pwc-archive/papers-with-abstracts huggingface dataset:
Camera Calibration with Pose Guidance
Authors: Yuzhuo Ren, Feng Hu
Date: 2021-02-19 00:00:00
Tasks: Autonomous Driving, Camera Calibration

Camera calibration plays a critical role in various computer vision tasks such as autonomous driving or augmented reality. Widely used camera calibration tools utilize plane pattern based methodology, such as using a chessboard or AprilTag board, user's calibration expertise level significantly affects calibration accuracy and consistency when without clear instruction. Furthermore, calibration is a recurring task that has to be performed each time the camera is changed or moved. It's also a great burden to calibrate huge amounts of cameras such as Driver Monitoring System (DMS) cameras in a production line with millions of vehicles. To resolve above issues, we propose a calibration system called Calibration with Pose Guidance to improve calibration accuracy, reduce calibration variance among different users or different trials of the same person. Experiment result shows that our proposed method achieves more accurate and consistent calibration than traditional calibration tools.
A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information
Authors: Cindy Trinh, Richard Combes
Date: 2021-02-19 00:00:00

Motivated by applications in cognitive radio networks, we consider the decentralized multi-player multi-armed bandit problem, without collision nor sensing information. We propose Randomized Selfish KL-UCB, an algorithm with very low computational complexity, inspired by the Selfish KL-UCB algorithm, which has been abandoned as it provably performs sub-optimally in some cases. We subject Randomized Selfish KL-UCB to extensive numerical experiments showing that it far outperforms state-of-the-art algorithms in almost all environments, sometimes by several orders of magnitude, and without the additional knowledge required by state-of-the-art algorithms. We also emphasize the potential of this algorithm for the more realistic dynamic setting, and support our claims with further experiments. We believe that the low complexity and high performance of Randomized Selfish KL-UCB makes it the most suitable for implementation in practical systems amongst known algorithms.
Information-Theoretic Bounds for Integral Estimation
Authors: Donald Q. Adams, Adarsh Barik, Jean Honorio
Date: 2021-02-19 00:00:00

In this paper, we consider a zero-order stochastic oracle model of estimating definite integrals. In this model, integral estimation methods may query an oracle function for a fixed number of noisy values of the integrand function and use these values to produce an estimate of the integral. We first show that the information-theoretic error lower bound for estimating the integral of a $d$-dimensional function over a region with $l_\infty$ radius $r$ using at most $T$ queries to the oracle function is $\Omega(2^d r^{d+1}\sqrt{d/T})$. Additionally, we find that the Gaussian Quadrature method under the same model achieves a rate of $O(2^{d}r^d/\sqrt{T})$ for functions with zero fourth and higher-order derivatives with respect to individual dimensions, and for Gaussian oracles, this rate is tight. For functions with nonzero fourth derivatives, the Gaussian Quadrature method achieves an upper bound which is not tight with the information-theoretic lower bound. Therefore, it is not minimax optimal, so there is space for the development of better integral estimation methods for such functions.
Quantifying Variational Approximation for the Log-Partition Function
Authors: Romain Cosson, Devavrat Shah
Date: 2021-02-19 00:00:00
Tasks: Variational Inference

Variational approximation, such as mean-field (MF) and tree-reweighted (TRW), provide a computationally efficient approximation of the log-partition function for a generic graphical model. TRW provably provides an upper bound, but the approximation ratio is generally not quantified. As the primary contribution of this work, we provide an approach to quantify the approximation ratio through the property of the underlying graph structure. Specifically, we argue that (a variant of) TRW produces an estimate that is within factor $\frac{1}{\sqrt{\kappa(G)}}$ of the true log-partition function for any discrete pairwise graphical model over graph $G$, where $\kappa(G) \in (0,1]$ captures how far $G$ is from tree structure with $\kappa(G) = 1$ for trees and $2/N$ for the complete graph over $N$ vertices. As a consequence, the approximation ratio is $1$ for trees, $\sqrt{(d+1)/2}$ for any graph with maximum average degree $d$, and $\stackrel{\beta\to\infty}{\approx} 1+1/(2\beta)$ for graphs with girth (shortest cycle) at least $\beta \log N$. In general, $\kappa(G)$ is the solution of a max-min problem associated with $G$ that can be evaluated in polynomial time for any graph. Using samples from the uniform distribution over the spanning trees of G, we provide a near linear-time variant that achieves an approximation ratio equal to the inverse of square-root of minimal (across edges) effective resistance of the graph. We connect our results to the graph partition-based approximation method and thus provide a unified perspective. Keywords: variational inference, log-partition function, spanning tree polytope, minimum effective resistance, min-max spanning tree, local inference
Adaptable Deformable Convolutions for Semantic Segmentation of Fisheye Images in Autonomous Driving Systems
Authors: Clément Playout, Ola Ahmad, Freddy Lecue, Farida Cheriet
Date: 2021-02-19 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Semantic Segmentation

