pwc-archive/papers-with-abstracts huggingface dataset:
VideoClick: Video Object Segmentation with a Single Click
Authors: Namdar Homayounfar, Justin Liang, Wei-Chiu Ma, Raquel Urtasun
Date: 2021-01-16 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Annotating videos with object segmentation masks typically involves a two stage procedure of drawing polygons per object instance for all the frames and then linking them through time. While simple, this is a very tedious, time consuming and expensive process, making the creation of accurate annotations at scale only possible for well-funded labs. What if we were able to segment an object in the full video with only a single click? This will enable video segmentation at scale with a very low budget opening the door to many applications. Towards this goal, in this paper we propose a bottom up approach where given a single click for each object in a video, we obtain the segmentation masks of these objects in the full video. In particular, we construct a correlation volume that assigns each pixel in a target frame to either one of the objects in the reference frame or the background. We then refine this correlation volume via a recurrent attention module and decode the final segmentation. To evaluate the performance, we label the popular and challenging Cityscapes dataset with video object segmentations. Results on this new CityscapesVideo dataset show that our approach outperforms all the baselines in this challenging setting.
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Authors: Yun Chen, Frieda Rong, Shivam Duggal, Shenlong Wang, Xinchen Yan, Sivabalan Manivasagam, Shangjie Xue, Ersin Yumer, Raquel Urtasun
Date: 2021-01-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: Data Augmentation, Synthetic Data Generation

Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photorealistic or do not model the 3D environment and the dynamic objects within, losing high-level control and physical realism. In this paper, we present GeoSim, a geometry-aware image composition process which synthesizes novel urban driving scenarios by augmenting existing images with dynamic objects extracted from other scenes and rendered at novel poses. Towards this goal, we first build a diverse bank of 3D objects with both realistic geometry and appearance from sensor data. During simulation, we perform a novel geometry-aware simulation-by-composition procedure which 1) proposes plausible and realistic object placements into a given scene, 2) render novel views of dynamic objects from the asset bank, and 3) composes and blends the rendered image segments. The resulting synthetic images are realistic, traffic-aware, and geometrically consistent, allowing our approach to scale to complex use cases. We demonstrate two such important applications: long-range realistic video simulation across multiple camera sensors, and synthetic data generation for data augmentation on downstream segmentation tasks. Please check https://tmux.top/publication/geosim/ for high-resolution video results.
ConE: A Concurrent Edit Detection Tool for Large Scale Software Development
Authors: Chandra Maddila, Nachiappan Nagappan, Christian Bird, Georgios Gousios, Arie van Deursen
Date: 2021-01-16 00:00:00

Modern, complex software systems are being continuously extended and adjusted. The developers responsible for this may come from different teams or organizations, and may be distributed over the world. This may make it difficult to keep track of what other developers are doing, which may result in multiple developers concurrently editing the same code areas. This, in turn, may lead to hard-to-merge changes or even merge conflicts, logical bugs that are difficult to detect, duplication of work, and wasted developer productivity. To address this, we explore the extent of this problem in the pull request based software development model. We study half a year of changes made to six large repositories in Microsoft in which at least 1,000 pull requests are created each month. We find that files concurrently edited in different pull requests are more likely to introduce bugs. Motivated by these findings, we design, implement, and deploy a service named ConE (Concurrent Edit Detector) that proactively detects pull requests containing concurrent edits, to help mitigate the problems caused by them. ConE has been designed to scale, and to minimize false alarms while still flagging relevant concurrently edited files. Key concepts of ConE include the detection of the Extent of Overlap between pull requests, and the identification of Rarely Concurrently Edited Files. To evaluate ConE, we report on its operational deployment on 234 repositories inside Microsoft. ConE assessed 26,000 pull requests and made 775 recommendations about conflicting changes, which were rated as useful in over 70% (554) of the cases. From interviews with 48 users we learned that they believed ConE would save time in conflict resolution and avoiding duplicate work, and that over 90% intend to keep using the service on a daily basis.
SceneGen: Learning to Generate Realistic Traffic Scenes
Authors: Shuhan Tan, Kelvin Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun
Date: 2021-01-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: Diversity

