pwc-archive/papers-with-abstracts huggingface dataset:
MetaDelta: A Meta-Learning System for Few-shot Image Classification
Authors: Yudong Chen, Chaoyu Guan, Zhikun Wei, Xin Wang, Wenwu Zhu
Date: 2021-02-22 00:00:00
Tasks: Classification, Decoder, Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Meta-Learning

Meta-learning aims at learning quickly on novel tasks with limited data by transferring generic experience learned from previous tasks. Naturally, few-shot learning has been one of the most popular applications for meta-learning. However, existing meta-learning algorithms rarely consider the time and resource efficiency or the generalization capacity for unknown datasets, which limits their applicability in real-world scenarios. In this paper, we propose MetaDelta, a novel practical meta-learning system for the few-shot image classification. MetaDelta consists of two core components: i) multiple meta-learners supervised by a central controller to ensure efficiency, and ii) a meta-ensemble module in charge of integrated inference and better generalization. In particular, each meta-learner in MetaDelta is composed of a unique pretrained encoder fine-tuned by batch training and parameter-free decoder used for prediction. MetaDelta ranks first in the final phase in the AAAI 2021 MetaDL Challenge\footnote{https://competitions.codalab.org/competitions/26638}, demonstrating the advantages of our proposed system. The codes are publicly available at https://github.com/Frozenmad/MetaDelta.
Communication Efficient Parallel Reinforcement Learning
Authors: Mridul Agarwal, Bhargav Ganguly, Vaneet Aggarwal
Date: 2021-02-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem where $M$ agents interact with $M$ identical and independent environments with $S$ states and $A$ actions using reinforcement learning for $T$ rounds. The agents share their data with a central server to minimize their regret. We aim to find an algorithm that allows the agents to minimize the regret with infrequent communication rounds. We provide \NAM\ which runs at each agent and prove that the total cumulative regret of $M$ agents is upper bounded as $\Tilde{O}(DS\sqrt{MAT})$ for a Markov Decision Process with diameter $D$, number of states $S$, and number of actions $A$. The agents synchronize after their visitations to any state-action pair exceeds a certain threshold. Using this, we obtain a bound of $O\left(MSA\log(MT)\right)$ on the total number of communications rounds. Finally, we evaluate the algorithm against multiple environments and demonstrate that the proposed algorithm performs at par with an always communication version of the UCRL2 algorithm, while with significantly lower communication.
Dissecting the Diffusion Process in Linear Graph Convolutional Networks
Authors: Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin
Date: 2021-02-22 00:00:00
Proceeding: NeurIPS 2021 12

Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years. A typical GCN layer consists of a linear feature propagation step and a nonlinear transformation step. Recent works show that a linear GCN can achieve comparable performance to the original non-linear GCN while being much more computationally efficient. In this paper, we dissect the feature propagation steps of linear GCNs from a perspective of continuous graph diffusion, and analyze why linear GCNs fail to benefit from more propagation steps. Following that, we propose Decoupled Graph Convolution (DGC) that decouples the terminal time and the feature propagation steps, making it more flexible and capable of exploiting a very large number of feature propagation steps. Experiments demonstrate that our proposed DGC improves linear GCNs by a large margin and makes them competitive with many modern variants of non-linear GCNs.
Provable Super-Convergence with a Large Cyclical Learning Rate
Authors: Samet Oymak
Date: 2021-02-22 00:00:00

Conventional wisdom dictates that learning rate should be in the stable regime so that gradient-based algorithms don't blow up. This letter introduces a simple scenario where an unstably large learning rate scheme leads to a super fast convergence, with the convergence rate depending only logarithmically on the condition number of the problem. Our scheme uses a Cyclical Learning Rate (CLR) where we periodically take one large unstable step and several small stable steps to compensate for the instability. These findings also help explain the empirical observations of [Smith and Topin, 2019] where they show that CLR with a large maximum learning rate can dramatically accelerate learning and lead to so-called "super-convergence". We prove that our scheme excels in the problems where Hessian exhibits a bimodal spectrum and the eigenvalues can be grouped into two clusters (small and large). The unstably large step is the key to enabling fast convergence over the small eigen-spectrum.
Dither computing: a hybrid deterministic-stochastic computing framework
Authors: Chai Wah Wu
Date: 2021-02-22 00:00:00

