pwc-archive/papers-with-abstracts huggingface dataset:
Schrödinger PCA: On the Duality between Principal Component Analysis and Schrödinger Equation
Authors: Ziming Liu, Sitian Qian, Yi-Xuan Wang, Yuxuan Yan, Tianyi Yang
Date: 2020-06-08 00:00:00

Principal component analysis (PCA) has achieved great success in unsupervised learning by identifying covariance correlations among features. If the data collection fails to capture the covariance information, PCA will not be able to discover meaningful modes. In particular, PCA will fail the spatial Gaussian Process (GP) model in the undersampling regime, i.e. the averaged distance of neighboring anchor points (spatial features) is greater than the correlation length of GP. Counterintuitively, by drawing the connection between PCA and Schr\"odinger equation, we can not only attack the undersampling challenge but also compute in an efficient and decoupled way with the proposed algorithm called Schr\"odinger PCA. Our algorithm only requires variances of features and estimated correlation length as input, constructs the corresponding Schr\"odinger equation, and solves it to obtain the energy eigenstates, which coincide with principal components. We will also establish the connection of our algorithm to the model reduction techniques in the partial differential equation (PDE) community, where the steady-state Schr\"odinger operator is identified as a second-order approximation to the covariance function. Numerical experiments are implemented to testify the validity and efficiency of the proposed algorithm, showing its potential for unsupervised learning tasks on general graphs and manifolds.
Speaker Diarization as a Fully Online Learning Problem in MiniVox
Authors: Baihan Lin, Xinxin Zhang
Date: 2020-06-08 00:00:00
Tasks: Self-Supervised Learning, speaker-diarization, Speaker Diarization

We proposed a novel machine learning framework to conduct real-time multi-speaker diarization and recognition without prior registration and pretraining in a fully online learning setting. Our contributions are two-fold. First, we proposed a new benchmark to evaluate the rarely studied fully online speaker diarization problem. We built upon existing datasets of real world utterances to automatically curate MiniVox, an experimental environment which generates infinite configurations of continuous multi-speaker speech stream. Second, we considered the practical problem of online learning with episodically revealed rewards and introduced a solution based on semi-supervised and self-supervised learning methods. Additionally, we provided a workable web-based recognition system which interactively handles the cold start problem of new user's addition by transferring representations of old arms to new ones with an extendable contextual bandit. We demonstrated that our proposed method obtained robust performance in the online MiniVox framework.
MC2G: An Efficient Algorithm for Matrix Completion with Social and Item Similarity Graphs
Authors: Qiaosheng Zhang, Geewon Suh, Changho Suh, Vincent Y. F. Tan
Date: 2020-06-08 00:00:00
Tasks: Clustering, Matrix Completion, Recommendation Systems

In this paper, we design and analyze MC2G (Matrix Completion with 2 Graphs), an algorithm that performs matrix completion in the presence of social and item similarity graphs. MC2G runs in quasilinear time and is parameter free. It is based on spectral clustering and local refinement steps. The expected number of sampled entries required for MC2G to succeed (i.e., recover the clusters in the graphs and complete the matrix) matches an information-theoretic lower bound up to a constant factor for a wide range of parameters. We show via extensive experiments on both synthetic and real datasets that MC2G outperforms other state-of-the-art matrix completion algorithms that leverage graph side information.
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation
Authors: Xiaobin Wei, Jianjiang Feng, Jie zhou
Date: 2020-06-08 00:00:00
Tasks: Depth Estimation, Depth Prediction, Motion Estimation, Position, Segmentation, Semantic Segmentation

