pwc-archive/papers-with-abstracts huggingface dataset:
Predicting human decisions with behavioral theories and machine learning
Authors: Ori Plonsky, Reut Apel, Eyal Ert, Moshe Tennenholtz, David Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell, Evan C. Carter, James F. Cavanagh, Ido Erev
Date: 2019-04-15 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Descriptive, Domain Generalization

Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines. Existing models often struggle even in highly stylized tasks like choice between lotteries. We introduce BEAST Gradient Boosting (BEAST-GB), a hybrid model integrating behavioral theory (BEAST) with machine learning. We first present CPC18, a competition for predicting risky choice, in which BEAST-GB won. Then, using two large datasets, we demonstrate BEAST-GB predicts more accurately than neural networks trained on extensive data and dozens of existing behavioral models. BEAST-GB also generalizes robustly across unseen experimental contexts, surpassing direct empirical generalization, and helps refine and improve the behavioral theory itself. Our analyses highlight the potential of anchoring predictions on behavioral theory even in data-rich settings and even when the theory alone falters. Our results underscore how integrating machine learning with theoretical frameworks, especially those-like BEAST-designed for prediction, can improve our ability to predict and understand human behavior.
A Personalized Preference Learning Framework for Caching in Mobile Networks
Authors: Adeel Malik, Joongheon Kim, Kwang Soon Kim, Won-Yong Shin
Date: 2019-04-15 00:00:00
Tasks: Collaborative Filtering

This paper comprehensively studies a content-centric mobile network based on a preference learning framework, where each mobile user is equipped with a finite-size cache. We consider a practical scenario where each user requests a content file according to its own preferences, which is motivated by the existence of heterogeneity in file preferences among different users. Under our model, we consider a single-hop-based device-to-device (D2D) content delivery protocol and characterize the average hit ratio for the following two file preference cases: the personalized file preferences and the common file preferences. By assuming that the model parameters such as user activity levels, user file preferences, and file popularity are unknown and thus need to be inferred, we present a collaborative filtering (CF)-based approach to learn these parameters. Then, we reformulate the hit ratio maximization problems into a submodular function maximization and propose two computationally efficient algorithms including a greedy approach to efficiently solve the cache allocation problems. We analyze the computational complexity of each algorithm. Moreover, we analyze the corresponding level of the approximation that our greedy algorithm can achieve compared to the optimal solution. Using a real-world dataset, we demonstrate that the proposed framework employing the personalized file preferences brings substantial gains over its counterpart for various system parameters.
Measuring the influence of mere exposure effect of TV commercial adverts on purchase behavior based on machine learning prediction models
Authors: Elisa Claire Alemán Carreón, Hirofumi Nonaka, Asahi Hentona, Hirochika Yamashiro
Date: 2019-04-15 00:00:00

Since its introduction, television has been the main channel of investment
for advertisements in order to influence customers purchase behavior. Many have
attributed the mere exposure effect as the source of influence in purchase
intention and purchase decision; however, most of the studies of television
advertisement effects are not only outdated, but their sample size is
questionable and their environments do not reflect reality. With the advent of
the internet, social media and new information technologies, many recent
studies focus on the effects of online advertisement, meanwhile, the investment
in television advertisement still has not declined. In response to this, we
applied machine learning algorithms SVM and XGBoost, as well as Logistic
Regression, to construct a number of prediction models based on at-home
advertisement exposure time and demographic data, examining the predictability
of Actual Purchase and Purchase Intention behaviors of 3000 customers across 36
different products during the span of 3 months. If models based on exposure
time had unreliable predictability in contrast to models based on demographic
data, doubts would surface about the effectiveness of the hard investment in
television advertising. Based on our results, we found that models based on
advert exposure time were consistently low in their predictability in
comparison with models based on demographic data only, and with models based on
both demographic data and exposure time data. We also found that there was not
a statistically significant difference between these last two kinds of models.
This suggests that advert exposure time has little to no effect in the
short-term in increasing positive actual purchase behavior.
Self-critical n-step Training for Image Captioning
Authors: Junlong Gao, Shiqi Wang, Shanshe Wang, Siwei Ma, Wen Gao
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Captioning, Reinforcement Learning

