability of the surrogate model.
Unknown Object Segmentation from Stereo Images
Authors: Maximilian Durner, Wout Boerdijk, Martin Sundermeyer, Werner Friedl, Zoltan-Csaba Marton, Rudolph Triebel
Date: 2021-03-11 00:00:00
Tasks: Instance Segmentation, Object, Segmentation, Semantic Segmentation

Although instance-aware perception is a key prerequisite for many autonomous robotic applications, most of the methods only partially solve the problem by focusing solely on known object categories. However, for robots interacting in dynamic and cluttered environments, this is not realistic and severely limits the range of potential applications. Therefore, we propose a novel object instance segmentation approach that does not require any semantic or geometric information of the objects beforehand. In contrast to existing works, we do not explicitly use depth data as input, but rely on the insight that slight viewpoint changes, which for example are provided by stereo image pairs, are often sufficient to determine object boundaries and thus to segment objects. Focusing on the versatility of stereo sensors, we employ a transformer-based architecture that maps directly from the pair of input images to the object instances. This has the major advantage that instead of a noisy, and potentially incomplete depth map as an input, on which the segmentation is computed, we use the original image pair to infer the object instances and a dense depth map. In experiments in several different application domains, we show that our Instance Stereo Transformer (INSTR) algorithm outperforms current state-of-the-art methods that are based on depth maps. Training code and pretrained models will be made available.
MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding
Authors: Tuhin Chakrabarty, Xurui Zhang, Smaranda Muresan, Nanyun Peng
Date: 2021-03-11 00:00:00
Proceeding: NAACL 2021 4
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Sentence

Generating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Based on a theoretically-grounded connection between metaphors and symbols, we propose a method to automatically construct a parallel corpus by transforming a large number of metaphorical sentences from the Gutenberg Poetry corpus (Jacobs, 2018) to their literal counterpart using recent advances in masked language modeling coupled with commonsense inference. For the generation task, we incorporate a metaphor discriminator to guide the decoding of a sequence to sequence model fine-tuned on our parallel data to generate high-quality metaphors. Human evaluation on an independent test set of literal statements shows that our best model generates metaphors better than three well-crafted baselines 66% of the time on average. A task-based evaluation shows that human-written poems enhanced with metaphors proposed by our model are preferred 68% of the time compared to poems without metaphors.
Intelligent behavior depends on the ecological niche: Scaling up AI to human-like intelligence in socio-cultural environments
Authors: Manfred Eppe, Pierre-Yves Oudeyer
Date: 2021-03-11 00:00:00

This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence. We explain how developmental and evolutionary theories of human cognition should further inform artificial intelligence. We emphasize the role of ecological niches in sculpting intelligent behavior, and in particular that human intelligence was fundamentally shaped to adapt to a constantly changing socio-cultural environment. We argue that a major limit of current work in AI is that it is missing this perspective, both theoretically and experimentally. Finally, we discuss the promising approach of developmental artificial intelligence, modeling infant development through multi-scale interaction between intrinsically motivated learning, embodiment and a fastly changing socio-cultural environment. This paper takes the form of an interview of Pierre-Yves Oudeyer by Mandred Eppe, organized within the context of a KI - K{\"{u}}nstliche Intelligenz special issue in developmental robotics.
Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm
Authors: Mert Seker, Anssi Männistö, Alexandros Iosifidis, Jenni Raitoharju
Date: 2021-03-11 00:00:00
Tasks: Human Detection, object-detection, Object Detection, Pose Estimation

The COVID-19 virus has caused a global pandemic since March 2020. The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of the most important measures is social distancing. Maintaining a minimum of one meter distance from other people is strongly suggested to reduce the risk of infection. This has created a strong interest in monitoring the social distances either as a safety measure or to study how the measures have affected human behavior and country-wise differences in this. The need for automatic social distance estimation algorithms is evident, but there is no suitable test benchmark for such algorithms. Collecting images with measured ground-truth pair-wise distances between all the people using different camera settings is cumbersome. Furthermore, performance evaluation for social distance estimation algorithms is not straightforward and there is no widely accepted evaluation protocol. In this paper, we provide a dataset of varying images with measured pair-wise social distances under different camera positionings and focal length values. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate social distance estimation algorithms. We also propose a method for automatic social distance estimation. Our method takes advantage of object detection and human pose estimation. It can be applied on any single image as long as focal length and sensor size information are known. The results on our benchmark are encouraging with 92% human detection rate and only 28.9% average error in distance estimation among the detected people.
ENTRUST: Argument Reframing with Language Models and Entailment
Authors: Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan
Date: 2021-03-11 00:00:00
Proceeding: NAACL 2021 4
Tasks: Text Generation

Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman 1983). Differences in lexical framing, the focus of our work, can have large effects on peoples' opinions and beliefs. To make progress towards reframing arguments for positive effects, we create a dataset and method for this task. We use a lexical resource for "connotations" to create a parallel corpus and propose a method for argument reframing that combines controllable text generation (positive connotation) with a post-decoding entailment component (same denotation). Our results show that our method is effective compared to strong baselines along the dimensions of fluency, meaning, and trustworthiness/reduction of fear.
Auto-COP: Adaptation Generation in Context-Oriented Programming using Reinforcement Learning Options
Authors: Nicolás Cardozo, Ivana Dusparic
Date: 2021-03-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Self-adaptive software systems continuously adapt in response to internal and external changes in their execution environment, captured as contexts. The COP paradigm posits a technique for the development of self-adaptive systems, capturing their main characteristics with specialized programming language constructs. COP adaptations are specified as independent modules composed in and out of the base system as contexts are activated and deactivated in response to sensed circumstances from the surrounding environment. However, the definition of adaptations, their contexts and associated specialized behavior, need to be specified at design time. In complex CPS this is intractable due to new unpredicted operating conditions. We propose Auto-COP, a new technique to enable generation of adaptations at run time. Auto-COP uses RL options to build action sequences, based on the previous instances of the system execution. Options are explored in interaction with the environment, and the most suitable options for each context are used to generate adaptations exploiting COP. To validate Auto-COP, we present two case studies exhibiting different system characteristics and application domains: a driving assistant and a robot delivery system. We present examples of Auto-COP code generated at run time, to illustrate the types of circumstances (contexts) requiring adaptation, and the corresponding generated adaptations for each context. We confirm that the generated adaptations exhibit correct system behavior measured by domain-specific performance metrics, while reducing the number of required execution/actuation steps by a factor of two showing that the adaptations are regularly selected by the running system as adaptive behavior is more appropriate than the execution of primitive actions.
Knowledge Graph Question Answering using Graph-Pattern Isomorphism
Authors: Daniel Vollmers, Rricha Jalota, Diego Moussallem, Hardik Topiwala, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck
Date: 2021-03-11 00:00:00
Tasks: Domain Adaptation, Graph Question Answering, Question Answering

Knowledge Graph Question Answering (KGQA) systems are based on machine learning algorithms, requiring thousands of question-answer pairs as training examples or natural language processing pipelines that need module fine-tuning. In this paper, we present a novel QA approach, dubbed TeBaQA. Our approach learns to answer questions based on graph isomorphisms from basic graph patterns of SPARQL queries. Learning basic graph patterns is efficient due to the small number of possible patterns. This novel paradigm reduces the amount of training data necessary to achieve state-of-the-art performance. TeBaQA also speeds up the domain adaption process by transforming the QA system development task into a much smaller and easier data compilation task. In our evaluation, TeBaQA achieves state-of-the-art performance on QALD-8 and delivers comparable results on QALD-9 and LC-QuAD v1. Additionally, we performed a fine-grained evaluation on complex queries that deal with aggregation and superlative questions as well as an ablation study, highlighting future research challenges.
ChallenCap: Monocular 3D Capture of Challenging Human Performances using Multi-Modal References
Authors: Yannan He, Anqi Pang, Xin Chen, Han Liang, Minye Wu, Yuexin Ma, Lan Xu
Date: 2021-03-11 00:00:00
Proceeding: CVPR 2021 1
Tasks: Decoder

Capturing challenging human motions is critical for numerous applications, but it suffers from complex motion patterns and severe self-occlusion under the monocular setting. In this paper, we propose ChallenCap -- a template-based approach to capture challenging 3D human motions using a single RGB camera in a novel learning-and-optimization framework, with the aid of multi-modal references. We propose a hybrid motion inference stage with a generation network, which utilizes a temporal encoder-decoder to extract the motion details from the pair-wise sparse-view reference, as well as a motion discriminator to utilize the unpaired marker-based references to extract specific challenging motion characteristics in a data-driven manner. We further adopt a robust motion optimization stage to increase the tracking accuracy, by jointly utilizing the learned motion details from the supervised multi-modal references as well as the reliable motion hints from the input image reference. Extensive experiments on our new challenging motion dataset demonstrate the effectiveness and robustness of our approach to capture challenging human motions.
Multi-objective discovery of PDE systems using evolutionary approach
Authors: Mikhail Maslyaev, Alexander Hvatov
Date: 2021-03-11 00:00:00
Tasks: Form

