pwc-archive/papers-with-abstracts huggingface dataset:
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
Authors: Greg Anderson, Shankara Pailoor, Isil Dillig, Swarat Chaudhuri
Date: 2019-04-22 00:00:00

In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval.
PLUME: Polyhedral Learning Using Mixture of Experts
Authors: Kulin Shah, P. S. Sastry, Naresh Manwani
Date: 2019-04-22 00:00:00
Tasks: Generalization Bounds, Mixture-of-Experts

In this paper, we propose a novel mixture of expert architecture for learning
polyhedral classifiers. We learn the parameters of the classifierusing an
expectation maximization algorithm. Wederive the generalization bounds of the
proposedapproach. Through an extensive simulation study, we show that the
proposed method performs comparably to other state-of-the-art approaches.
Synaptic Partner Assignment Using Attentional Voxel Association Networks
Authors: Nicholas Turner, Kisuk Lee, Ran Lu, Jingpeng Wu, Dodam Ih, H. Sebastian Seung
Date: 2019-04-22 00:00:00

Connectomics aims to recover a complete set of synaptic connections within a dataset imaged by volume electron microscopy. Many systems have been proposed for locating synapses, and recent research has included a way to identify the synaptic partners that communicate at a synaptic cleft. We re-frame the problem of identifying synaptic partners as directly generating the mask of the synaptic partners from a given cleft. We train a convolutional network to perform this task. The network takes the local image context and a binary mask representing a single cleft as input. It is trained to produce two binary output masks: one which labels the voxels of the presynaptic partner within the input image, and another similar labeling for the postsynaptic partner. The cleft mask acts as an attentional gating signal for the network. We find that an implementation of this approach performs well on a dataset of mouse somatosensory cortex, and evaluate it as part of a combined system to predict both clefts and connections.
Tracking and Improving Information in the Service of Fairness
Authors: Sumegha Garg, Michael P. Kim, Omer Reingold
Date: 2019-04-22 00:00:00
Tasks: Decision Making, Fairness, Informativeness

As algorithmic prediction systems have become widespread, fears that these systems may inadvertently discriminate against members of underrepresented populations have grown. With the goal of understanding fundamental principles that underpin the growing number of approaches to mitigating algorithmic discrimination, we investigate the role of information in fair prediction. A common strategy for decision-making uses a predictor to assign individuals a risk score; then, individuals are selected or rejected on the basis of this score. In this work, we study a formal framework for measuring the information content of predictors. Central to this framework is the notion of a refinement, first studied by Degroot and Fienberg. Intuitively, a refinement of a predictor $z$ increases the overall informativeness of the predictions without losing the information already contained in $z$. We show that increasing information content through refinements improves the downstream selection rules across a wide range of fairness measures (e.g. true positive rates, false positive rates, selection rates). In turn, refinements provide a simple but effective tool for reducing disparity in treatment and impact without sacrificing the utility of the predictions. Our results suggest that in many applications, the perceived "cost of fairness" results from an information disparity across populations, and thus, may be avoided with improved information.
Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition
Authors: Guanbin Li, Xin Zhu, Yirui Zeng, Qing Wang, Liang Lin
Date: 2019-04-22 00:00:00
Tasks: Facial Action Unit Detection, Graph Neural Network, Representation Learning

Facial action unit (AU) recognition is a crucial task for facial expressions
analysis and has attracted extensive attention in the field of artificial
intelligence and computer vision. Existing works have either focused on
designing or learning complex regional feature representations, or delved into
various types of AU relationship modeling. Albeit with varying degrees of
progress, it is still arduous for existing methods to handle complex
situations. In this paper, we investigate how to integrate the semantic
relationship propagation between AUs in a deep neural network framework to
enhance the feature representation of facial regions, and propose an AU
semantic relationship embedded representation learning (SRERL) framework.
Specifically, by analyzing the symbiosis and mutual exclusion of AUs in various
facial expressions, we organize the facial AUs in the form of structured
knowledge-graph and integrate a Gated Graph Neural Network (GGNN) in a
multi-scale CNN framework to propagate node information through the graph for
generating enhanced AU representation. As the learned feature involves both the
appearance characteristics and the AU relationship reasoning, the proposed
model is more robust and can cope with more challenging cases, e.g.,
illumination change and partial occlusion. Extensive experiments on the two
public benchmarks demonstrate that our method outperforms the previous work and
achieves state of the art performance.
Tripping through time: Efficient Localization of Activities in Videos
Authors: Meera Hahn, Asim Kadav, James M. Rehg, Hans Peter Graf
Date: 2019-04-22 00:00:00
Tasks: Reinforcement Learning

Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. Previous works have approached this task by processing the entire video, often more than once, to localize relevant activities. In the real world applications of this approach, such as video surveillance, efficiency is a key system requirement. In this paper, we present TripNet, an end-to-end system that uses a gated attention architecture to model fine-grained textual and visual representations in order to align text and video content. Furthermore, TripNet uses reinforcement learning to efficiently localize relevant activity clips in long videos, by learning how to intelligently skip around the video. It extracts visual features for few frames to perform activity classification. In our evaluation over Charades-STA, ActivityNet Captions and the TACoS dataset, we find that TripNet achieves high accuracy and saves processing time by only looking at 32-41% of the entire video.
Late or Earlier Information Fusion from Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-cGAN
Authors: Ksenia Bittner, Marco Körner, Peter Reinartz
Date: 2019-04-22 00:00:00
Tasks: Decoder

We present the workflow of a DSM refinement methodology using a Hybrid-cGAN
where the generative part consists of two encoders and a common decoder which
blends the spectral and height information within one network. The inputs to
the Hybrid-cGAN are single-channel photogrammetric DSMs with continuous values
and single-channel pan-chromatic (PAN) half-meter resolution satellite images.
Experimental results demonstrate that the earlier information fusion from data
with different physical meanings helps to propagate fine details and complete
an inaccurate or missing 3D information about building forms. Moreover, it
improves the building boundaries making them more rectilinear.
Data-Driven Interaction Review of an Ed-Tech Application
Authors: Alejandro Baldominos, David Quintana
Date: 2018-12-13 00:00:00
Tasks: Recommendation Systems

Smile and Learn is an Ed-Tech company that runs a smart library with more
that 100 applications, games and interactive stories, aimed at children aged
two to 10 and their families. The platform gathers thousands of data points
from the interaction with the system to subsequently offer reports and
recommendations. Given the complexity of navigating all the content, the
library implements a recommender system. The purpose of this paper is to
evaluate two aspects of such system focused on children: the influence of the
order of recommendations on user exploratory behavior, and the impact of the
choice of the recommendation algorithm on engagement. The assessment, based on
data collected between 15 October 2018 and 1 December 2018, required the
analysis of the number of clicks performed on the recommendations depending on
their ordering, and an A/B/C testing where two standard recommendation
algorithmswere comparedwith a randomrecommendation that served as baseline. The
results suggest a direct connection between the order of the recommendation and
the interest raised, and the superiority of recommendations based on popularity
against other alternatives.
City-scale Road Extraction from Satellite Imagery
Authors: Adam Van Etten
Date: 2019-04-22 00:00:00

Automated road network extraction from remote sensing imagery remains a significant challenge despite its importance in a broad array of applications. To this end, we leverage recent open source advances and the high quality SpaceNet dataset to explore road network extraction at scale, an approach we call City-scale Road Extraction from Satellite Imagery (CRESI). Specifically, we create an algorithm to extract road networks directly from imagery over city-scale regions, which can subsequently be used for routing purposes. We quantify the performance of our algorithm with the APLS and TOPO graph-theoretic metrics over a diverse 608 square kilometer test area covering four cities. We find an aggregate score of APLS = 0.73, and a TOPO score of 0.58 (a significant improvement over existing methods). Inference speed is 160 square kilometers per hour on modest hardware. Finally, we demonstrate that one can use the extracted road network for any number of applications, such as optimized routing.
Cut-free Calculi and Relational Semantics for Temporal STIT Logics
Authors: Kees van Berkel, Tim Lyon
Date: 2019-04-22 00:00:00

We present cut-free labelled sequent calculi for a central formalism in
logics of agency: STIT logics with temporal operators. These include sequent
systems for Ldm, Tstit and Xstit. All calculi presented possess essential
structural properties such as contraction- and cut-admissibility. The labelled
calculi G3Ldm and G3TSTIT are shown sound and complete relative to irreflexive
temporal frames. Additionally, we extend current results by showing that also
XSTIT can be characterized through relational frames, omitting the use of BT+AC
frames.
Privacy-Enhancing Fall Detection from Remote Sensor Data Using Multi-Party Computation
Authors: Pradip Mainali, Carlton Shepherd
Date: 2019-04-22 00:00:00