Existing methods for image captioning are usually trained by cross entropy
loss, which leads to exposure bias and the inconsistency between the optimizing
function and evaluation metrics. Recently it has been shown that these two
issues can be addressed by incorporating techniques from reinforcement
learning, where one of the popular techniques is the advantage actor-critic
algorithm that calculates per-token advantage by estimating state value with a
parametrized estimator at the cost of introducing estimation bias. In this
paper, we estimate state value without using a parametrized value estimator.
With the properties of image captioning, namely, the deterministic state
transition function and the sparse reward, state value is equivalent to its
preceding state-action value, and we reformulate advantage function by simply
replacing the former with the latter. Moreover, the reformulated advantage is
extended to n-step, which can generally increase the absolute value of the mean
of reformulated advantage while lowering variance. Then two kinds of rollout
are adopted to estimate state-action value, which we call self-critical n-step
training. Empirically we find that our method can obtain better performance
compared to the state-of-the-art methods that use the sequence level advantage
and parametrized estimator respectively on the widely used MSCOCO benchmark.
Pedestrian Detection in Thermal Images using Saliency Maps
Authors: Debasmita Ghose, Shasvat Mukeshkumar Desai, Sneha Bhattacharya, Deep Chakraborty, Madalina Fiterau, Tauhidur Rahman
Date: 2019-04-15 00:00:00
Tasks: Pedestrian Detection, RGB Salient Object Detection

Thermal images are mainly used to detect the presence of people at night or
in bad lighting conditions, but perform poorly at daytime. To solve this
problem, most state-of-the-art techniques employ a fusion network that uses
features from paired thermal and color images. Instead, we propose to augment
thermal images with their saliency maps, to serve as an attention mechanism for
the pedestrian detector especially during daytime. We investigate how such an
approach results in improved performance for pedestrian detection using only
thermal images, eliminating the need for paired color images. For our
experiments, we train the Faster R-CNN for pedestrian detection and report the
added effect of saliency maps generated using static and deep methods (PiCA-Net
and R3-Net). Our best performing model results in an absolute reduction of miss
rate by 13.4% and 19.4% over the baseline in day and night images respectively.
We also annotate and release pixel level masks of pedestrians on a subset of
the KAIST Multispectral Pedestrian Detection dataset, which is a first publicly
available dataset for salient pedestrian detection.
Robust Visual Tracking Revisited: From Correlation Filter to Template Matching
Authors: Fanghui Liu, Chen Gong, Xiaolin Huang, Tao Zhou, Jie Yang, DaCheng Tao
Date: 2019-04-15 00:00:00
Tasks: Template Matching, Visual Tracking

In this paper, we propose a novel matching based tracker by investigating the
relationship between template matching and the recent popular correlation
filter based trackers (CFTs). Compared to the correlation operation in CFTs, a
sophisticated similarity metric termed "mutual buddies similarity" (MBS) is
proposed to exploit the relationship of multiple reciprocal nearest neighbors
for target matching. By doing so, our tracker obtains powerful discriminative
ability on distinguishing target and background as demonstrated by both
empirical and theoretical analyses. Besides, instead of utilizing single
template with the improper updating scheme in CFTs, we design a novel online
template updating strategy named "memory filtering" (MF), which aims to select
a certain amount of representative and reliable tracking results in history to
construct the current stable and expressive template set. This scheme is
beneficial for the proposed tracker to comprehensively "understand" the target
appearance variations, "recall" some stable results. Both qualitative and
quantitative evaluations on two benchmarks suggest that the proposed tracking
method performs favorably against some recently developed CFTs and other
competitive trackers.
PIV-Based 3D Fluid Flow Reconstruction Using Light Field Camera
Authors: Zhong Li, Jinwei Ye, Yu Ji, Hao Sheng, Jingyi Yu
Date: 2019-04-15 00:00:00
Tasks: Depth Estimation, Optical Flow Estimation