Stochastic computing has a long history as an alternative method of performing arithmetic on a computer. While it can be considered an unbiased estimator of real numbers, it has a variance and MSE on the order of $\Omega(\frac{1}{N})$. On the other hand, deterministic variants of stochastic computing remove the stochastic aspect, but cannot approximate arbitrary real numbers with arbitrary precision and are biased estimators. However, they have an asymptotically superior MSE on the order of $O(\frac{1}{N^2})$. Recent results in deep learning with stochastic rounding suggest that the bias in the rounding can degrade performance. We proposed an alternative framework, called dither computing, that combines aspects of stochastic computing and its deterministic variants and that can perform computing with similar efficiency, is unbiased, and with a variance and MSE also on the optimal order of $\Theta(\frac{1}{N^2})$. We also show that it can be beneficial in stochastic rounding applications as well. We provide implementation details and give experimental results to comparatively show the benefits of the proposed scheme.
Three dimensional unique identifier based automated georeferencing and coregistration of point clouds in underground environment
Authors: Sarvesh Kumar Singh, Bikram Pratap Banerjee, Simit Raval
Date: 2021-02-22 00:00:00

Spatially and geometrically accurate laser scans are essential in modelling infrastructure for applications in civil, mining and transportation. Monitoring of underground or indoor environments such as mines or tunnels is challenging due to unavailability of a sensor positioning framework, complicated structurally symmetric layouts, repetitive features and occlusions. Current practices largely include a manual selection of discernable reference points for georeferencing and coregistration purpose. This study aims at overcoming these practical challenges in underground or indoor laser scanning. The developed approach involves automatically and uniquely identifiable three dimensional unique identifiers (3DUIDs) in laser scans, and a 3D registration (3DReG) workflow. Field testing of the method in an underground tunnel has been found accurate, effective and efficient. Additionally, a method for automatically extracting roadway tunnel profile has been exhibited. The developed 3DUID can be used in roadway profile extraction, guided automation, sensor calibration, reference targets for routine survey and deformation monitoring.
Abstraction and Analogy-Making in Artificial Intelligence
Authors: Melanie Mitchell
Date: 2021-02-22 00:00:00
Tasks: Program induction

Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep learning, and probabilistic program induction. The paper concludes with several proposals for designing challenge tasks and evaluation measures in order to make quantifiable and generalizable progress in this area.
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player
Authors: Hanlin Niu, Ze Ji, Farshad Arvin, Barry Lennox, Hujun Yin, Joaquin Carrasco
Date: 2021-02-21 00:00:00
Tasks: Collision Avoidance, Deep Reinforcement Learning, Navigate, Reinforcement Learning (RL)

This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile robots that map raw sensor data to linear and angular velocities and navigate in an unknown environment without a map. An efficient training strategy is proposed to allow a robot to learn from both human experience data and self-exploratory data. A game format simulation framework is designed to allow the human player to tele-operate the mobile robot to a goal and human action is also scored using the reward function. Both human player data and self-playing data are sampled using prioritized experience replay algorithm. The proposed algorithm and training strategy have been evaluated in two different experimental configurations: \textit{Environment 1}, a simulated cluttered environment, and \textit{Environment 2}, a simulated corridor environment, to investigate the performance. It was demonstrated that the proposed method achieved the same level of reward using only 16\% of the training steps required by the standard Deep Deterministic Policy Gradient (DDPG) method in Environment 1 and 20\% of that in Environment 2. In the evaluation of 20 random missions, the proposed method achieved no collision in less than 2~h and 2.5~h of training time in the two Gazebo environments respectively. The method also generated smoother trajectories than DDPG. The proposed method has also been implemented on a real robot in the real-world environment for performance evaluation. We can confirm that the trained model with the simulation software can be directly applied into the real-world scenario without further fine-tuning, further demonstrating its higher robustness than DDPG. The video and code are available: https://youtu.be/BmwxevgsdGc https://github.com/hanlinniu/turtlebot3_ddpg_collision_avoidance
3D Vision-guided Pick-and-Place Using Kuka LBR iiwa Robot
Authors: Hanlin Niu, Ze Ji, Zihang Zhu, Hujun Yin, Joaquin Carrasco
Date: 2021-02-21 00:00:00
Tasks: Object

This paper presents the development of a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera. The main steps include camera intrinsic and extrinsic calibration, hand-eye calibration, initial object pose registration, objects pose alignment algorithm, and pick-and-place execution. The proposed system allows the robot be able to to pick and place object with limited times of registering a new object and the developed software can be applied for new object scenario quickly. The integrated system was tested using the hardware combination of kuka iiwa, Robotiq grippers (two finger gripper and three finger gripper) and 3D cameras (Intel realsense D415 camera, Intel realsense D435 camera, Microsoft Kinect V2). The whole system can also be modified for the combination of other robotic arm, gripper and 3D camera.
A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization
Authors: HanQin Cai, Yuchen Lou, Daniel Mckenzie, Wotao Yin
Date: 2021-02-21 00:00:00