Advanced Driver-Assistance Systems rely heavily on perception tasks such as semantic segmentation where images are captured from large field of view (FoV) cameras. State-of-the-art works have made considerable progress toward applying Convolutional Neural Network (CNN) to standard (rectilinear) images. However, the large FoV cameras used in autonomous vehicles produce fisheye images characterized by strong geometric distortion. This work demonstrates that a CNN trained on standard images can be readily adapted to fisheye images, which is crucial in real-world applications where time-consuming real-time data transformation must be avoided. Our adaptation protocol mainly relies on modifying the support of the convolutions by using their deformable equivalents on top of pre-existing layers. We prove that tuning an optimal support only requires a limited amount of labeled fisheye images, as a small number of training samples is sufficient to significantly improve an existing model's performance on wide-angle images. Furthermore, we show that finetuning the weights of the network is not necessary to achieve high performance once the deformable components are learned. Finally, we provide an in-depth analysis of the effect of the deformable convolutions, bringing elements of discussion on the behavior of CNN models.
Keyboards as a new model of computation
Authors: Yoan Géran, Bastien Laboureix, Corto Mascle, Valentin D. Richard
Date: 2021-02-19 00:00:00
Tasks: model

We introduce a new formalisation of languages, called keyboards. We consider a set of elementary operations (writing/erasing a letter, going to the right or to the left,...) and we define a keyboard as a set of finite sequences of such operations, called keys. The corresponding language is the set of words obtained by applying some sequence of those keys. Unlike classical models of computation, every key can be applied anytime. We define various classes of languages based on different sets of elementary operations, and compare their expressive powers. We also compare them to well-known classes of languages (Chomsky hierarchy). We obtain a strict hierarchy of languages, whose expressivity is orthogonal to the one of the aforementionned classical models. -- Nous introduisons une nouvelle repr\'esentation de langages, les claviers. On se munit d'un ensemble d'op\'erations \'el\'ementaires (ajout, effacement d'une lettre, d\'eplacement \`a droite, \`a gauche, ...), et on d\'efinit un clavier comme un ensemble de suites finies d'op\'erations \'el\'ementaires, appel\'ees touches. Son langage sera l'ensemble des mots obtenus en appliquant une suite quelconque de touches. Contrairement \`a des mod\`eles de calcul classiques, toutes les touches peuvent \^etre appliqu\'ees \`a tout moment. En premier lieu nous d\'efinissons diff\'erentes classes de claviers en faisant varier l'ensemble des op\'erations \'el\'ementaires autoris\'ees, et nous comparons l'expressivit\'e des classes de langages obtenues. Nous comparons \'egalement ces classes \`a la hi\'erarchie de Chomsky. Nous obtenons que toutes les classes \'etudi\'ees sont diff\'erentes, et nous caract\'erisons les classes inclues dans les rationnels et les alg\'ebriques. L'expressivit\'e des claviers semble orthogonale \`a celle des mod\`eles \'evoqu\'es pr\'ec\'edemment.
Conditional Adversarial Networks for Multi-Domain Text Classification
Authors: Yuan Wu, Diana Inkpen, Ahmed El-Roby
Date: 2021-02-19 00:00:00
Proceeding: EACL (AdaptNLP) 2021 4
Tasks: Classification, General Classification, text-classification, Text Classification

In this paper, we propose conditional adversarial networks (CANs), a framework that explores the relationship between the shared features and the label predictions to impose more discriminability to the shared features, for multi-domain text classification (MDTC). The proposed CAN introduces a conditional domain discriminator to model the domain variance in both shared feature representations and class-aware information simultaneously and adopts entropy conditioning to guarantee the transferability of the shared features. We provide theoretical analysis for the CAN framework, showing that CAN's objective is equivalent to minimizing the total divergence among multiple joint distributions of shared features and label predictions. Therefore, CAN is a theoretically sound adversarial network that discriminates over multiple distributions. Evaluation results on two MDTC benchmarks show that CAN outperforms prior methods. Further experiments demonstrate that CAN has a good ability to generalize learned knowledge to unseen domains.
Co-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
Authors: Yu Zhu, Boning Li, Santiago Segarra
Date: 2021-02-19 00:00:00
Tasks: Clustering, hypergraph partitioning

We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every vertex to each of its incident hyperedges is represented through an edge-dependent weight, conferring the model higher expressivity than the classical hypergraph. In our method, we leverage random walks with EDVWs to construct a hypergraph Laplacian and use its spectral properties to embed vertices and hyperedges in a common space. We then cluster these embeddings to obtain our proposed co-clustering method, of particular relevance in applications requiring the simultaneous clustering of data entities and features. Numerical experiments using real-world data demonstrate the effectiveness of our proposed approach in comparison with state-of-the-art alternatives.
Spatial-temporal switching estimators for imaging locally concentrated dynamics
Authors: Parisa Karimi, Mark Butala, Zhizhen Zhao, Farzad Kamalabadi
Date: 2021-02-19 00:00:00
Tasks: Denoising