Particle Imaging Velocimetry (PIV) estimates the flow of fluid by analyzing the motion of injected particles. The problem is challenging as the particles lie at different depths but have similar appearance and tracking a large number of particles is particularly difficult. In this paper, we present a PIV solution that uses densely sampled light field to reconstruct and track 3D particles. We exploit the refocusing capability and focal symmetry constraint of the light field for reliable particle depth estimation. We further propose a new motion-constrained optical flow estimation scheme by enforcing local motion rigidity and the Navier-Stoke constraint. Comprehensive experiments on synthetic and real experiments show that using a single light field camera, our technique can recover dense and accurate 3D fluid flows in small to medium volumes.
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
Authors: Qilong Wang, Jiangtao Xie, WangMeng Zuo, Lei Zhang, Peihua Li
Date: 2019-04-15 00:00:00
Tasks: Fine-Grained Visual Recognition, General Classification, GPU, Image Classification, Scene Classification, Texture Classification

Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potential for improving representation and generalization abilities of deep CNNs. However, integration of global covariance pooling into deep CNNs brings two challenges: (1) robust covariance estimation given deep features of high dimension and small sample size; (2) appropriate usage of geometry of covariances. To address these challenges, we propose a global Matrix Power Normalized COVariance (MPN-COV) Pooling. Our MPN-COV conforms to a robust covariance estimator, very suitable for scenario of high dimension and small sample size. It can also be regarded as Power-Euclidean metric between covariances, effectively exploiting their geometry. Furthermore, a global Gaussian embedding network is proposed to incorporate first-order statistics into MPN-COV. For fast training of MPN-COV networks, we implement an iterative matrix square root normalization, avoiding GPU unfriendly eigen-decomposition inherent in MPN-COV. Additionally, progressive 1x1 convolutions and group convolution are introduced to compress covariance representations. The proposed methods are highly modular, readily plugged into existing deep CNNs. Extensive experiments are conducted on large-scale object classification, scene categorization, fine-grained visual recognition and texture classification, showing our methods outperform the counterparts and obtain state-of-the-art performance.
An Empirical Investigation of Global and Local Normalization for Recurrent Neural Sequence Models Using a Continuous Relaxation to Beam Search
Authors: Kartik Goyal, Chris Dyer, Taylor Berg-Kirkpatrick
Date: 2019-04-15 00:00:00
Proceeding: NAACL 2019 6
Tasks: CCG Supertagging, Machine Translation

Globally normalized neural sequence models are considered superior to their
locally normalized equivalents because they may ameliorate the effects of label
bias. However, when considering high-capacity neural parametrizations that
condition on the whole input sequence, both model classes are theoretically
equivalent in terms of the distributions they are capable of representing.
Thus, the practical advantage of global normalization in the context of modern
neural methods remains unclear. In this paper, we attempt to shed light on this
problem through an empirical study. We extend an approach for search-aware
training via a continuous relaxation of beam search (Goyal et al., 2017b) in
order to enable training of globally normalized recurrent sequence models
through simple backpropagation. We then use this technique to conduct an
empirical study of the interaction between global normalization, high-capacity
encoders, and search-aware optimization. We observe that in the context of
inexact search, globally normalized neural models are still more effective than
their locally normalized counterparts. Further, since our training approach is
sensitive to warm-starting with pre-trained models, we also propose a novel
initialization strategy based on self-normalization for pre-training globally
normalized models. We perform analysis of our approach on two tasks: CCG
supertagging and Machine Translation, and demonstrate the importance of global
normalization under different conditions while using search-aware training.
ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging
Authors: Samarth Brahmbhatt, Cusuh Ham, Charles C. Kemp, James Hays
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Grasp Contact Prediction, Object, Translation