We propose a semantics-driven unsupervised learning approach for monocular depth and ego-motion estimation from videos in this paper. Recent unsupervised learning methods employ photometric errors between synthetic view and actual image as a supervision signal for training. In our method, we exploit semantic segmentation information to mitigate the effects of dynamic objects and occlusions in the scene, and to improve depth prediction performance by considering the correlation between depth and semantics. To avoid costly labeling process, we use noisy semantic segmentation results obtained by a pre-trained semantic segmentation network. In addition, we minimize the position error between the corresponding points of adjacent frames to utilize 3D spatial information. Experimental results on the KITTI dataset show that our method achieves good performance in both depth and ego-motion estimation tasks.
Photoacoustic Microscopy with Sparse Data Enabled by Convolutional Neural Networks for Fast Imaging
Authors: Jiasheng Zhou, Da He, Xiaoyu Shang, Zhendong Guo, Sung-Liang Chen, Jiajia Luo
Date: 2020-06-08 00:00:00

Photoacoustic microscopy (PAM) has been a promising biomedical imaging technology in recent years. However, the point-by-point scanning mechanism results in low-speed imaging, which limits the application of PAM. Reducing sampling density can naturally shorten image acquisition time, which is at the cost of image quality. In this work, we propose a method using convolutional neural networks (CNNs) to improve the quality of sparse PAM images, thereby speeding up image acquisition while keeping good image quality. The CNN model utilizes both squeeze-and-excitation blocks and residual blocks to achieve the enhancement, which is a mapping from a 1/4 or 1/16 low-sampling sparse PAM image to a latent fully-sampled image. The perceptual loss function is applied to keep the fidelity of images. The model is mainly trained and validated on PAM images of leaf veins. The experiments show the effectiveness of our proposed method, which significantly outperforms existing methods quantitatively and qualitatively. Our model is also tested using in vivo PAM images of blood vessels of mouse ears and eyes. The results show that the model can enhance the image quality of the sparse PAM image of blood vessels from several aspects, which may help fast PAM and facilitate its clinical applications.
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective
Authors: Prasad Cheema, Mahito Sugiyama
Date: 2020-06-08 00:00:00
Tasks: Model Selection, regression

The appearance of the double-descent risk phenomenon has received growing interest in the machine learning and statistics community, as it challenges well-understood notions behind the U-shaped train-test curves. Motivated through Rissanen's minimum description length (MDL), Balasubramanian's Occam's Razor, and Amari's information geometry, we investigate how the logarithm of the model volume: $\log V$, works to extend intuition behind the AIC and BIC model selection criteria. We find that for the particular model classes of isotropic linear regression and statistical lattices, the $\log V$ term may be decomposed into a sum of distinct components, each of which assist in their explanations of the appearance of this phenomenon. In particular they suggest why generalization error does not necessarily continue to grow with increasing model dimensionality.
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models
Authors: Taher Jafferjee, Ehsan Imani, Erin Talvitie, Martha White, Micheal Bowling
Date: 2020-06-08 00:00:00
Tasks: Reinforcement Learning (RL)

Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environment model. However, it is often difficult to learn accurate models of environment dynamics, and even small errors may result in failure of Dyna agents. In this paper, we investigate one type of model error: hallucinated states. These are states generated by the model, but that are not real states of the environment. We present the Hallucinated Value Hypothesis (HVH): updating values of real states towards values of hallucinated states results in misleading state-action values which adversely affect the control policy. We discuss and evaluate four Dyna variants; three which update real states toward simulated -- and therefore potentially hallucinated -- states and one which does not. The experimental results provide evidence for the HVH thus suggesting a fruitful direction toward developing Dyna algorithms robust to model error.
Neural Sparse Representation for Image Restoration
Authors: Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang
Date: 2020-06-08 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Denoising, Image Compression, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks. Our method structurally enforces sparsity constraints upon hidden neurons. The sparsity constraints are favorable for gradient-based learning algorithms and attachable to convolution layers in various networks. Sparsity in neurons enables computation saving by only operating on non-zero components without hurting accuracy. Meanwhile, our method can magnify representation dimensionality and model capacity with negligible additional computation cost. Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks, including image super-resolution, image denoising, and image compression artifacts removal. Code is available at https://github.com/ychfan/nsr
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection
Authors: Liang Du, Xiaoqing Ye, Xiao Tan, Jianfeng Feng, Zhenbo Xu, Errui Ding, Shilei Wen
Date: 2020-06-08 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Object Detection, Domain Adaptation, Object, object-detection, Object Detection

Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques. Owing to the severe spatial occlusion and inherent variance of point density with the distance to sensors, appearance of a same object varies a lot in point cloud data. Designing robust feature representation against such appearance changes is hence the key issue in a 3D object detection method. In this paper, we innovatively propose a domain adaptation like approach to enhance the robustness of the feature representation. More specifically, we bridge the gap between the perceptual domain where the feature comes from a real scene and the conceptual domain where the feature is extracted from an augmented scene consisting of non-occlusion point cloud rich of detailed information. This domain adaptation approach mimics the functionality of the human brain when proceeding object perception. Extensive experiments demonstrate that our simple yet effective approach fundamentally boosts the performance of 3D point cloud object detection and achieves the state-of-the-art results.
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret
Authors: Mehdi Jafarnia-Jahromi, Chen-Yu Wei, Rahul Jain, Haipeng Luo
Date: 2020-06-08 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Recently, model-free reinforcement learning has attracted research attention due to its simplicity, memory and computation efficiency, and the flexibility to combine with function approximation. In this paper, we propose Exploration Enhanced Q-learning (EE-QL), a model-free algorithm for infinite-horizon average-reward Markov Decision Processes (MDPs) that achieves regret bound of $O(\sqrt{T})$ for the general class of weakly communicating MDPs, where $T$ is the number of interactions. EE-QL assumes that an online concentrating approximation of the optimal average reward is available. This is the first model-free learning algorithm that achieves $O(\sqrt T)$ regret without the ergodic assumption, and matches the lower bound in terms of $T$ except for logarithmic factors. Experiments show that the proposed algorithm performs as well as the best known model-based algorithms.
Stable Reinforcement Learning with Unbounded State Space
Authors: Devavrat Shah, Qiaomin Xie, Zhi Xu
Date: 2020-06-08 00:00:00
Proceeding: L4DC 2020 6
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

We consider the problem of reinforcement learning (RL) with unbounded state space motivated by the classical problem of scheduling in a queueing network. Traditional policies as well as error metric that are designed for finite, bounded or compact state space, require infinite samples for providing any meaningful performance guarantee (e.g. $\ell_\infty$ error) for unbounded state space. That is, we need a new notion of performance metric. As the main contribution of this work, inspired by the literature in queuing systems and control theory, we propose stability as the notion of "goodness": the state dynamics under the policy should remain in a bounded region with high probability. As a proof of concept, we propose an RL policy using Sparse-Sampling-based Monte Carlo Oracle and argue that it satisfies the stability property as long as the system dynamics under the optimal policy respects a Lyapunov function. The assumption of existence of a Lyapunov function is not restrictive as it is equivalent to the positive recurrence or stability property of any Markov chain, i.e., if there is any policy that can stabilize the system then it must possess a Lyapunov function. And, our policy does not utilize the knowledge of the specific Lyapunov function. To make our method sample efficient, we provide an improved, sample efficient Sparse-Sampling-based Monte Carlo Oracle with Lipschitz value function that may be of interest in its own right. Furthermore, we design an adaptive version of the algorithm, based on carefully constructed statistical tests, which finds the correct tuning parameter automatically.
Distributional Robustness with IPMs and links to Regularization and GANs
Authors: Hisham Husain
Date: 2020-06-08 00:00:00
Proceeding: NeurIPS 2020 12

