pwc-archive/papers-with-abstracts huggingface dataset:
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Authors: Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
Date: 2020-11-01 00:00:00
Tasks: Gaussian Processes, Variational Inference

We show that the gradient estimates used in training Deep Gaussian Processes (DGPs) with importance-weighted variational inference are susceptible to signal-to-noise ratio (SNR) issues. Specifically, we show both theoretically and via an extensive empirical evaluation that the SNR of the gradient estimates for the latent variable's variational parameters decreases as the number of importance samples increases. As a result, these gradient estimates degrade to pure noise if the number of importance samples is too large. To address this pathology, we show how doubly reparameterized gradient estimators, originally proposed for training variational autoencoders, can be adapted to the DGP setting and that the resultant estimators completely remedy the SNR issue, thereby providing more reliable training. Finally, we demonstrate that our fix can lead to consistent improvements in the predictive performance of DGP models.
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent
Authors: Frederik Kunstner, Raunak Kumar, Mark Schmidt
Date: 2020-11-02 00:00:00

Expectation maximization (EM) is the default algorithm for fitting probabilistic models with missing or latent variables, yet we lack a full understanding of its non-asymptotic convergence properties. Previous works show results along the lines of "EM converges at least as fast as gradient descent" by assuming the conditions for the convergence of gradient descent apply to EM. This approach is not only loose, in that it does not capture that EM can make more progress than a gradient step, but the assumptions fail to hold for textbook examples of EM like Gaussian mixtures. In this work we first show that for the common setting of exponential family distributions, viewing EM as a mirror descent algorithm leads to convergence rates in Kullback-Leibler (KL) divergence. Then, we show how the KL divergence is related to first-order stationarity via Bregman divergences. In contrast to previous works, the analysis is invariant to the choice of parametrization and holds with minimal assumptions. We also show applications of these ideas to local linear (and superlinear) convergence rates, generalized EM, and non-exponential family distributions.
Evaluating Bias In Dutch Word Embeddings
Authors: Rodrigo Alejandro Chávez Mulsa, Gerasimos Spanakis
Date: 2020-10-31 00:00:00
Proceeding: GeBNLP (COLING) 2020 12
Tasks: Clustering, Sentence, Sentence Embeddings, Word Embeddings

Recent research in Natural Language Processing has revealed that word embeddings can encode social biases present in the training data which can affect minorities in real world applications. This paper explores the gender bias implicit in Dutch embeddings while investigating whether English language based approaches can also be used in Dutch. We implement the Word Embeddings Association Test (WEAT), Clustering and Sentence Embeddings Association Test (SEAT) methods to quantify the gender bias in Dutch word embeddings, then we proceed to reduce the bias with Hard-Debias and Sent-Debias mitigation methods and finally we evaluate the performance of the debiased embeddings in downstream tasks. The results suggest that, among others, gender bias is present in traditional and contextualized Dutch word embeddings. We highlight how techniques used to measure and reduce bias created for English can be used in Dutch embeddings by adequately translating the data and taking into account the unique characteristics of the language. Furthermore, we analyze the effect of the debiasing techniques on downstream tasks which show a negligible impact on traditional embeddings and a 2% decrease in performance in contextualized embeddings. Finally, we release the translated Dutch datasets to the public along with the traditional embeddings with mitigated bias.
Sampling-Decomposable Generative Adversarial Recommender
Authors: Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen
Date: 2020-11-02 00:00:00
Proceeding: NeurIPS 2020 12

Recommendation techniques are important approaches for alleviating information overload. Being often trained on implicit user feedback, many recommenders suffer from the sparsity challenge due to the lack of explicitly negative samples. The GAN-style recommenders (i.e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective. However, producing samples from the generator is very time-consuming, and our empirical study shows that the discriminator performs poor in top-k item recommendation. To this end, a theoretical analysis is made for the GAN-style algorithms, showing that the generator of limit capacity is diverged from the optimal generator. This may interpret the limitation of discriminator's performance. Based on these findings, we propose a Sampling-Decomposable Generative Adversarial Recommender (SD-GAR). In the framework, the divergence between some generator and the optimum is compensated by self-normalized importance sampling; the efficiency of sample generation is improved with a sampling-decomposable generator, such that each sample can be generated in O(1) with the Vose-Alias method. Interestingly, due to decomposability of sampling, the generator can be optimized with the closed-form solutions in an alternating manner, being different from policy gradient in the GAN-style algorithms. We extensively evaluate the proposed algorithm with five real-world recommendation datasets. The results show that SD-GAR outperforms IRGAN by 12.4% and the SOTA recommender by 10% on average. Moreover, discriminator training can be 20x faster on the dataset with more than 120K items.
Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference
Authors: Ali Lotfi Rezaabad, Rahi Kalantari, Sriram Vishwanath, Mingyuan Zhou, Jonathan Tamir
Date: 2020-10-31 00:00:00
Tasks: Graph Embedding, Link Prediction, Node Classification, Variational Inference