The evolution of images with physics-based dynamics is often spatially localized and nonlinear. A switching linear dynamic system (SLDS) is a natural model under which to pose such problems when the system's evolution randomly switches over the observation interval. Because of the high parameter space dimensionality, efficient and accurate recovery of the underlying state is challenging. The work presented in this paper focuses on the common cases where the dynamic evolution may be adequately modeled as a collection of decoupled, locally concentrated dynamic operators. Patch-based hybrid estimators are proposed for real-time reconstruction of images from noisy measurements given perfect or partial information about the underlying system dynamics. Numerical results demonstrate the effectiveness of the proposed approach for denoising in a realistic data-driven simulation of remotely sensed cloud dynamics.
Clarification of Video Retrieval Query Results by the Automated Insertion of Supporting Shots
Authors: Sean Butler
Date: 2021-02-19 00:00:00
Tasks: Retrieval, Video Editing, Video Retrieval

Computational Video Editing Systems output video generally follows a particular form, e.g. conversation or music videos, in this way they are domain specific. We describe a recent development in our video annotation and segmentation system to support general computational video editing in which we derive a single generic editing strategy from general cinema narrative principles instead of using a hierarchical film gram-mar. We demonstrate how this single principle coupled with a database of scripts derived from annotated videos leverages the existing video editing knowledge encoded within the editing of those sequences in a flexible and generic manner. We discuss the cinema theory foundations for this generic editing strategy, review the algorithms used to effect it, and goon by means of examples to show its appropriateness in an automated system.
Multi-Domain Adaptation in Neural Machine Translation Through Multidimensional Tagging
Authors: Emmanouil Stergiadis, Satendra Kumar, Fedor Kovalev, Pavel Levin
Date: 2021-02-19 00:00:00
Proceeding: MTSummit 2021 8
Tasks: Articles, Domain Adaptation, Machine Translation, NMT, Sentence, Translation

While NMT has achieved remarkable results in the last 5 years, production systems come with strict quality requirements in arbitrarily niche domains that are not always adequately covered by readily available parallel corpora. This is typically addressed by training domain specific models, using fine-tuning methods and some variation of back-translation on top of in-domain monolingual corpora. However, industrial practitioners can rarely afford to focus on a single domain. A far more typical scenario includes a set of closely related, yet succinctly different sub-domains. At Booking.com, we need to translate property descriptions, user reviews, as well as messages, (for example those sent between a customer and an agent or property manager). An editor might need to translate articles across a set of different topics. An e-commerce platform would typically need to translate both the description of each item and the user generated content related to them. To this end, we propose MDT: a novel method to simultaneously fine-tune on several sub-domains by passing multidimensional sentence-level information to the model during training and inference. We show that MDT achieves results competitive to N specialist models each fine-tuned on a single constituent domain, while effectively serving all N sub-domains, therefore cutting development and maintenance costs by the same factor. Besides BLEU (industry standard automatic evaluation metric known to only weakly correlate with human judgement) we also report rigorous human evaluation results for all models and sub-domains as well as specific examples that better contextualise the performance of each model in terms of adequacy and fluency. To facilitate further research, we plan to make the code available upon acceptance.
Learning to Persuade on the Fly: Robustness Against Ignorance
Authors: You Zu, Krishnamurthy Iyer, Haifeng Xu
Date: 2021-02-19 00:00:00
Tasks: Persuasiveness, Philosophy

Motivated by information sharing in online platforms, we study repeated persuasion between a sender and a stream of receivers where at each time, the sender observes a payoff-relevant state drawn independently and identically from an unknown distribution, and shares state information with the receivers who each choose an action. The sender seeks to persuade the receivers into taking actions aligned with the sender's preference by selectively sharing state information. However, in contrast to the standard models, neither the sender nor the receivers know the distribution, and the sender has to persuade while learning the distribution on the fly. We study the sender's learning problem of making persuasive action recommendations to achieve low regret against the optimal persuasion mechanism with the knowledge of the distribution. To do this, we first propose and motivate a persuasiveness criterion for the unknown distribution setting that centers robustness as a requirement in the face of uncertainty. Our main result is an algorithm that, with high probability, is robustly-persuasive and achieves $O(\sqrt{T\log T})$ regret, where $T$ is the horizon length. Intuitively, at each time our algorithm maintains a set of candidate distributions, and chooses a signaling mechanism that is simultaneously persuasive for all of them. Core to our proof is a tight analysis about the cost of robust persuasion, which may be of independent interest. We further prove that this regret order is optimal (up to logarithmic terms) by showing that no algorithm can achieve regret better than $\Omega(\sqrt{T})$.
A theory of capacity and sparse neural encoding
Authors: Pierre Baldi, Roman Vershynin
Date: 2021-02-19 00:00:00