Motion-based fall detection systems are concerned with detecting falls from vulnerable users, which is typically performed by classifying measurements from a body-worn inertial measurement unit (IMU) using machine learning. Such systems, however, necessitate the collection of high-resolution measurements that may violate users' privacy, such as revealing their gait, activities of daily living (ADLs), and relative position using dead reckoning. In this paper, we investigate the application of multi-party computation (MPC) to IMU-based fall detection for protecting device measurement confidentiality. Our system is evaluated in a cloud-based setting that precludes parties from learning the underlying data using multiple, disparate cloud instances deployed in three geographical configurations. Using a publicly-available dataset, we demonstrate that MPC-based fall detection from IMU measurements is practical while achieving state-of-the-art error rates. In the best case, our system executes in 365.2 milliseconds, which falls well within the required time window for on-device data acquisition (750ms).
You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions
Authors: Evonne Ng, Donglai Xiang, Hanbyul Joo, Kristen Grauman
Date: 2019-04-22 00:00:00
Proceeding: CVPR 2020 6
Tasks: Pose Estimation

The body pose of a person wearing a camera is of great interest for applications in augmented reality, healthcare, and robotics, yet much of the person's body is out of view for a typical wearable camera. We propose a learning-based approach to estimate the camera wearer's 3D body pose from egocentric video sequences. Our key insight is to leverage interactions with another person---whose body pose we can directly observe---as a signal inherently linked to the body pose of the first-person subject. We show that since interactions between individuals often induce a well-ordered series of back-and-forth responses, it is possible to learn a temporal model of the interlinked poses even though one party is largely out of view. We demonstrate our idea on a variety of domains with dyadic interaction and show the substantial impact on egocentric body pose estimation, which improves the state of the art. Video results are available at http://vision.cs.utexas.edu/projects/you2me/
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
Authors: Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alex M. Bronstein, Avi Mendelson
Date: 2019-04-22 00:00:00
Tasks: Network Pruning, Neural Architecture Search, Quantization

Recently, deep learning has become a de facto standard in machine learning with convolutional neural networks (CNNs) demonstrating spectacular success on a wide variety of tasks. However, CNNs are typically very demanding computationally at inference time. One of the ways to alleviate this burden on certain hardware platforms is quantization relying on the use of low-precision arithmetic representation for the weights and the activations. Another popular method is the pruning of the number of filters in each layer. While mainstream deep learning methods train the neural networks weights while keeping the network architecture fixed, the emerging neural architecture search (NAS) techniques make the latter also amenable to training. In this paper, we formulate optimal arithmetic bit length allocation and neural network pruning as a NAS problem, searching for the configurations satisfying a computational complexity budget while maximizing the accuracy. We use a differentiable search method based on the continuous relaxation of the search space proposed by Liu et al. (arXiv:1806.09055). We show, by grid search, that heterogeneous quantized networks suffer from a high variance which renders the benefit of the search questionable. For pruning, improvement over homogeneous cases is possible, but it is still challenging to find those configurations with the proposed method. The code is publicly available at https://github.com/yochaiz/Slimmable and https://github.com/yochaiz/darts-UNIQ
Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNet
Authors: Khaled Saleh, Mohammed Hossny, Saeid Nahavandi
Date: 2019-04-22 00:00:00

Understanding the behaviors and intentions of humans are one of the main
challenges autonomous ground vehicles still faced with. More specifically, when
it comes to complex environments such as urban traffic scenes, inferring the
intentions and actions of vulnerable road users such as pedestrians become even
harder. In this paper, we address the problem of intent action prediction of
pedestrians in urban traffic environments using only image sequences from a
monocular RGB camera. We propose a real-time framework that can accurately
detect, track and predict the intended actions of pedestrians based on a
tracking-by-detection technique in conjunction with a novel spatio-temporal
DenseNet model. We trained and evaluated our framework based on real data
collected from urban traffic environments. Our framework has shown resilient
and competitive results in comparison to other baseline approaches. Overall, we
achieved an average precision score of 84.76% with a real-time performance at
20 FPS.
Learning gradient-based ICA by neurally estimating mutual information
Authors: Hlynur Davíð Hlynsson, Laurenz Wiskott
Date: 2019-04-22 00:00:00
Tasks: blind source separation

Several methods of estimating the mutual information of random variables have
been developed in recent years. They can prove valuable for novel approaches to
learning statistically independent features. In this paper, we use one of these
methods, a mutual information neural estimation (MINE) network, to present a
proof-of-concept of how a neural network can perform linear ICA. We minimize
the mutual information, as estimated by a MINE network, between the output
units of a differentiable encoder network. This is done by simple alternate
optimization of the two networks. The method is shown to get a qualitatively
equal solution to FastICA on blind-source-separation of noisy sources.
Learning to Calibrate Straight Lines for Fisheye Image Rectification
Authors: Zhu-Cun Xue, Nan Xue, Gui-Song Xia, Weiming Shen
Date: 2019-04-22 00:00:00
Proceeding: CVPR 2019 6

