pwc-archive/papers-with-abstracts huggingface dataset:
MUMBO: MUlti-task Max-value Bayesian Optimization
Authors: Henry B. Moss, David S. Leslie, Paul Rayson
Date: 2020-06-22 00:00:00
Tasks: Bayesian Optimization

We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functions somehow related to our true target function. This is a broad class of problems including the popular task of multi-fidelity optimization. However, while information-theoretic acquisition functions are known to provide state-of-the-art Bayesian optimization, existing implementations for multi-task scenarios have prohibitive computational requirements. Previous acquisition functions have therefore been suitable only for problems with both low-dimensional parameter spaces and function query costs sufficiently large to overshadow very significant optimization overheads. In this work, we derive a novel multi-task version of entropy search, delivering robust performance with low computational overheads across classic optimization challenges and multi-task hyper-parameter tuning. MUMBO is scalable and efficient, allowing multi-task Bayesian optimization to be deployed in problems with rich parameter and fidelity spaces.
High-Resolution Air Quality Prediction Using Low-Cost Sensors
Authors: Thibaut Cassard, Grégoire Jauvion, David Lissmyr
Date: 2020-06-22 00:00:00
Tasks: Prediction, Vocal Bursts Intensity Prediction

The use of low-cost sensors in air quality monitoring networks is still a much-debated topic among practitioners: they are much cheaper than traditional air quality monitoring stations set up by public authorities (a few hundred dollars compared to a few dozens of thousand dollars) at the cost of a lower accuracy and robustness. This paper presents a case study of using low-cost sensors measurements in an air quality prediction engine. The engine predicts jointly PM2.5 and PM10 concentrations in the United States at a very high resolution in the range of a few dozens of meters. It is fed with the measurements provided by official air quality monitoring stations, the measurements provided by a network of more than 4000 low-cost sensors across the country, and traffic estimates. We show that the use of low-cost sensors' measurements improves the engine's accuracy very significantly. In particular, we derive a strong link between the density of low-cost sensors and the predictions' accuracy: the more low-cost sensors are in an area, the more accurate are the predictions. As an illustration, in areas with the highest density of low-cost sensors, the low-cost sensors' measurements bring a 25% and 15% improvement in PM2.5 and PM10 predictions' accuracy respectively. An other strong conclusion is that in some areas with a high density of low-cost sensors, the engine performs better when fed with low-cost sensors' measurements only than when fed with official monitoring stations' measurements only: this suggests that an air quality monitoring network composed of low-cost sensors is effective in monitoring air quality. This is a very important result, as such a monitoring network is much cheaper to set up.
Deep Low-rank Prior in Dynamic MR Imaging
Authors: Ziwen Ke, Wenqi Huang, Jing Cheng, Zhuoxu Cui, Sen Jia, Haifeng Wang, Xin Liu, Hairong Zheng, Leslie Ying, Yanjie Zhu, Dong Liang
Date: 2020-06-22 00:00:00
Tasks: Rolling Shutter Correction

The deep learning methods have achieved attractive performance in dynamic MR cine imaging. However, all of these methods are only driven by the sparse prior of MR images, while the important low-rank (LR) prior of dynamic MR cine images is not explored, which limits the further improvements on dynamic MR reconstruction. In this paper, a learned singular value thresholding (Learned-SVT) operation is proposed to explore deep low-rank prior in dynamic MR imaging for obtaining improved reconstruction results. In particular, we come up with two novel and distinct schemes to introduce the learnable low-rank prior into deep network architectures in an unrolling manner and a plug-and-play manner respectively. In the unrolling manner, we put forward a model-based unrolling sparse and low-rank network for dynamic MR imaging, dubbed SLR-Net. The SLR-Net is defined over a deep network flow graph, which is unrolled from the iterative procedures in the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a sparse and low-rank based dynamic MRI model. In the plug-and-play manner, we present a plug-and-play LR network module that can be easily embedded into any other dynamic MR neural networks without changing the network paradigm. Experimental results show that both schemes can further improve the state-of-the-art CS methods, such as k-t SLR, and sparsity-driven deep learning-based methods, such as DC-CNN and CRNN, both qualitatively and quantitatively.
Progressive Graph Learning for Open-Set Domain Adaptation
Authors: Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh
Date: 2020-06-22 00:00:00
Proceeding: ICML 2020 1
Tasks: Domain Adaptation, Graph Learning, Graph Neural Network