We consider the problem of generating realistic traffic scenes automatically. Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones. As a result, existing simulators lack the fidelity necessary to train and test self-driving vehicles. To address this limitation, we present SceneGen, a neural autoregressive model of traffic scenes that eschews the need for rules and heuristics. In particular, given the ego-vehicle state and a high definition map of surrounding area, SceneGen inserts actors of various classes into the scene and synthesizes their sizes, orientations, and velocities. We demonstrate on two large-scale datasets SceneGen's ability to faithfully model distributions of real traffic scenes. Moreover, we show that SceneGen coupled with sensor simulation can be used to train perception models that generalize to the real world.
Deep Cox Mixtures for Survival Regression
Authors: Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh, Katherine Heller
Date: 2021-01-16 00:00:00
Tasks: regression, Survival Analysis

Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics.
Big Data application in congestion detection and classification using Apache spark
Authors: Atousa Zarindast, Anuj Sharma
Date: 2021-01-16 00:00:00
Tasks: General Classification, Management

With the era of big data, an explosive amount of information is now available. This enormous increase of Big Data in both academia and industry requires large-scale data processing systems. A large body of research is behind optimizing Spark's performance to make it state of the art, a fast and general data processing system. Many science and engineering fields have advanced with Big Data analytics, such as Biology, finance, and transportation. Intelligent transportation systems (ITS) gain popularity and direct benefit from the richness of information. The objective is to improve the safety and management of transportation networks by reducing congestion and incidents. The first step toward the goal is better understanding, modeling, and detecting congestion across a network efficiently and effectively. In this study, we introduce an efficient congestion detection model. The underlying network consists of 3017 segments in I-35, I-80, I-29, and I-380 freeways with an overall length of 1570 miles and averaged (0.4-0.6) miles per segment. The result of congestion detection shows the proposed method is 90% accurate while has reduced computation time by 99.88%.
Hierarchical Reinforcement Learning By Discovering Intrinsic Options
Authors: Jesse Zhang, Haonan Yu, Wei Xu
Date: 2021-01-16 00:00:00
Proceeding: ICLR 2021 1
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks. Unlike current hierarchical RL approaches that tend to formulate goal-reaching low-level tasks or pre-define ad hoc lower-level policies, HIDIO encourages lower-level option learning that is independent of the task at hand, requiring few assumptions or little knowledge about the task structure. These options are learned through an intrinsic entropy minimization objective conditioned on the option sub-trajectories. The learned options are diverse and task-agnostic. In experiments on sparse-reward robotic manipulation and navigation tasks, HIDIO achieves higher success rates with greater sample efficiency than regular RL baselines and two state-of-the-art hierarchical RL methods.
Evaluating Online and Offline Accuracy Traversal Algorithms for k-Complete Neural Network Architectures
Authors: Yigit Alparslan, Ethan Jacob Moyer, Edward Kim
Date: 2021-01-16 00:00:00
Tasks: Binary Classification

Architecture sizes for neural networks have been studied widely and several search methods have been offered to find the best architecture size in the shortest amount of time possible. In this paper, we study compact neural network architectures for binary classification and investigate improvements in speed and accuracy when favoring overcomplete architecture candidates that have a very high-dimensional representation of the input. We hypothesize that an overcomplete model architecture that creates a relatively high-dimensional representation of the input will be not only be more accurate but would also be easier and faster to find. In an NxM search space, we propose an online traversal algorithm that finds the best architecture candidate in O(1) time for best case and O(N) amortized time for average case for any compact binary classification problem by using k-completeness as heuristics in our search. The two other offline search algorithms we implement are brute force traversal and diagonal traversal, which both find the best architecture candidate in O(NxM) time. We compare our new algorithm to brute force and diagonal searching as a baseline and report search time improvement of 52.1% over brute force and of 15.4% over diagonal search to find the most accurate neural network architecture when given the same dataset. In all cases discussed in the paper, our online traversal algorithm can find an accurate, if not better, architecture in significantly shorter amount of time.
Linguistically-Enriched and Context-Aware Zero-shot Slot Filling
Authors: A. B. Siddique, Fuad Jamour, Vagelis Hristidis
Date: 2021-01-16 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), slot-filling, Slot Filling, Zero-shot Slot Filling