Robustness to adversarial attacks is an important concern due to the fragility of deep neural networks to small perturbations and has received an abundance of attention in recent years. Distributionally Robust Optimization (DRO), a particularly promising way of addressing this challenge, studies robustness via divergence-based uncertainty sets and has provided valuable insights into robustification strategies such as regularization. In the context of machine learning, the majority of existing results have chosen $f$-divergences, Wasserstein distances and more recently, the Maximum Mean Discrepancy (MMD) to construct uncertainty sets. We extend this line of work for the purposes of understanding robustness via regularization by studying uncertainty sets constructed with Integral Probability Metrics (IPMs) - a large family of divergences including the MMD, Total Variation and Wasserstein distances. Our main result shows that DRO under \textit{any} choice of IPM corresponds to a family of regularization penalties, which recover and improve upon existing results in the setting of MMD and Wasserstein distances. Due to the generality of our result, we show that other choices of IPMs correspond to other commonly used penalties in machine learning. Furthermore, we extend our results to shed light on adversarial generative modelling via $f$-GANs, constituting the first study of distributional robustness for the $f$-GAN objective. Our results unveil the inductive properties of the discriminator set with regards to robustness, allowing us to give positive comments for several penalty-based GAN methods such as Wasserstein-, MMD- and Sobolev-GANs. In summary, our results intimately link GANs to distributional robustness, extend previous results on DRO and contribute to our understanding of the link between regularization and robustness at large.
Confidence sequences for sampling without replacement
Authors: Ian Waudby-Smith, Aaditya Ramdas
Date: 2020-06-08 00:00:00
Proceeding: NeurIPS 2020 12

Many practical tasks involve sampling sequentially without replacement (WoR) from a finite population of size $N$, in an attempt to estimate some parameter $\theta^\star$. Accurately quantifying uncertainty throughout this process is a nontrivial task, but is necessary because it often determines when we stop collecting samples and confidently report a result. We present a suite of tools for designing confidence sequences (CS) for $\theta^\star$. A CS is a sequence of confidence sets $(C_n)_{n=1}^N$, that shrink in size, and all contain $\theta^\star$ simultaneously with high probability. We present a generic approach to constructing a frequentist CS using Bayesian tools, based on the fact that the ratio of a prior to the posterior at the ground truth is a martingale. We then present Hoeffding- and empirical-Bernstein-type time-uniform CSs and fixed-time confidence intervals for sampling WoR, which improve on previous bounds in the literature and explicitly quantify the benefit of WoR sampling.
Fast Synthetic LiDAR Rendering via Spherical UV Unwrapping of Equirectangular Z-Buffer Images
Authors: Mohammed Hossny, Khaled Saleh, Mohammed Attia, Ahmed Abobakr, Julie Iskander
Date: 2020-06-08 00:00:00
Tasks: Autonomous Vehicles

LiDAR data is becoming increasingly essential with the rise of autonomous vehicles. Its ability to provide 360deg horizontal field of view of point cloud, equips self-driving vehicles with enhanced situational awareness capabilities. While synthetic LiDAR data generation pipelines provide a good solution to advance the machine learning research on LiDAR, they do suffer from a major shortcoming, which is rendering time. Physically accurate LiDAR simulators (e.g. Blensor) are computationally expensive with an average rendering time of 14-60 seconds per frame for urban scenes. This is often compensated for via using 3D models with simplified polygon topology (low poly assets) as is the case of CARLA (Dosovitskiy et al., 2017). However, this comes at the price of having coarse grained unrealistic LiDAR point clouds. In this paper, we present a novel method to simulate LiDAR point cloud with faster rendering time of 1 sec per frame. The proposed method relies on spherical UV unwrapping of Equirectangular Z-Buffer images. We chose Blensor (Gschwandtner et al., 2011) as the baseline method to compare the point clouds generated using the proposed method. The reported error for complex urban landscapes is 4.28cm for a scanning range between 2-120 meters with Velodyne HDL64-E2 parameters. The proposed method reported a total time per frame to 3.2 +/- 0.31 seconds per frame. In contrast, the BlenSor baseline method reported 16.2 +/- 1.82 seconds.
Deep Neural Network Based Real-time Kiwi Fruit Flower Detection in an Orchard Environment
Authors: JongYoon Lim, Ho Seok Ahn, Mahla Nejati, Jamie Bell, Henry Williams, Bruce A. MacDonald
Date: 2020-06-08 00:00:00
Tasks: object-detection, Object Detection