Domain shift is a fundamental problem in visual recognition which typically arises when the source and target data follow different distributions. The existing domain adaptation approaches which tackle this problem work in the closed-set setting with the assumption that the source and the target data share exactly the same classes of objects. In this paper, we tackle a more realistic problem of open-set domain shift where the target data contains additional classes that are not present in the source data. More specifically, we introduce an end-to-end Progressive Graph Learning (PGL) framework where a graph neural network with episodic training is integrated to suppress underlying conditional shift and adversarial learning is adopted to close the gap between the source and target distributions. Compared to the existing open-set adaptation approaches, our approach guarantees to achieve a tighter upper bound of the target error. Extensive experiments on three standard open-set benchmarks evidence that our approach significantly outperforms the state-of-the-arts in open-set domain adaptation.
Split to Be Slim: An Overlooked Redundancy in Vanilla Convolution
Authors: Qiulin Zhang, Zhuqing Jiang, Qishuo Lu, Jia'nan Han, Zhengxin Zeng, Shang-Hua Gao, Aidong Men
Date: 2020-06-22 00:00:00
Tasks: GPU

Many effective solutions have been proposed to reduce the redundancy of models for inference acceleration. Nevertheless, common approaches mostly focus on eliminating less important filters or constructing efficient operations, while ignoring the pattern redundancy in feature maps. We reveal that many feature maps within a layer share similar but not identical patterns. However, it is difficult to identify if features with similar patterns are redundant or contain essential details. Therefore, instead of directly removing uncertain redundant features, we propose a \textbf{sp}lit based \textbf{conv}olutional operation, namely SPConv, to tolerate features with similar patterns but require less computation. Specifically, we split input feature maps into the representative part and the uncertain redundant part, where intrinsic information is extracted from the representative part through relatively heavy computation while tiny hidden details in the uncertain redundant part are processed with some light-weight operation. To recalibrate and fuse these two groups of processed features, we propose a parameters-free feature fusion module. Moreover, our SPConv is formulated to replace the vanilla convolution in a plug-and-play way. Without any bells and whistles, experimental results on benchmarks demonstrate SPConv-equipped networks consistently outperform state-of-the-art baselines in both accuracy and inference time on GPU, with FLOPs and parameters dropped sharply.
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
Authors: Mingyi Shi, Kfir Aberman, Andreas Aristidou, Taku Komura, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen
Date: 2020-06-22 00:00:00

We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video.While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation. At the crux of our approach lies a deep neural network with embedded kinematic priors, which decomposes sequences of 2D joint positions into two separate attributes: a single, symmetric, skeleton, encoded by bone lengths, and a sequence of 3D joint rotations associated with global root positions and foot contact labels. These attributes are fed into an integrated forward kinematics (FK) layer that outputs 3D positions, which are compared to a ground truth. In addition, an adversarial loss is applied to the velocities of the recovered rotations, to ensure that they lie on the manifold of natural joint rotations. The key advantage of our approach is that it learns to infer natural joint rotations directly from the training data, rather than assuming an underlying model, or inferring them from joint positions using a data-agnostic IK solver. We show that enforcing a single consistent skeleton along with temporally coherent joint rotations constrains the solution space, leading to a more robust handling of self-occlusions and depth ambiguities.
Lipschitz Recurrent Neural Networks
Authors: N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney
Date: 2020-06-22 00:00:00
Proceeding: ICLR 2021 1
Tasks: Language Modeling, Language Modelling, Sequential Image Classification

Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity. This particular functional form facilitates stability analysis of the long-term behavior of the recurrent unit using tools from nonlinear systems theory. In turn, this enables architectural design decisions before experimentation. Sufficient conditions for global stability of the recurrent unit are obtained, motivating a novel scheme for constructing hidden-to-hidden matrices. Our experiments demonstrate that the Lipschitz RNN can outperform existing recurrent units on a range of benchmark tasks, including computer vision, language modeling and speech prediction tasks. Finally, through Hessian-based analysis we demonstrate that our Lipschitz recurrent unit is more robust with respect to input and parameter perturbations as compared to other continuous-time RNNs.
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
Authors: Grégoire Mialon, Dexiong Chen, Alexandre d'Aspremont, Julien Mairal
Date: 2020-06-22 00:00:00
Proceeding: ICLR 2021 1