Efficient modeling of relational data arising in physical, social, and information sciences is challenging due to complicated dependencies within the data. In this work, we build off of semi-implicit graph variational auto-encoders to capture higher-order statistics in a low-dimensional graph latent representation. We incorporate hyperbolic geometry in the latent space through a Poincare embedding to efficiently represent graphs exhibiting hierarchical structure. To address the naive posterior latent distribution assumptions in classical variational inference, we use semi-implicit hierarchical variational Bayes to implicitly capture posteriors of given graph data, which may exhibit heavy tails, multiple modes, skewness, and highly correlated latent structures. We show that the existing semi-implicit variational inference objective provably reduces information in the observed graph. Based on this observation, we estimate and add an additional mutual information term to the semi-implicit variational inference learning objective to capture rich correlations arising between the input and latent spaces. We show that the inclusion of this regularization term in conjunction with the Poincare embedding boosts the quality of learned high-level representations and enables more flexible and faithful graphical modeling. We experimentally demonstrate that our approach outperforms existing graph variational auto-encoders both in Euclidean and in hyperbolic spaces for edge link prediction and node classification.
Searching k-Optimal Goals for an Orienteering Problem on a Specialized Graph with Budget Constraints
Authors: Abhinav Sharma, Advait Deshpande, Yanming Wang, Xinyi Xu, Prashan Madumal, Anbin Hou
Date: 2020-11-02 00:00:00

We propose a novel non-randomized anytime orienteering algorithm for finding k-optimal goals that maximize reward on a specialized graph with budget constraints. This specialized graph represents a real-world scenario which is analogous to an orienteering problem of finding k-most optimal goal states.
Multi-Agent Reinforcement Learning for Visibility-based Persistent Monitoring
Authors: Jingxi Chen, Amrish Baskaran, Zhongshun Zhang, Pratap Tokekar
Date: 2020-11-02 00:00:00
Tasks: Graph Attention, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The Visibility-based Persistent Monitoring (VPM) problem seeks to find a set of trajectories (or controllers) for robots to persistently monitor a changing environment. Each robot has a sensor, such as a camera, with a limited field-of-view that is obstructed by obstacles in the environment. The robots may need to coordinate with each other to ensure no point in the environment is left unmonitored for long periods of time. We model the problem such that there is a penalty that accrues every time step if a point is left unmonitored. However, the dynamics of the penalty are unknown to us. We present a Multi-Agent Reinforcement Learning (MARL) algorithm for the VPM problem. Specifically, we present a Multi-Agent Graph Attention Proximal Policy Optimization (MA-G-PPO) algorithm that takes as input the local observations of all agents combined with a low resolution global map to learn a policy for each agent. The graph attention allows agents to share their information with others leading to an effective joint policy. Our main focus is to understand how effective MARL is for the VPM problem. We investigate five research questions with this broader goal. We find that MA-G-PPO is able to learn a better policy than the non-RL baseline in most cases, the effectiveness depends on agents sharing information with each other, and the policy learnt shows emergent behavior for the agents.
Coresets for Regressions with Panel Data
Authors: Lingxiao Huang, K. Sudhir, Nisheeth K. Vishnoi
Date: 2020-11-02 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: regression

This paper introduces the problem of coresets for regression problems to panel data settings. We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\varepsilon$ (where $\varepsilon$ is the error parameter) and the number of regression parameters - independent of the number of individuals in the panel data or the time units each individual is observed for. Our approach is based on the Feldman-Langberg framework in which a key step is to upper bound the "total sensitivity" that is roughly the sum of maximum influences of all individual-time pairs taken over all possible choices of regression parameters. Empirically, we assess our approach with synthetic and real-world datasets; the coreset sizes constructed using our approach are much smaller than the full dataset and coresets indeed accelerate the running time of computing the regression objective.
Noise-Contrastive Estimation for Multivariate Point Processes
Authors: Hongyuan Mei, Tom Wan, Jason Eisner
Date: 2020-11-02 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: parameter estimation, Point Processes

The log-likelihood of a generative model often involves both positive and negative terms. For a temporal multivariate point process, the negative term sums over all the possible event types at each time and also integrates over all the possible times. As a result, maximum likelihood estimation is expensive. We show how to instead apply a version of noise-contrastive estimation---a general parameter estimation method with a less expensive stochastic objective. Our specific instantiation of this general idea works out in an interestingly non-trivial way and has provable guarantees for its optimality, consistency and efficiency. On several synthetic and real-world datasets, our method shows benefits: for the model to achieve the same level of log-likelihood on held-out data, our method needs considerably fewer function evaluations and less wall-clock time.
Improvements in Remote Cardiopulmonary Measurement Using a Five Band Digital Camera
Authors: McDuff, D., Gontarek, S., and Picard, R
Date: 2014-05-14 00:00:00
Tasks: Heart Rate Variability, Photoplethysmography (PPG)