In this paper, we present a novel approach to kiwi fruit flower detection using Deep Neural Networks (DNNs) to build an accurate, fast, and robust autonomous pollination robot system. Recent work in deep neural networks has shown outstanding performance on object detection tasks in many areas. Inspired this, we aim for exploiting DNNs for kiwi fruit flower detection and present intensive experiments and their analysis on two state-of-the-art object detectors; Faster R-CNN and Single Shot Detector (SSD) Net, and feature extractors; Inception Net V2 and NAS Net with real-world orchard datasets. We also compare those approaches to find an optimal model which is suitable for a real-time agricultural pollination robot system in terms of accuracy and processing speed. We perform experiments with dataset collected from different seasons and locations (spatio-temporal consistency) in order to demonstrate the performance of the generalized model. The proposed system demonstrates promising results of 0.919, 0.874, and 0.889 for precision, recall, and F1-score respectively on our real-world dataset, and the performance satisfies the requirement for deploying the system onto an autonomous pollination robotics system.
The Strength of Nesterov's Extrapolation in the Individual Convergence of Nonsmooth Optimization
Authors: W. Tao, Z. Pan, G. Wu, Q. Tao
Date: 2020-06-08 00:00:00
Tasks: BIG-bench Machine Learning, Open-Ended Question Answering

The extrapolation strategy raised by Nesterov, which can accelerate the convergence rate of gradient descent methods by orders of magnitude when dealing with smooth convex objective, has led to tremendous success in training machine learning tasks. In this article, the convergence of individual iterates of projected subgradient (PSG) methods for nonsmooth convex optimization problems is theoretically studied based on Nesterov's extrapolation, which we name individual convergence. We prove that Nesterov's extrapolation has the strength to make the individual convergence of PSG optimal for nonsmooth problems. In light of this consideration, a direct modification of the subgradient evaluation suffices to achieve optimal individual convergence for strongly convex problems, which can be regarded as making an interesting step toward the open question about stochastic gradient descent (SGD) posed by Shamir. Furthermore, we give an extension of the derived algorithms to solve regularized learning tasks with nonsmooth losses in stochastic settings. Compared with other state-of-the-art nonsmooth methods, the derived algorithms can serve as an alternative to the basic SGD especially in coping with machine learning problems, where an individual output is needed to guarantee the regularization structure while keeping an optimal rate of convergence. Typically, our method is applicable as an efficient tool for solving large-scale $l$1-regularized hinge-loss learning problems. Several comparison experiments demonstrate that our individual output not only achieves an optimal convergence rate but also guarantees better sparsity than the averaged solution.
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning
Authors: Sihan Zeng, Aqeel Anwar, Thinh Doan, Arijit Raychowdhury, Justin Romberg
Date: 2020-06-08 00:00:00
Tasks: Atari Games, Multi-Task Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We develop a mathematical framework for solving multi-task reinforcement learning (MTRL) problems based on a type of policy gradient method. The goal in MTRL is to learn a common policy that operates effectively in different environments; these environments have similar (or overlapping) state spaces, but have different rewards and dynamics. We highlight two fundamental challenges in MTRL that are not present in its single task counterpart, and illustrate them with simple examples. We then develop a decentralized entropy-regularized policy gradient method for solving the MTRL problem, and study its finite-time convergence rate. We demonstrate the effectiveness of the proposed method using a series of numerical experiments. These experiments range from small-scale "GridWorld" problems that readily demonstrate the trade-offs involved in multi-task learning to large-scale problems, where common policies are learned to navigate an airborne drone in multiple (simulated) environments.