Grasping and manipulating objects is an important human skill. Since
hand-object contact is fundamental to grasping, capturing it can lead to
important insights. However, observing contact through external sensors is
challenging because of occlusion and the complexity of the human hand. We
present ContactDB, a novel dataset of contact maps for household objects that
captures the rich hand-object contact that occurs during grasping, enabled by
use of a thermal camera. Participants in our study grasped 3D printed objects
with a post-grasp functional intent. ContactDB includes 3750 3D meshes of 50
household objects textured with contact maps and 375K frames of synchronized
RGB-D+thermal images. To the best of our knowledge, this is the first
large-scale dataset that records detailed contact maps for human grasps.
Analysis of this data shows the influence of functional intent and object size
on grasping, the tendency to touch/avoid 'active areas', and the high frequency
of palm and proximal finger contact. Finally, we train state-of-the-art image
translation and 3D convolution algorithms to predict diverse contact patterns
from object shape. Data, code and models are available at
https://contactdb.cc.gatech.edu.
Pun Generation with Surprise
Authors: He He, Nanyun Peng, Percy Liang
Date: 2019-04-15 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling, Sentence, Text Generation

We tackle the problem of generating a pun sentence given a pair of homophones
(e.g., "died" and "dyed"). Supervised text generation is inappropriate due to
the lack of a large corpus of puns, and even if such a corpus existed, mimicry
is at odds with generating novel content. In this paper, we propose an
unsupervised approach to pun generation using a corpus of unhumorous text and
what we call the local-global surprisal principle: we posit that in a pun
sentence, there is a strong association between the pun word (e.g., "dyed") and
the distant context, as well as a strong association between the alternative
word (e.g., "died") and the immediate context. This contrast creates surprise
and thus humor. We instantiate this principle for pun generation in two ways:
(i) as a measure based on the ratio of probabilities under a language model,
and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram
model. Human evaluation shows that our retrieve-and-edit approach generates
puns successfully 31% of the time, tripling the success rate of a neural
generation baseline.
Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
Authors: Senthil Purushwalkam, Abhinav Gupta, Danny M. Kaufman, Bryan Russell
Date: 2019-04-15 00:00:00
Proceeding: ICLR 2019 5

We introduce an approach to model surface properties governing bounces in
everyday scenes. Our model learns end-to-end, starting from sensor inputs, to
predict post-bounce trajectories and infer two underlying physical properties
that govern bouncing - restitution and effective collision normals. Our model,
Bounce and Learn, comprises two modules -- a Physics Inference Module (PIM) and
a Visual Inference Module (VIM). VIM learns to infer physical parameters for
locations in a scene given a single still image, while PIM learns to model
physical interactions for the prediction task given physical parameters and
observed pre-collision 3D trajectories. To achieve our results, we introduce
the Bounce Dataset comprising 5K RGB-D videos of bouncing trajectories of a
foam ball to probe surfaces of varying shapes and materials in everyday scenes
including homes and offices. Our proposed model learns from our collected
dataset of real-world bounces and is bootstrapped with additional information
from simple physics simulations. We show on our newly collected dataset that
our model out-performs baselines, including trajectory fitting with Newtonian
physics, in predicting post-bounce trajectories and inferring physical
properties of a scene.
Learning Spatiotemporal Features of Ride-sourcing Services with Fusion Convolutional Network
Authors: Feng Xiao, Dapeng Zhang, Gang Kou, Lu Li
Date: 2019-04-15 00:00:00
Tasks: Demand Forecasting