Usually, the systems of partial differential equations (PDEs) are discovered from observational data in the single vector equation form. However, this approach restricts the application to the real cases, where, for example, the form of the external forcing is of interest. In the paper, a multi-objective co-evolution algorithm is described. The single equations within the system and the system itself are evolved simultaneously to obtain the system. This approach allows discovering the systems with the form-independent equations. In contrast to the single vector equation, a component-wise system is more suitable for expert interpretation and, therefore, for applications. The example of the two-dimensional Navier-Stokes equation is considered.
Intraclass clustering: an implicit learning ability that regularizes DNNs
Authors: Carbonnelle Simon, Christophe De Vleeschouwer
Date: 2021-03-11 00:00:00
Proceeding: ICLR 2021 1
Tasks: Clustering, Data Augmentation

Several works have shown that the regularization mechanisms underlying deep neural networks' generalization performances are still poorly understood. In this paper, we hypothesize that deep neural networks are regularized through their ability to extract meaningful clusters among the samples of a class. This constitutes an implicit form of regularization, as no explicit training mechanisms or supervision target such behaviour. To support our hypothesis, we design four different measures of intraclass clustering, based on the neuron- and layer-level representations of the training data. We then show that these measures constitute accurate predictors of generalization performance across variations of a large set of hyperparameters (learning rate, batch size, optimizer, weight decay, dropout rate, data augmentation, network depth and width).
Hybrid Physics and Deep Learning Model for Interpretable Vehicle State Prediction
Authors: Alexandra Baier, Zeyd Boukhers, Steffen Staab
Date: 2021-03-11 00:00:00

Physical motion models offer interpretable predictions for the motion of vehicles. However, some model parameters, such as those related to aero- and hydrodynamics, are expensive to measure and are often only roughly approximated reducing prediction accuracy. Recurrent neural networks achieve high prediction accuracy at low cost, as they can use cheap measurements collected during routine operation of the vehicle, but their results are hard to interpret. To precisely predict vehicle states without expensive measurements of physical parameters, we propose a hybrid approach combining deep learning and physical motion models including a novel two-phase training procedure. We achieve interpretability by restricting the output range of the deep neural network as part of the hybrid model, which limits the uncertainty introduced by the neural network to a known quantity. We have evaluated our approach for the use case of ship and quadcopter motion. The results show that our hybrid model can improve model interpretability with no decrease in accuracy compared to existing deep learning approaches.
Variational inference with a quantum computer
Authors: Marcello Benedetti, Brian Coyle, Mattia Fiorentini, Michael Lubasch, Matthias Rosenkranz
Date: 2021-03-11 00:00:00
Tasks: Variational Inference

Inference is the task of drawing conclusions about unobserved variables given observations of related variables. Applications range from identifying diseases from symptoms to classifying economic regimes from price movements. Unfortunately, performing exact inference is intractable in general. One alternative is variational inference, where a candidate probability distribution is optimized to approximate the posterior distribution over unobserved variables. For good approximations, a flexible and highly expressive candidate distribution is desirable. In this work, we use quantum Born machines as variational distributions over discrete variables. We apply the framework of operator variational inference to achieve this goal. In particular, we adopt two specific realizations: one with an adversarial objective and one based on the kernelized Stein discrepancy. We demonstrate the approach numerically using examples of Bayesian networks, and implement an experiment on an IBM quantum computer. Our techniques enable efficient variational inference with distributions beyond those that are efficiently representable on a classical computer.
Learning Word-Level Confidence For Subword End-to-End ASR
Authors: David Qiu, Qiujia Li, Yanzhang He, Yu Zhang, Bo Li, Liangliang Cao, Rohit Prabhavalkar, Deepti Bhatia, Wei Li, Ke Hu, Tara N. Sainath, Ian McGraw
Date: 2021-03-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Model Selection, speech-recognition, Speech Recognition

We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR). Although prior works have proposed training auxiliary confidence models for ASR systems, they do not extend naturally to systems that operate on word-pieces (WP) as their vocabulary. In particular, ground truth WP correctness labels are needed for training confidence models, but the non-unique tokenization from word to WP causes inaccurate labels to be generated. This paper proposes and studies two confidence models of increasing complexity to solve this problem. The final model uses self-attention to directly learn word-level confidence without needing subword tokenization, and exploits full context features from multiple hypotheses to improve confidence accuracy. Experiments on Voice Search and long-tail test sets show standard metrics (e.g., NCE, AUC, RMSE) improving substantially. The proposed confidence module also enables a model selection approach to combine an on-device E2E model with a hybrid model on the server to address the rare word recognition problem for the E2E model.
A semi-agnostic ansatz with variable structure for quantum machine learning
Authors: M. Bilkis, M. Cerezo, Guillaume Verdon, Patrick J. Coles, Lukasz Cincio
Date: 2021-03-11 00:00:00
Tasks: BIG-bench Machine Learning, Data Compression, Quantum Machine Learning

Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.