Remote measurement of the blood volume pulse via photoplethysmography (PPG) using digital cameras and ambient light has great potential for healthcare and affective computing. However, traditional RGB cameras have limited frequency resolution. We present results of PPG measurements from a novel five band camera and show that alternate frequency bands, in particular an orange band, allowed physiological measurements much more highly correlated with an FDA approved contact PPG sensor.
In a study with participants (n = 10) at rest and under stress, correlations of over 0.92 (p < 0.01) were obtained for heart rate, breathing rate, and heart rate variability measurements. In addition, the remotely measured heart rate variability spectrograms
closely matched those from the contact approach. The best results were obtained using a combination of cyan, green, and orange
(CGO) bands; incorporating red and blue channel observations did not improve performance. In short, RGB is not optimal for this
problem: CGO is better. Incorporating alternative color channel sensors should not increase the cost of such cameras dramatically.
Multi-View Adaptive Fusion Network for 3D Object Detection
Authors: Guojun Wang, Bin Tian, Yachen Zhang, Long Chen, Dongpu Cao, Jian Wu
Date: 2020-11-02 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Feature Importance, Object, object-detection, Object Detection

3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse both modalities without information loss and interference. To solve this issue, we propose a single-stage multi-view fusion framework that takes LiDAR bird's-eye view, LiDAR range view and camera view images as inputs for 3D object detection. To effectively fuse multi-view features, we propose an attentive pointwise fusion (APF) module to estimate the importance of the three sources with attention mechanisms that can achieve adaptive fusion of multi-view features in a pointwise manner. Furthermore, an attentive pointwise weighting (APW) module is designed to help the network learn structure information and point feature importance with two extra tasks, namely, foreground classification and center regression, and the predicted foreground probability is used to reweight the point features. We design an end-to-end learnable network named MVAF-Net to integrate these two components. Our evaluations conducted on the KITTI 3D object detection datasets demonstrate that the proposed APF and APW modules offer significant performance gains. Moreover, the proposed MVAF-Net achieves the best performance among all single-stage fusion methods and outperforms most two-stage fusion methods, achieving the best trade-off between speed and accuracy on the KITTI benchmark.
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control
Authors: Nan Lin, YuXuan Li, Yujun Zhu, Ruolin Wang, Xiayu Zhang, Jianmin Ji, Keke Tang, Xiaoping Chen, Xinming Zhang
Date: 2020-11-02 00:00:00
Tasks: Hierarchical Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Traditionally, reinforcement learning methods predict the next action based on the current state. However, in many situations, directly applying actions to control systems or robots is dangerous and may lead to unexpected behaviors because action is rather low-level. In this paper, we propose a novel hierarchical reinforcement learning framework without explicit action. Our meta policy tries to manipulate the next optimal state and actual action is produced by the inverse dynamics model. To stabilize the training process, we integrate adversarial learning and information bottleneck into our framework. Under our framework, widely available state-only demonstrations can be exploited effectively for imitation learning. Also, prior knowledge and constraints can be applied to meta policy. We test our algorithm in simulation tasks and its combination with imitation learning. The experimental results show the reliability and robustness of our algorithms.
SIMDive: Approximate SIMD Soft Multiplier-Divider for FPGAs with Tunable Accuracy
Authors: Zahra Ebrahimi, Salim Ullah, Akash Kumar
Date: 2020-11-02 00:00:00

The ever-increasing quest for data-level parallelism and variable precision in ubiquitous multimedia and Deep Neural Network (DNN) applications has motivated the use of Single Instruction, Multiple Data (SIMD) architectures. To alleviate energy as their main resource constraint, approximate computing has re-emerged,albeit mainly specialized for their Application-Specific Integrated Circuit (ASIC) implementations. This paper, presents for the first time, an SIMD architecture based on novel multiplier and divider with tunable accuracy, targeted for Field-Programmable Gate Arrays (FPGAs). The proposed hybrid architecture implements Mitchell's algorithms and supports precision variability from 8 to 32 bits. Experimental results obtained from Vivado, multimedia and DNN applications indicate superiority of proposed architecture (both SISD and SIMD) over accurate and state-of-the-art approximate counterparts. In particular, the proposed SISD divider outperforms the accurate Intellectual Property (IP) divider provided by Xilinx with 4x higher speed and 4.6x less energy and tolerating only < 0.8% error. Moreover, the proposed SIMD multiplier-divider supersede accurate SIMD multiplier by achieving up to 26%, 45%, 36%, and 56% improvement in area, throughput, power, and energy, respectively.
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
Authors: Bob Wei, Mengye Ren, Wenyuan Zeng, Ming Liang, Bin Yang, Raquel Urtasun
Date: 2020-11-02 00:00:00
Tasks: Motion Planning