To collectively forecast the demand for ride-sourcing services in all regions of a city, the deep learning approaches have been applied with commendable results. However, the local statistical differences throughout the geographical layout of the city make the spatial stationarity assumption of the convolution invalid, which limits the performance of CNNs on the demand forecasting task. In this paper, we propose a novel deep learning framework called LC-ST-FCN (locally connected spatiotemporal fully-convolutional neural network) to address the unique challenges of the region-level demand forecasting problem within one end-to-end architecture (E2E). We first employ the 3D convolutional layers to fuse the spatial and temporal information existed in the input and then feed the spatiotemporal features extracted by the 3D convolutional layers to the subsequent 2D convolutional layers. Afterward, the prediction value of each region is obtained by the locally connected convolutional layers which relax the parameter sharing scheme. We evaluate the proposed model on a real dataset from a ride-sourcing service platform (DiDiChuxing) and observe significant improvements compared with a bunch of baseline models. Besides, we also illustrate the effectiveness of our proposed model by visualizing how different types of convolutional layers transform their input and capture useful features. The visualization results show that fully convolutional architecture enables the model to better localize the related regions. And the locally connected layers play an important role in dealing with the local statistical differences and activating useful regions.
Personalized Re-ranking for Recommendation
Authors: Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Wenwu Ou
Date: 2019-04-15 00:00:00
Tasks: Recommendation Systems, Re-Ranking

Ranking is a core task in recommender systems, which aims at providing an ordered list of items to users. Typically, a ranking function is learned from the labeled dataset to optimize the global performance, which produces a ranking score for each individual item. However, it may be sub-optimal because the scoring function applies to each item individually and does not explicitly consider the mutual influence between items, as well as the differences of users' preferences or intents. Therefore, we propose a personalized re-ranking model for recommender systems. The proposed re-ranking model can be easily deployed as a follow-up modular after any ranking algorithm, by directly using the existing ranking feature vectors. It directly optimizes the whole recommendation list by employing a transformer structure to efficiently encode the information of all items in the list. Specifically, the Transformer applies a self-attention mechanism that directly models the global relationships between any pair of items in the whole list. We confirm that the performance can be further improved by introducing pre-trained embedding to learn personalized encoding functions for different users. Experimental results on both offline benchmarks and real-world online e-commerce systems demonstrate the significant improvements of the proposed re-ranking model.
Differential Privacy for Eye-Tracking Data
Authors: Ao Liu, Lirong Xia, Andrew Duchowski, Reynold Bailey, Kenneth Holmqvist, Eakta Jain
Date: 2019-04-15 00:00:00

As large eye-tracking datasets are created, data privacy is a pressing
concern for the eye-tracking community. De-identifying data does not guarantee
privacy because multiple datasets can be linked for inferences. A common belief
is that aggregating individuals' data into composite representations such as
heatmaps protects the individual. However, we analytically examine the privacy
of (noise-free) heatmaps and show that they do not guarantee privacy. We
further propose two noise mechanisms that guarantee privacy and analyze their
privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is
an elegant solution to preserve privacy for heatmaps. Our results have
implications for interdisciplinary research to create differentially private
mechanisms for eye tracking.
An Axiomatic Approach to Regularizing Neural Ranking Models
Authors: Corby Rosset, Bhaskar Mitra, Chenyan Xiong, Nick Craswell, Xia Song, Saurabh Tiwary
Date: 2019-04-15 00:00:00
Tasks: Information Retrieval, parameter estimation, Retrieval

Axiomatic information retrieval (IR) seeks a set of principle properties
desirable in IR models. These properties when formally expressed provide
guidance in the search for better relevance estimation functions. Neural
ranking models typically contain a large number of parameters. The training of
these models involve a search for appropriate parameter values based on large
quantities of labeled examples. Intuitively, axioms that can guide the search
for better traditional IR models should also help in better parameter
estimation for machine learning based rankers. This work explores the use of IR
axioms to augment the direct supervision from labeled data for training neural
ranking models. We modify the documents in our dataset along the lines of
well-known axioms during training and add a regularization loss based on the
agreement between the ranking model and the axioms on which version of the
document---the original or the perturbed---should be preferred. Our experiments
show that the neural ranking model achieves faster convergence and better
generalization with axiomatic regularization.