We address the problem of learning on sets of features, motivated by the need of performing pooling operations in long biological sequences of varying sizes, with long-range dependencies, and possibly few labeled data. To address this challenging task, we introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost. Our aggregation technique admits two useful interpretations: it may be seen as a mechanism related to attention layers in neural networks, or it may be seen as a scalable surrogate of a classical optimal transport-based kernel. We experimentally demonstrate the effectiveness of our approach on biological sequences, achieving state-of-the-art results for protein fold recognition and detection of chromatin profiles tasks, and, as a proof of concept, we show promising results for processing natural language sequences. We provide an open-source implementation of our embedding that can be used alone or as a module in larger learning models at https://github.com/claying/OTK.
Deep Residual Mixture Models
Authors: Perttu Hämäläinen, Martin Trapp, Tuure Saloheimo, Arno Solin
Date: 2020-06-22 00:00:00
Tasks: BIG-bench Machine Learning

We propose Deep Residual Mixture Models (DRMMs), a novel deep generative model architecture. Compared to other deep models, DRMMs allow more flexible conditional sampling: The model can be trained once with all variables, and then used for sampling with arbitrary combinations of conditioning variables, Gaussian priors, and (in)equality constraints. This provides new opportunities for interactive and exploratory machine learning, where one should minimize the user waiting for retraining a model. We demonstrate DRMMs in constrained multi-limb inverse kinematics and controllable generation of animations.
Object Tracking through Residual and Dense LSTMs
Authors: Fabio Garcea, Alessandro Cucco, Lia Morra, Fabrizio Lamberti
Date: 2020-06-22 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking

Visual object tracking task is constantly gaining importance in several fields of application as traffic monitoring, robotics, and surveillance, to name a few. Dealing with changes in the appearance of the tracked object is paramount to achieve high tracking accuracy, and is usually achieved by continually learning features. Recently, deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative, bypassing the need to retrain the feature extraction in an online fashion. Inspired by the success of residual and dense networks in image recognition, we propose here to enhance the capabilities of hybrid trackers using residual and/or dense LSTMs. By introducing skip connections, it is possible to increase the depth of the architecture while ensuring a fast convergence. Experimental results on the Re3 tracker show that DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances such as occlusions and out-of-view objects. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
Differentiable Rendering: A Survey
Authors: Hiroharu Kato, Deniz Beker, Mihai Morariu, Takahiro Ando, Toru Matsuoka, Wadim Kehl, Adrien Gaidon
Date: 2020-06-22 00:00:00
Tasks: Image Segmentation, object-detection, Object Detection, Semantic Segmentation, Survey

Deep neural networks (DNNs) have shown remarkable performance improvements on vision-related tasks such as object detection or image segmentation. Despite their success, they generally lack the understanding of 3D objects which form the image, as it is not always possible to collect 3D information about the scene or to easily annotate it. Differentiable rendering is a novel field which allows the gradients of 3D objects to be calculated and propagated through images. It also reduces the requirement of 3D data collection and annotation, while enabling higher success rate in various applications. This paper reviews existing literature and discusses the current state of differentiable rendering, its applications and open research problems.
Few-shot 3D Point Cloud Semantic Segmentation
Authors: Na Zhao, Tat-Seng Chua, Gim Hee Lee
Date: 2020-06-22 00:00:00
Proceeding: CVPR 2021 1
Tasks: Few-shot 3D Point Cloud Semantic Segmentation, Segmentation, Semantic Segmentation

Many existing approaches for 3D point cloud semantic segmentation are fully supervised. These fully supervised approaches heavily rely on large amounts of labeled training data that are difficult to obtain and cannot segment new classes after training. To mitigate these limitations, we propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method to segment new classes given a few labeled examples. Specifically, each class is represented by multiple prototypes to model the complex data distribution of labeled points. Subsequently, we employ a transductive label propagation method to exploit the affinities between labeled multi-prototypes and unlabeled points, and among the unlabeled points. Furthermore, we design an attention-aware multi-level feature learning network to learn the discriminative features that capture the geometric dependencies and semantic correlations between points. Our proposed method shows significant and consistent improvements compared to baselines in different few-shot point cloud semantic segmentation settings (i.e., 2/3-way 1/5-shot) on two benchmark datasets. Our code is available at https://github.com/Na-Z/attMPTI.
Characterizing Hirability via Personality and Behavior
Authors: Harshit Malik, Hersh Dhillon, Roland Goecke, Ramanathan Subramanian
Date: 2020-06-22 00:00:00