In this paper, we propose an end-to-end self-driving network featuring a sparse attention module that learns to automatically attend to important regions of the input. The attention module specifically targets motion planning, whereas prior literature only applied attention in perception tasks. Learning an attention mask directly targeted for motion planning significantly improves the planner safety by performing more focused computation. Furthermore, visualizing the attention improves interpretability of end-to-end self-driving.
Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design
Authors: M. Giselle Fernández-Godino, Michael J. Grosskopf, Julia B. Nakhleh, Brandon M. Wilson, John Kline, Gowri Srinivasan
Date: 2020-10-28 00:00:00

A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulations codes are used to model ICF implosions, these tools contain necessary numerical approximation but miss physical processes that limit predictive capability. Identification of relationships between controllable design inputs to ICF experiments and measurable outcomes (e.g. yield, shape) from performed experiments can help guide the future design of experiments and development of simulation codes, to potentially improve the accuracy of the computational models used to simulate ICF experiments. We use sparse matrix decomposition methods to identify clusters of a few related design variables. Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule). A variable importance analysis finds that in addition to variables highly correlated with neutron yield such as picket power and laser energy, variables that represent a dramatic change of the ICF design such as number of pulse steps are also very important. The obtained sparse components are then used to train a random forest (RF) surrogate for predicting total yield. The RF performance on the training and testing data compares with the performance of the RF surrogate trained using all design variables considered. This work is intended to inform design changes in future ICF experiments by augmenting the expert intuition and simulations results.
Pose-based Body Language Recognition for Emotion and Psychiatric Symptom Interpretation
Authors: Zhengyuan Yang, Amanda Kay, Yuncheng Li, Wendi Cross, Jiebo Luo
Date: 2020-10-30 00:00:00
Tasks: Action Recognition, Emotion Interpretation, Emotion Recognition

Inspired by the human ability to infer emotions from body language, we propose an automated framework for body language based emotion recognition starting from regular RGB videos. In collaboration with psychologists, we further extend the framework for psychiatric symptom prediction. Because a specific application domain of the proposed framework may only supply a limited amount of data, the framework is designed to work on a small training set and possess a good transferability. The proposed system in the first stage generates sequences of body language predictions based on human poses estimated from input videos. In the second stage, the predicted sequences are fed into a temporal network for emotion interpretation and psychiatric symptom prediction. We first validate the accuracy and transferability of the proposed body language recognition method on several public action recognition datasets. We then evaluate the framework on a proposed URMC dataset, which consists of conversations between a standardized patient and a behavioral health professional, along with expert annotations of body language, emotions, and potential psychiatric symptoms. The proposed framework outperforms other methods on the URMC dataset.
(Un)Masked COVID-19 Trends from Social Media
Authors: Asmit Kumar Singh, Paras Mehan, Divyanshu Sharma, Rohan Pandey, Tavpritesh Sethi, Ponnurangam Kumaraguru
Date: 2020-10-30 00:00:00
Tasks: Segmentation, Semantic Segmentation

Wearing masks is a useful protection method against COVID-19, which has caused widespread economic and social impact worldwide. Across the globe, governments have put mandates for the use of face masks, which have received both positive and negative reaction. Online social media provides an exciting platform to study the use of masks and analyze underlying mask-wearing patterns. In this article, we analyze 2.04 million social media images for six US cities. An increase in masks worn in images is seen as the COVID-19 cases rose, particularly when their respective states imposed strict regulations. We also found a decrease in the posting of group pictures as stay-at-home laws were put into place. Furthermore, mask compliance in the Black Lives Matter protest was analyzed, eliciting that 40% of the people in group photos wore masks, and 45% of them wore the masks with a fit score of greater than 80%. We introduce two new datasets, VAriety MAsks - Classification (VAMA-C) and VAriety MAsks - Segmentation (VAMA-S), for mask detection and mask fit analysis tasks, respectively. For the analysis, we create two frameworks, face mask detector (for classifying masked and unmasked faces) and mask fit analyzer (a semantic segmentation based model to calculate a mask-fit score). The face mask detector achieved a classification accuracy of 98%, and the semantic segmentation model for the mask fit analyzer achieved an Intersection Over Union (IOU) score of 98%. We conclude that such a framework can be used to evaluate the effectiveness of such public health strategies using social media platforms in times of pandemic.