Slot filling is identifying contiguous spans of words in an utterance that correspond to certain parameters (i.e., slots) of a user request/query. Slot filling is one of the most important challenges in modern task-oriented dialog systems. Supervised learning approaches have proven effective at tackling this challenge, but they need a significant amount of labeled training data in a given domain. However, new domains (i.e., unseen in training) may emerge after deployment. Thus, it is imperative that these models seamlessly adapt and fill slots from both seen and unseen domains -- unseen domains contain unseen slot types with no training data, and even seen slots in unseen domains are typically presented in different contexts. This setting is commonly referred to as zero-shot slot filling. Little work has focused on this setting, with limited experimental evaluation. Existing models that mainly rely on context-independent embedding-based similarity measures fail to detect slot values in unseen domains or do so only partially. We propose a new zero-shot slot filling neural model, LEONA, which works in three steps. Step one acquires domain-oblivious, context-aware representations of the utterance word by exploiting (a) linguistic features; (b) named entity recognition cues; (c) contextual embeddings from pre-trained language models. Step two fine-tunes these rich representations and produces slot-independent tags for each word. Step three exploits generalizable context-aware utterance-slot similarity features at the word level, uses slot-independent tags, and contextualizes them to produce slot-specific predictions for each word. Our thorough evaluation on four diverse public datasets demonstrates that our approach consistently outperforms the SOTA models by 17.52%, 22.15%, 17.42%, and 17.95% on average for unseen domains on SNIPS, ATIS, MultiWOZ, and SGD datasets, respectively.
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems
Authors: Yigit Alparslan, Ethan Jacob Moyer, Isamu Mclean Isozaki, Daniel Schwartz, Adam Dunlop, Shesh Dave, Edward Kim
Date: 2021-01-16 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, General Classification

In recent years, deep neural networks have had great success in machine learning and pattern recognition. Architecture size for a neural network contributes significantly to the success of any neural network. In this study, we optimize the selection process by investigating different search algorithms to find a neural network architecture size that yields the highest accuracy. We apply binary search on a very well-defined binary classification network search space and compare the results to those of linear search. We also propose how to relax some of the assumptions regarding the dataset so that our solution can be generalized to any binary classification problem. We report a 100-fold running time improvement over the naive linear search when we apply the binary search method to our datasets in order to find the best architecture candidate. By finding the optimal architecture size for any binary classification problem quickly, we hope that our research contributes to discovering intelligent algorithms for optimizing architecture size selection in machine learning.
Phases of learning dynamics in artificial neural networks: with or without mislabeled data
Authors: Yu Feng, Yuhai Tu
Date: 2021-01-16 00:00:00

Despite tremendous success of deep neural network in machine learning, the underlying reason for its superior learning capability remains unclear. Here, we present a framework based on statistical physics to study dynamics of stochastic gradient descent (SGD) that drives learning in neural networks. By using the minibatch gradient ensemble, we construct order parameters to characterize dynamics of weight updates in SGD. Without mislabeled data, we find that the SGD learning dynamics transitions from a fast learning phase to a slow exploration phase, which is associated with large changes in order parameters that characterize the alignment of SGD gradients and their mean amplitude. In the case with randomly mislabeled samples, SGD learning dynamics falls into four distinct phases. The system first finds solutions for the correctly labeled samples in phase I, it then wanders around these solutions in phase II until it finds a direction to learn the mislabeled samples during phase III, after which it finds solutions that satisfy all training samples during phase IV. Correspondingly, the test error decreases during phase I and remains low during phase II; however, it increases during phase III and reaches a high plateau during phase IV. The transitions between different phases can be understood by changes of order parameters that characterize the alignment of mean gradients for the correctly and incorrectly labeled samples and their (relative) strength during learning. We find that individual sample losses for the two datasets are most separated during phase II, which leads to a cleaning process to eliminate mislabeled samples for improving generalization.
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks
Authors: Jia Liu, Yaochu Jin
Date: 2021-01-16 00:00:00

Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans. To address this issue, various methods have been proposed to design network architectures that are robust to one particular type of adversarial attacks. It is practically impossible, however, to predict beforehand which type of attacks a machine learn model may suffer from. To address this challenge, we propose to search for deep neural architectures that are robust to five types of well-known adversarial attacks using a multi-objective evolutionary algorithm. To reduce the computational cost, a normalized error rate of a randomly chosen attack is calculated as the robustness for each newly generated neural architecture at each generation. All non-dominated network architectures obtained by the proposed method are then fully trained against randomly chosen adversarial attacks and tested on two widely used datasets. Our experimental results demonstrate the superiority of optimized neural architectures found by the proposed approach over state-of-the-art networks that are widely used in the literature in terms of the classification accuracy under different adversarial attacks.
Bladder segmentation based on deep learning approaches: current limitations and lessons
Authors: Mark G. Bandyk, Dheeraj R Gopireddy, Chandana Lall, K. C. Balaji, Jose Dolz
Date: 2021-01-16 00:00:00
Tasks: Bladder Segmentation, Deep Learning, Segmentation

Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumour. Hence, multi region segmentation on patients presenting with symptoms of bladder tumours using deep learning heralds a new level of staging accuracy and prediction of the biologic behaviour of the tumour. Nevertheless, despite the success of these models in other medical problems, progress in multi region bladder segmentation is still at a nascent stage, with just a handful of works tackling a multi region scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems
Authors: Alexander Robey, Lars Lindemann, Stephen Tu, Nikolai Matni
Date: 2021-01-16 00:00:00

The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
Binary strings of finite VC dimension
Authors: Hunter R Johnson
Date: 2021-01-16 00:00:00
Tasks: Diversity

Any binary string can be associated with a unary predicate $P$ on $\mathbb{N}$. In this paper we investigate subsets named by a predicate $P$ such that the relation $P(x+y)$ has finite VC dimension. This provides a measure of complexity for binary strings with different properties than the standard string complexity function (based on diversity of substrings). We prove that strings of bounded VC dimension are meagre in the topology of the reals, provide simple rules for bounding the VC dimension of a string, and show that the bi-infinite strings of VC dimension $d$ are a non-sofic shift space. Additionally we characterize the irreducible strings of low VC dimension (0,1 and 2), and provide connections to mathematical logic.
Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation
Authors: Hamed Jelodar, Rita Orji, Stan Matwin, Swarna Weerasinghe, Oladapo Oyebode, Yongli Wang
Date: 2021-01-16 00:00:00
Tasks: Medical Diagnosis

Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of emotional feelings and their trends help implement timely intervention strategies. Given the limitations of medical diagnosis of early emotional change signs during the quarantine period, artificial intelligence models provide effective mechanisms in uncovering early signs, symptoms and escalating trends. Novelty of the approach presented herein is a multitask methodological framework of text data processing, implemented as a pipeline for meaningful emotion detection and analysis, based on the Plutchik/Ekman approach to emotion detection and trend detection. We present an evaluation of the framework and a pilot system. Results of confirm the effectiveness of the proposed framework for topic trends and emotion detection of COVID-19 tweets. Our findings revealed Stay-At-Home restrictions result in people expressing on twitter both negative and positive emotional semantics. Semantic trends of safety issues related to staying at home rapidly decreased within the 28 days and also negative feelings related to friends dying and quarantined life increased in some days. These findings have potential to impact public health policy decisions through monitoring trends of emotional feelings of those who are quarantined. The framework presented here has potential to assist in such monitoring by using as an online emotion detection tool kit.