Motivated by biological considerations, we study sparse neural maps from an input layer to a target layer with sparse activity, and specifically the problem of storing $K$ input-target associations $(x,y)$, or memories, when the target vectors $y$ are sparse. We mathematically prove that $K$ undergoes a phase transition and that in general, and somewhat paradoxically, sparsity in the target layers increases the storage capacity of the map. The target vectors can be chosen arbitrarily, including in random fashion, and the memories can be both encoded and decoded by networks trained using local learning rules, including the simple Hebb rule. These results are robust under a variety of statistical assumptions on the data. The proofs rely on elegant properties of random polytopes and sub-gaussian random vector variables. Open problems and connections to capacity theories and polynomial threshold maps are discussed.
Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems
Authors: Dimitrios Michael Manias, Abdallah Shami
Date: 2021-02-19 00:00:00
Tasks: Federated Learning

With the incoming introduction of 5G networks and the advancement in technologies, such as Network Function Virtualization and Software Defined Networking, new and emerging networking technologies and use cases are taking shape. One such technology is the Internet of Vehicles (IoV), which describes an interconnected system of vehicles and infrastructure. Coupled with recent developments in artificial intelligence and machine learning, the IoV is transformed into an Intelligent Transportation System (ITS). There are, however, several operational considerations that hinder the adoption of ITS systems, including scalability, high availability, and data privacy. To address these challenges, Federated Learning, a collaborative and distributed intelligence technique, is suggested. Through an ITS case study, the ability of a federated model deployed on roadside infrastructure throughout the network to recover from faults by leveraging group intelligence while reducing recovery time and restoring acceptable system performance is highlighted. With a multitude of use cases and benefits, Federated Learning is a key enabler for ITS and is poised to achieve widespread implementation in 5G and beyond networks and applications.
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning
Authors: D. Dang, S. V. R. Chittamuru, S. Pasricha, R. Mahapatra, D. Sahoo
Date: 2021-02-19 00:00:00
Tasks: Computational Efficiency, Deep Learning

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP). This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high performance deep learning training. We present the design for a convolutional neural network, BPLight-CNN, which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves (i) at least 34x speedup, 34x improvement in computational efficiency, and 38.5x energy savings, during training; and (ii) 29x speedup, 31x improvement in computational efficiency, and 38.7x improvement in energy savings, during inference compared to the state-of-the-art designs. All these comparisons are done at a 16-bit resolution; and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared to the state-of-the-art.
Hamiltonian-Driven Shadow Tomography of Quantum States
Authors: Hong-Ye Hu, Yi-Zhuang You
Date: 2021-02-19 00:00:00

Classical shadow tomography provides an efficient method for predicting functions of an unknown quantum state from a few measurements of the state. It relies on a unitary channel that efficiently scrambles the quantum information of the state to the measurement basis. Facing the challenge of realizing deep unitary circuits on near-term quantum devices, we explore the scenario in which the unitary channel can be shallow and is generated by a quantum chaotic Hamiltonian via time evolution. We provide an unbiased estimator of the density matrix for all ranges of the evolution time. We analyze the sample complexity of the Hamiltonian-driven shadow tomography. For Pauli observables, we find that it can be more efficient than the unitary-2-design-based shadow tomography in a sequence of intermediate time windows that range from an order-1 scrambling time to a time scale of $D^{1/6}$, given the Hilbert space dimension $D$. In particular, the efficiency of predicting diagonal Pauli observables is improved by a factor of $D$ without sacrificing the efficiency of predicting off-diagonal Pauli observables.
Scaling up DNA digital data storage by efficiently predicting DNA hybridisation using deep learning
Authors: David Buterez
Date: 2021-02-19 00:00:00

Deoxyribonucleic acid (DNA) has shown great promise in enabling computational applications, most notably in the fields of DNA digital data storage and DNA computing. Information is encoded as DNA strands, which will naturally bind in solution, thus enabling search and pattern-matching capabilities. Being able to control and predict the process of DNA hybridisation is crucial for the ambitious future of Hybrid Molecular-Electronic Computing. Current tools are, however, limited in terms of throughput and applicability to large-scale problems. We present the first comprehensive study of machine learning methods applied to the task of predicting DNA hybridisation. For this purpose, we introduce an in silico-generated hybridisation dataset of over 2.5 million data points, enabling the use of deep learning. Depending on hardware, we achieve a reduction in inference time ranging from one to over two orders of magnitude compared to the state-of-the-art, while retaining high fidelity. We then discuss the integration of our methods in modern, scalable workflows.