This paper presents a new deep-learning based method to simultaneously
calibrate the intrinsic parameters of fisheye lens and rectify the distorted
images. Assuming that the distorted lines generated by fisheye projection
should be straight after rectification, we propose a novel deep neural network
to impose explicit geometry constraints onto processes of the fisheye lens
calibration and the distorted image rectification. In addition, considering the
nonlinearity of distortion distribution in fisheye images, the proposed network
fully exploits multi-scale perception to equalize the rectification effects on
the whole image. To train and evaluate the proposed model, we also create a new
largescale dataset labeled with corresponding distortion parameters and
well-annotated distorted lines. Compared with the state-of-the-art methods, our
model achieves the best published rectification quality and the most accurate
estimation of distortion parameters on a large set of synthetic and real
fisheye images.
Simple Heuristics Yield Provable Algorithms for Masked Low-Rank Approximation
Authors: Cameron Musco, Christopher Musco, David P. Woodruff
Date: 2019-04-22 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Tensor Decomposition

In $masked\ low-rank\ approximation$, one is given $A \in \mathbb{R}^{n \times n}$ and binary mask matrix $W \in \{0,1\}^{n \times n}$. The goal is to find a rank-$k$ matrix $L$ for which: $$cost(L) = \sum_{i=1}^{n} \sum_{j = 1}^{n} W_{i,j} \cdot (A_{i,j} - L_{i,j} )^2 \leq OPT + \epsilon \|A\|_F^2 ,$$ where $OPT = \min_{rank-k\ \hat{L}} cost(\hat L)$ and $\epsilon$ is a given error parameter. Depending on the choice of $W$, this problem captures factor analysis, low-rank plus diagonal decomposition, robust PCA, low-rank matrix completion, low-rank plus block matrix approximation, and many problems. Many of these problems are NP-hard, and while some algorithms with provable guarantees are known, they either 1) run in time $n^{\Omega(k^2/\epsilon)}$ or 2) make strong assumptions, e.g., that $A$ is incoherent or that $W$ is random. In this work, we show that a common polynomial time heuristic, which simply sets $A$ to $0$ where $W$ is $0$, and then finds a standard low-rank approximation, yields bicriteria approximation guarantees for this problem. In particular, for rank $k' > k$ depending on the $public\ coin\ partition\ number$ of $W$, the heuristic outputs rank-$k'$ $L$ with cost$(L) \leq OPT + \epsilon \|A\|_F^2$. This partition number is in turn bounded by the $randomized\ communication\ complexity$ of $W$, when interpreted as a two-player communication matrix. For many important examples of masked low-rank approximation, including all those listed above, this result yields bicriteria approximation guarantees with $k' = k \cdot poly(\log n/\epsilon)$. Further, we show that different models of communication yield algorithms for natural variants of masked low-rank approximation. For example, multi-player number-in-hand communication complexity connects to masked tensor decomposition and non-deterministic communication complexity to masked Boolean low-rank factorization.
Judging Chemical Reaction Practicality From Positive Sample only Learning
Authors: Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-liang Lu, Ning Xia
Date: 2019-04-22 00:00:00

Chemical reaction practicality is the core task among all symbol intelligence
based chemical information processing, for example, it provides indispensable
clue for further automatic synthesis route inference. Considering that chemical
reactions have been represented in a language form, we propose a new solution
to generally judge the practicality of organic reaction without considering
complex quantum physical modeling or chemistry knowledge. While tackling the
practicality judgment as a machine learning task from positive and negative
(chemical reaction) samples, all existing studies have to carefully handle the
serious insufficiency issue on the negative samples. We propose an
auto-construction method to well solve the extensively existed long-term
difficulty. Experimental results show our model can effectively predict the
practicality of chemical reactions, which achieves a high accuracy of 99.76\%
on real large-scale chemical lab reaction practicality judgment.
Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module
Authors: Yingchao Feng, Wenhui Diao, Zhonghan Chang, Menglong Yan, Xian Sun, Xin Gao
Date: 2019-04-22 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

The performance of object instance segmentation in remote sensing images has
been greatly improved through the introduction of many landmark frameworks
based on convolutional neural network. However, the object densely issue still
affects the accuracy of such segmentation frameworks. Objects of the same class
are easily confused, which is most likely due to the close docking between
objects. We think context information is critical to address this issue. So, we
propose a novel framework called SLCMASK-Net, in which a sequence local context
module (SLC) is introduced to avoid confusion between objects of the same
class. The SLC module applies a sequence of dilation convolution blocks to
progressively learn multi-scale context information in the mask branch.
Besides, we try to add SLC module to different locations in our framework and
experiment with the effect of different parameter settings. Comparative
experiments are conducted on remote sensing images acquired by QuickBird with a
resolution of $0.5m-1m$ and the results show that the proposed method achieves
state-of-the-art performance.