We consider the zeroth-order optimization problem in the huge-scale setting, where the dimension of the problem is so large that performing even basic vector operations on the decision variables is infeasible. In this paper, we propose a novel algorithm, coined ZO-BCD, that exhibits favorable overall query complexity and has a much smaller per-iteration computational complexity. In addition, we discuss how the memory footprint of ZO-BCD can be reduced even further by the clever use of circulant measurement matrices. As an application of our new method, we propose the idea of crafting adversarial attacks on neural network based classifiers in a wavelet domain, which can result in problem dimensions of over 1.7 million. In particular, we show that crafting adversarial examples to audio classifiers in a wavelet domain can achieve the state-of-the-art attack success rate of 97.9%.
Pruning the Index Contents for Memory Efficient Open-Domain QA
Authors: Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz
Date: 2021-02-21 00:00:00
Tasks: Open-Domain Question Answering

This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches. Specifically, it proposes the novel R2-D2 (Rank twice, reaD twice) pipeline composed of retriever, passage reranker, extractive reader, generative reader and a simple way to combine them. Furthermore, previous work often comes with a massive index of external documents that scales in the order of tens of GiB. This work presents a simple approach for pruning the contents of a massive index such that the open-domain QA system altogether with index, OS, and library components fits into 6GiB docker image while retaining only 8% of original index contents and losing only 3% EM accuracy.
Synthesizing Irreproducibility in Deep Networks
Authors: Robert R. Snapp, Gil I. Shamir
Date: 2021-02-21 00:00:00

The success and superior performance of deep networks is spreading their popularity and use to an increasing number of applications. Very recent works, however, demonstrate that modern day deep networks suffer from irreproducibility (also referred to as nondeterminism or underspecification). Two or more models that are identical in architecture, structure, training hyper-parameters, and parameters, and that are trained on exactly the same training data, yield different predictions on individual previously unseen examples. Thus, a model that performs well on controlled test data, may perform in unexpected ways when deployed in the real world, whose data is expected to be similar to the test data. We study simple synthetic models and data to understand the origins of these problems. We show that even with a single nonlinearity and for very simple data and models, irreproducibility occurs. Our study demonstrates the effects of randomness in initialization, training data shuffling window size, and activation functions on prediction irreproducibility, even under very controlled synthetic data. While, as one would expect, randomness in initialization and in shuffling the training examples exacerbates the phenomenon, we show that model complexity and the choice of nonlinearity also play significant roles in making deep models irreproducible.
Pre-Training BERT on Arabic Tweets: Practical Considerations
Authors: Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish, Younes Samih
Date: 2021-02-21 00:00:00
Tasks: Diversity

Pretraining Bidirectional Encoder Representations from Transformers (BERT) for downstream NLP tasks is a non-trival task. We pretrained 5 BERT models that differ in the size of their training sets, mixture of formal and informal Arabic, and linguistic preprocessing. All are intended to support Arabic dialects and social media. The experiments highlight the centrality of data diversity and the efficacy of linguistically aware segmentation. They also highlight that more data or more training step do not necessitate better models. Our new models achieve new state-of-the-art results on several downstream tasks. The resulting models are released to the community under the name QARiB.
Probabilistic Vehicle Reconstruction Using a Multi-Task CNN
Authors: Max Coenen, Franz Rottensteiner
Date: 2021-02-21 00:00:00
Tasks: Object Reconstruction, Retrieval, Sensitivity

The retrieval of the 3D pose and shape of objects from images is an ill-posed problem. A common way to object reconstruction is to match entities such as keypoints, edges, or contours of a deformable 3D model, used as shape prior, to their corresponding entities inferred from the image. However, such approaches are highly sensitive to model initialisation, imprecise keypoint localisations and/or illumination conditions. In this paper, we present a probabilistic approach for shape-aware 3D vehicle reconstruction from stereo images that leverages the outputs of a novel multi-task CNN. Specifically, we train a CNN that outputs probability distributions for the vehicle's orientation and for both, vehicle keypoints and wireframe edges. Together with 3D stereo information we integrate the predicted distributions into a common probabilistic framework. We believe that the CNN-based detection of wireframe edges reduces the sensitivity to illumination conditions and object contrast and that using the raw probability maps instead of inferring keypoint positions reduces the sensitivity to keypoint localisation errors. We show that our method achieves state-of-the-art results, evaluating our method on the challenging KITTI benchmark and on our own new 'Stereo-Vehicle' dataset.
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
Authors: Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael B. Gotway, Jianming Liang
Date: 2021-02-21 00:00:00
Tasks: Anatomy, Medical Image Analysis, Medical Image Segmentation, Self-Supervised Learning, Transfer Learning

