pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Page-wise Recommendations
Authors: Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, Jiliang Tang
Date: 2018-05-07 00:00:00
Tasks: Deep Reinforcement Learning, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recommender systems can mitigate the information overload problem by
suggesting users' personalized items. In real-world recommendations such as
e-commerce, a typical interaction between the system and its users is -- users
are recommended a page of items and provide feedback; and then the system
recommends a new page of items. To effectively capture such interaction for
recommendations, we need to solve two key problems -- (1) how to update
recommending strategy according to user's \textit{real-time feedback}, and 2)
how to generate a page of items with proper display, which pose tremendous
challenges to traditional recommender systems. In this paper, we study the
problem of page-wise recommendations aiming to address aforementioned two
challenges simultaneously. In particular, we propose a principled approach to
jointly generate a set of complementary items and the corresponding strategy to
display them in a 2-D page; and propose a novel page-wise recommendation
framework based on deep reinforcement learning, DeepPage, which can optimize a
page of items with proper display based on real-time feedback from users. The
experimental results based on a real-world e-commerce dataset demonstrate the
effectiveness of the proposed framework.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
Authors: Feng Liu, Ruiming Tang, Xutao Li, Wei-Nan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, Yuzhou Zhang
Date: 2018-10-29 00:00:00
Tasks: Collaborative Filtering, Decision Making, Deep Reinforcement Learning, Multi-Armed Bandits, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Recommendation is crucial in both academia and industry, and various techniques are proposed such as content-based collaborative filtering, matrix factorization, logistic regression, factorization machines, neural networks and multi-armed bandits. However, most of the previous studies suffer from two limitations: (1) considering the recommendation as a static procedure and ignoring the dynamic interactive nature between users and the recommender systems, (2) focusing on the immediate feedback of recommended items and neglecting the long-term rewards. To address the two limitations, in this paper we propose a novel recommendation framework based on deep reinforcement learning, called DRR. The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards. Furthermore, a state representation module is incorporated into DRR, which can explicitly capture the interactions between items and users. Three instantiation structures are developed. Extensive experiments on four real-world datasets are conducted under both the offline and online evaluation settings. The experimental results demonstrate the proposed DRR method indeed outperforms the state-of-the-art competitors.
A Compact Convolutional Neural Network for Textured Surface Anomaly Detection
Authors: Domen Racki, Dejan Tomazevic, Danijel Skocaj
Date: 2018-05-18 00:00:00
Proceeding: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) 2018 5
Tasks: Anomaly Detection, Anomaly Segmentation, Classification, General Classification, Segmentation

Convolutional neural methods have proven to outperform other approaches in various computer vision tasks. In this paper we apply the deep learning technique to the domain of automated visual surface inspection. We design a unified CNN-based framework for segmentation and detection of surface anomalies. We investigate whether a compact CNN architecture, which exhibit fewer parameters that need to be learned, can be used, while retaining high classification accuracy. We propose and evaluate a compact CNN architecture on a dataset consisting of diverse textured surfaces with variously-shaped weakly-labeled anomalies. The proposed approach achieves state-of-the-art results in terms of anomaly segmentation as well as classification.
Bolasso: model consistent Lasso estimation through the bootstrap
Authors: Francis Bach
Date: 2008-04-08 00:00:00
Tasks: model, Model Selection, regression, Variable Selection

We consider the least-square linear regression problem with regularization by
the l1-norm, a problem usually referred to as the Lasso. In this paper, we
present a detailed asymptotic analysis of model consistency of the Lasso. For
various decays of the regularization parameter, we compute asymptotic
equivalents of the probability of correct model selection (i.e., variable
selection). For a specific rate decay, we show that the Lasso selects all the
variables that should enter the model with probability tending to one
exponentially fast, while it selects all other variables with strictly positive
probability. We show that this property implies that if we run the Lasso for
several bootstrapped replications of a given sample, then intersecting the
supports of the Lasso bootstrap estimates leads to consistent model selection.
This novel variable selection algorithm, referred to as the Bolasso, is
compared favorably to other linear regression methods on synthetic data and
datasets from the UCI machine learning repository.
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification
Authors: Jesse Mu, Helen Yannakoudakis, Ekaterina Shutova
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Document Embedding, Sentence