While personality traits have been extensively modeled as behavioral constructs, we model \textbf{\textit{job hirability}} as a \emph{personality construct}. On the {\emph{First Impressions Candidate Screening}} (FICS) dataset, we examine relationships among personality and hirability measures. Modeling hirability as a discrete/continuous variable with the \emph{big-five} personality traits as predictors, we utilize (a) apparent personality annotations, and (b) personality estimates obtained via audio, visual and textual cues for hirability prediction (HP). We also examine the efficacy of a two-step HP process involving (1) personality estimation from multimodal behavioral cues, followed by (2) HP from personality estimates. Interesting results from experiments performed on $\approx$~5000 FICS videos are as follows. (1) For each of the \emph{text}, \emph{audio} and \emph{visual} modalities, HP via the above two-step process is more effective than directly predicting from behavioral cues. Superior results are achieved when hirability is modeled as a continuous vis-\'a-vis categorical variable. (2) Among visual cues, eye and bodily information achieve performance comparable to face cues for predicting personality and hirability. (3) Explanatory analyses reveal the impact of multimodal behavior on personality impressions; \eg, Conscientiousness impressions are impacted by the use of \emph{cuss words} (verbal behavior), and \emph{eye movements} (non-verbal behavior), confirming prior observations.
Clinical Predictive Keyboard using Statistical and Neural Language Modeling
Authors: John Pavlopoulos, Panagiotis Papapetrou
Date: 2020-06-22 00:00:00
Tasks: Language Modeling, Language Modelling

A language model can be used to predict the next word during authoring, to correct spelling or to accelerate writing (e.g., in sms or emails). Language models, however, have only been applied in a very small scale to assist physicians during authoring (e.g., discharge summaries or radiology reports). But along with the assistance to the physician, computer-based systems which expedite the patient's exit also assist in decreasing the hospital infections. We employed statistical and neural language modeling to predict the next word of a clinical text and assess all the models in terms of accuracy and keystroke discount in two datasets with radiology reports. We show that a neural language model can achieve as high as 51.3% accuracy in radiology reports (one out of two words predicted correctly). We also show that even when the models are employed only for frequent words, the physician can save valuable time.
Bandit algorithms: Letting go of logarithmic regret for statistical robustness
Authors: Kumar Ashutosh, Jayakrishnan Nair, Anmol Kagrecha, Krishna Jagannathan
Date: 2020-06-22 00:00:00

We study regret minimization in a stochastic multi-armed bandit setting and establish a fundamental trade-off between the regret suffered under an algorithm, and its statistical robustness. Considering broad classes of underlying arms' distributions, we show that bandit learning algorithms with logarithmic regret are always inconsistent and that consistent learning algorithms always suffer a super-logarithmic regret. This result highlights the inevitable statistical fragility of all `logarithmic regret' bandit algorithms available in the literature---for instance, if a UCB algorithm designed for $\sigma$-subGaussian distributions is used in a subGaussian setting with a mismatched variance parameter, the learning performance could be inconsistent. Next, we show a positive result: statistically robust and consistent learning performance is attainable if we allow the regret to be slightly worse than logarithmic. Specifically, we propose three classes of distribution oblivious algorithms that achieve an asymptotic regret that is arbitrarily close to logarithmic.
An Online Algorithm for Computation Offloading in Non-Stationary Environments
Authors: Aniq Ur Rahman, Gourab Ghatak, Antonio De Domenico
Date: 2020-06-22 00:00:00

We consider the latency minimization problem in a task-offloading scenario, where multiple servers are available to the user equipment for outsourcing computational tasks. To account for the temporally dynamic nature of the wireless links and the availability of the computing resources, we model the server selection as a multi-armed bandit (MAB) problem. In the considered MAB framework, rewards are characterized in terms of the end-to-end latency. We propose a novel online learning algorithm based on the principle of optimism in the face of uncertainty, which outperforms the state-of-the-art algorithms by up to ~1s. Our results highlight the significance of heavily discounting the past rewards in dynamic environments.
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Authors: Jinming Cao, Yangyan Li, Mingchao Sun, Ying Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen, Changhe Tu
Date: 2020-06-22 00:00:00
Tasks: image-classification, Image Classification

Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel. The composition of the two convolutions constitutes an over-parameterization, since it adds learnable parameters, while the resulting linear operation can be expressed by a single convolution layer. We refer to this depthwise over-parameterized convolutional layer as DO-Conv. We show with extensive experiments that the mere replacement of conventional convolutional layers with DO-Conv layers boosts the performance of CNNs on many classical vision tasks, such as image classification, detection, and segmentation. Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization. As DO-Conv introduces performance gains without incurring any computational complexity increase for inference, we advocate it as an alternative to the conventional convolutional layer. We open-source a reference implementation of DO-Conv in Tensorflow, PyTorch and GluonCV at https://github.com/yangyanli/DO-Conv.