This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imaging--focusing on particular parts of the body for defined clinical purposes--generates images of great similarity in anatomy across patients and yields sophisticated anatomical patterns across images, which are associated with rich semantics about human anatomy and which are natural visual words. We show that these visual words can be automatically harvested according to anatomical consistency via self-discovery, and that the self-discovered visual words can serve as strong yet free supervision signals for deep models to learn semantics-enriched generic image representation via self-supervision (self-classification and self-restoration). Our extensive experiments demonstrate the annotation efficiency of TransVW by offering higher performance and faster convergence with reduced annotation cost in several applications. Our TransVW has several important advantages, including (1) TransVW is a fully autodidactic scheme, which exploits the semantics of visual words for self-supervised learning, requiring no expert annotation; (2) visual word learning is an add-on strategy, which complements existing self-supervised methods, boosting their performance; and (3) the learned image representation is semantics-enriched models, which have proven to be more robust and generalizable, saving annotation efforts for a variety of applications through transfer learning. Our code, pre-trained models, and curated visual words are available at https://github.com/JLiangLab/TransVW.
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation
Authors: Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y. Ng, Pranav Rajpurkar
Date: 2021-02-21 00:00:00
Tasks: Contrastive Learning

Self-supervised contrastive learning between pairs of multiple views of the same image has been shown to successfully leverage unlabeled data to produce meaningful visual representations for both natural and medical images. However, there has been limited work on determining how to select pairs for medical images, where availability of patient metadata can be leveraged to improve representations. In this work, we develop a method to select positive pairs coming from views of possibly different images through the use of patient metadata. We compare strategies for selecting positive pairs for chest X-ray interpretation including requiring them to be from the same patient, imaging study or laterality. We evaluate downstream task performance by fine-tuning the linear layer on 1% of the labeled dataset for pleural effusion classification. Our best performing positive pair selection strategy, which involves using images from the same patient from the same study across all lateralities, achieves a performance increase of 14.4% in mean AUC from the ImageNet pretrained baseline. Our controlled experiments show that the keys to improving downstream performance on disease classification are (1) using patient metadata to appropriately create positive pairs from different images with the same underlying pathologies, and (2) maximizing the number of different images used in query pairing. In addition, we explore leveraging patient metadata to select hard negative pairs for contrastive learning, but do not find improvement over baselines that do not use metadata. Our method is broadly applicable to medical image interpretation and allows flexibility for incorporating medical insights in choosing pairs for contrastive learning.
Safe Reinforcement Learning Using Robust Action Governor
Authors: Yutong Li, Nan Li, H. Eric Tseng, Anouck Girard, Dimitar Filev, Ilya Kolmanovsky
Date: 2021-02-21 00:00:00
Tasks: RAG, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Reinforcement Learning (RL) is essentially a trial-and-error learning procedure which may cause unsafe behavior during the exploration-and-exploitation process. This hinders the application of RL to real-world control problems, especially to those for safety-critical systems. In this paper, we introduce a framework for safe RL that is based on integration of a RL algorithm with an add-on safety supervision module, called the Robust Action Governor (RAG), which exploits set-theoretic techniques and online optimization to manage safety-related requirements during learning. We illustrate this proposed safe RL framework through an application to automotive adaptive cruise control.
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution
Authors: Ahlad Kumar, Harsh Vardhan Singh
Date: 2021-02-21 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution, Transfer Learning

The recent outbreak of COVID-19 has motivated researchers to contribute in the area of medical imaging using artificial intelligence and deep learning. Super-resolution (SR), in the past few years, has produced remarkable results using deep learning methods. The ability of deep learning methods to learn the non-linear mapping from low-resolution (LR) images to their corresponding high-resolution (HR) images leads to compelling results for SR in diverse areas of research. In this paper, we propose a deep learning based image super-resolution architecture in Tchebichef transform domain. This is achieved by integrating a transform layer into the proposed architecture through a customized Tchebichef convolutional layer ($TCL$). The role of TCL is to convert the LR image from the spatial domain to the orthogonal transform domain using Tchebichef basis functions. The inversion of the aforementioned transformation is achieved using another layer known as the Inverse Tchebichef convolutional Layer (ITCL), which converts back the LR images from the transform domain to the spatial domain. It has been observed that using the Tchebichef transform domain for the task of SR takes the advantage of high and low-frequency representation of images that makes the task of super-resolution simplified. We, further, introduce transfer learning approach to enhance the quality of Covid based medical images. It is shown that our architecture enhances the quality of X-ray and CT images of COVID-19, providing a better image quality that helps in clinical diagnosis. Experimental results obtained using the proposed Tchebichef transform domain super-resolution (TTDSR) architecture provides competitive results when compared with most of the deep learning methods employed using a fewer number of trainable parameters.