Most current approaches to metaphor identification use restricted linguistic
contexts, e.g. by considering only a verb's arguments or the sentence
containing a phrase. Inspired by pragmatic accounts of metaphor, we argue that
broader discourse features are crucial for better metaphor identification. We
train simple gradient boosting classifiers on representations of an utterance
and its surrounding discourse learned with a variety of document embedding
methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam
metaphor identification task without the complex metaphor-specific features or
deep neural architectures employed by other systems. A qualitative analysis
further confirms the need for broader context in metaphor processing.
Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet
Authors: Youshan Zhang, Brian D. Davison
Date: 2019-04-04 00:00:00
Tasks: Classification, Domain Adaptation, General Classification

Deep neural networks have been widely used in computer vision. There are
several well trained deep neural networks for the ImageNet classification
challenge, which has played a significant role in image recognition. However,
little work has explored pre-trained neural networks for image recognition in
domain adaption. In this paper, we are the first to extract better-represented
features from a pre-trained Inception ResNet model for domain adaptation. We
then present a modified distribution alignment method for classification using
the extracted features. We test our model using three benchmark datasets
(Office+Caltech-10, Office-31, and Office-Home). Extensive experiments
demonstrate significant improvements (4.8%, 5.5%, and 10%) in classification
accuracy over the state-of-the-art.
Robust Evaluation of Language-Brain Encoding Experiments
Authors: Lisa Beinborn, Samira Abnar, Rochelle Choenni
Date: 2019-04-04 00:00:00
Tasks: Sensitivity

Language-brain encoding experiments evaluate the ability of language models
to predict brain responses elicited by language stimuli. The evaluation
scenarios for this task have not yet been standardized which makes it difficult
to compare and interpret results. We perform a series of evaluation experiments
with a consistent encoding setup and compute the results for multiple fMRI
datasets. In addition, we test the sensitivity of the evaluation measures to
randomized data and analyze the effect of voxel selection methods. Our
experimental framework is publicly available to make modelling decisions more
transparent and support reproducibility for future comparisons.
Estimating 3D Motion and Forces of Person-Object Interactions from Monocular Video
Authors: Zongmian Li, Jiri Sedlar, Justin Carpentier, Ivan Laptev, Nicolas Mansard, Josef Sivic
Date: 2019-04-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object

In this paper, we introduce a method to automatically reconstruct the 3D motion of a person interacting with an object from a single RGB video. Our method estimates the 3D poses of the person and the object, contact positions, and forces and torques actuated by the human limbs. The main contributions of this work are three-fold. First, we introduce an approach to jointly estimate the motion and the actuation forces of the person on the manipulated object by modeling contacts and the dynamics of their interactions. This is cast as a large-scale trajectory optimization problem. Second, we develop a method to automatically recognize from the input video the position and timing of contacts between the person and the object or the ground, thereby significantly simplifying the complexity of the optimization. Third, we validate our approach on a recent MoCap dataset with ground truth contact forces and demonstrate its performance on a new dataset of Internet videos showing people manipulating a variety of tools in unconstrained environments.
TightCap: 3D Human Shape Capture with Clothing Tightness Field
Authors: Xin Chen, Anqi Pang, Yang Wei, Lan Xui, Jingyi Yu
Date: 2019-04-04 00:00:00
Tasks: Virtual Try-on

In this paper, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single 3D human scan, which enables numerous applications such as virtual try-on, biometrics and body evaluation. To break the severe variations of the human poses and garments, we propose to model the clothing tightness - the displacements from the garments to the human shape implicitly in the global UV texturing domain. To this end, we utilize an enhanced statistical human template and an effective multi-stage alignment scheme to map the 3D scan into a hybrid 2D geometry image. Based on this 2D representation, we propose a novel framework to predicted clothing tightness via a novel tightness formulation, as well as an effective optimization scheme to further reconstruct multi-layer human shape and garments under various clothing categories and human postures. We further propose a new clothing tightness dataset (CTD) of human scans with a large variety of clothing styles, poses and corresponding ground-truth human shapes to stimulate further research. Extensive experiments demonstrate the effectiveness of our TightCap to achieve high-quality human shape and dressed garments reconstruction, as well as the further applications for clothing segmentation, retargeting and animation.
Libra R-CNN: Towards Balanced Learning for Object Detection
Authors: Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, Dahua Lin
Date: 2019-04-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection

Compared with model architectures, the training process, which is also
crucial to the success of detectors, has received relatively less attention in
object detection. In this work, we carefully revisit the standard training
practice of detectors, and find that the detection performance is often limited
by the imbalance during the training process, which generally consists in three
levels - sample level, feature level, and objective level. To mitigate the
adverse effects caused thereby, we propose Libra R-CNN, a simple but effective
framework towards balanced learning for object detection. It integrates three
novel components: IoU-balanced sampling, balanced feature pyramid, and balanced
L1 loss, respectively for reducing the imbalance at sample, feature, and
objective level. Benefitted from the overall balanced design, Libra R-CNN
significantly improves the detection performance. Without bells and whistles,
it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN
Faster R-CNN and RetinaNet respectively on MSCOCO.
T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor
Authors: Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, Maja Pantic
Date: 2019-04-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Pose Estimation

Recent findings indicate that over-parametrization, while crucial for
successfully training deep neural networks, also introduces large amounts of
redundancy. Tensor methods have the potential to efficiently parametrize
over-complete representations by leveraging this redundancy. In this paper, we
propose to fully parametrize Convolutional Neural Networks (CNNs) with a single
high-order, low-rank tensor. Previous works on network tensorization have
focused on parametrizing individual layers (convolutional or fully connected)
only, and perform the tensorization layer-by-layer separately. In contrast, we
propose to jointly capture the full structure of a neural network by
parametrizing it with a single high-order tensor, the modes of which represent
each of the architectural design parameters of the network (e.g. number of
convolutional blocks, depth, number of stacks, input features, etc). This
parametrization allows to regularize the whole network and drastically reduce
the number of parameters. Our model is end-to-end trainable and the low-rank
structure imposed on the weight tensor acts as an implicit regularization. We
study the case of networks with rich structure, namely Fully Convolutional
Networks (FCNs), which we propose to parametrize with a single 8th-order
tensor. We show that our approach can achieve superior performance with small
compression rates, and attain high compression rates with negligible drop in
accuracy for the challenging task of human pose estimation.
A Systematic Literature Review about the impact of Artificial Intelligence on Autonomous Vehicle Safety
Authors: A. M. Nascimento, L. F. Vismari, C. B. S. T. Molina, P. S. Cugnasca, J. B. Camargo Jr., J. R. de Almeida Jr., R. Inam, E. Fersman, M. V. Marquezini, A. Y. Hata
Date: 2019-04-04 00:00:00
Tasks: Autonomous Vehicles, Systematic Literature Review

Autonomous Vehicles (AV) are expected to bring considerable benefits to
society, such as traffic optimization and accidents reduction. They rely
heavily on advances in many Artificial Intelligence (AI) approaches and
techniques. However, while some researchers in this field believe AI is the
core element to enhance safety, others believe AI imposes new challenges to
assure the safety of these new AI-based systems and applications. In this
non-convergent context, this paper presents a systematic literature review to
paint a clear picture of the state of the art of the literature in AI on AV
safety. Based on an initial sample of 4870 retrieved papers, 59 studies were
selected as the result of the selection criteria detailed in the paper. The
shortlisted studies were then mapped into six categories to answer the proposed
research questions. An AV system model was proposed and applied to orient the
discussions about the SLR findings. As a main result, we have reinforced our
preliminary observation about the necessity of considering a serious safety
agenda for the future studies on AI-based AV systems.
YOLACT: Real-time Instance Segmentation
Authors: Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee
Date: 2019-04-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: GPU, Instance Segmentation, Real-time Instance Segmentation, Segmentation, Semantic Segmentation, Translation

We present a simple, fully-convolutional model for real-time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach. Moreover, we obtain this result after training on only one GPU. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. Finally, we also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty.
Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting
Authors: Ralph Abboud, Ismail Ilkan Ceylan, Thomas Lukasiewicz
Date: 2019-04-04 00:00:00

Weighted model counting (WMC) has emerged as a prevalent approach for probabilistic inference. In its most general form, WMC is #P-hard. Weighted DNF counting (weighted #DNF) is a special case, where approximations with probabilistic guarantees are obtained in O(nm), where n denotes the number of variables, and m the number of clauses of the input DNF, but this is not scalable in practice. In this paper, we propose a neural model counting approach for weighted #DNF that combines approximate model counting with deep learning, and accurately approximates model counts in linear time when width is bounded. We conduct experiments to validate our method, and show that our model learns and generalizes very well to large-scale #DNF instances.
Advancing NLP with Cognitive Language Processing Signals
Authors: Nora Hollenstein, Maria Barrett, Marius Troendle, Francesco Bigiolli, Nicolas Langer, Ce Zhang
Date: 2019-04-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation Classification, Sentiment Analysis

When we read, our brain processes language and generates cognitive processing
data such as gaze patterns and brain activity. These signals can be recorded
while reading. Cognitive language processing data such as eye-tracking features
have shown improvements on single NLP tasks. We analyze whether using such
human features can show consistent improvement across tasks and data sources.
We present an extensive investigation of the benefits and limitations of using
cognitive processing data for NLP. Specifically, we use gaze and EEG features
to augment models of named entity recognition, relation classification, and
sentiment analysis. These methods significantly outperform the baselines and
show the potential and current limitations of employing human language
processing data for NLP.
Visualizing Attention in Transformer-Based Language Representation Models
Authors: Jesse Vig
Date: 2019-04-04 00:00:00
Tasks: Language Modelling

We present an open-source tool for visualizing multi-head self-attention in
Transformer-based language representation models. The tool extends earlier work
by visualizing attention at three levels of granularity: the attention-head
level, the model level, and the neuron level. We describe how each of these
views can help to interpret the model, and we demonstrate the tool on the BERT
model and the OpenAI GPT-2 model. We also present three use cases for analyzing
GPT-2: detecting model bias, identifying recurring patterns, and linking
neurons to model behavior.
UU-Nets Connecting Discriminator and Generator for Image to Image Translation
Authors: Wu Jionghao
Date: 2019-04-04 00:00:00
Tasks: Decoder, Image Generation, Image-to-Image Translation, Translation

Adversarial generative model have successfully manifest itself in image
synthesis. However, the performance deteriorate and unstable, because
discriminator is far stable than generator, and it is hard to control the game
between the two modules. Various methods have been introduced to tackle the
problem such as WGAN, Relativistic GAN and their successors by adding or
restricting the loss function, which certainly help balance the min-max game,
but they all focused on the loss function ignoring the intrinsic structure
limitation. We present a UU-Net architecture inspired by U-net bridging the
encoder and the decoder, UU-Net composed by two U-Net liked modules
respectively served as generator and discriminator. Because the modules in
U-net are symmetrical, therefore it shares weights easily between all four
components. Thanks to UU-net's modules identical and symmetric property, we
could not only carried the features from inner generator's encoder to its
decoder, but also to the discriminator's encoder and decoder. By this design,
it give us more control and condition flexibility to intervene the process
between the generator and the discriminator.
Deep Multi-class Adversarial Specularity Removal
Authors: John Lin, Mohamed El Amine Seddik, Mohamed Tamaazousti, Youssef Tamaazousti, Adrien Bartoli
Date: 2019-04-04 00:00:00

We propose a novel learning approach, in the form of a fully-convolutional
neural network (CNN), which automatically and consistently removes specular
highlights from a single image by generating its diffuse component. To train
the generative network, we define an adversarial loss on a discriminative
network as in the GAN framework and combined it with a content loss. In
contrast to existing GAN approaches, we implemented the discriminator to be a
multi-class classifier instead of a binary one, to find more constraining
features. This helps the network pinpoint the diffuse manifold by providing two
more gradient terms. We also rendered a synthetic dataset designed to help the
network generalize well. We show that our model performs well across various
synthetic and real images and outperforms the state-of-the-art in consistency.
Studying Cultural Differences in Emoji Usage across the East and the West
Authors: Sharath Chandra Guntuku, Mingyang Li, Louis Tay, Lyle H. Ungar
Date: 2019-04-04 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Diversity

Global acceptance of Emojis suggests a cross-cultural, normative use of
Emojis. Meanwhile, nuances in Emoji use across cultures may also exist due to
linguistic differences in expressing emotions and diversity in conceptualizing
topics. Indeed, literature in cross-cultural psychology has found both
normative and culture-specific ways in which emotions are expressed. In this
paper, using social media, we compare the Emoji usage based on frequency,
context, and topic associations across countries in the East (China and Japan)
and the West (United States, United Kingdom, and Canada). Across the East and
the West, our study examines a) similarities and differences on the usage of
different categories of Emojis such as People, Food \& Drink, Travel \& Places
etc., b) potential mapping of Emoji use differences with previously identified
cultural differences in users' expression about diverse concepts such as death,
money emotions and family, and c) relative correspondence of validated
psycho-linguistic categories with Ekman's emotions. The analysis of Emoji use
in the East and the West reveals recognizable normative and culture specific
patterns. This research reveals the ways in which Emojis can be used for
cross-cultural communication.