pwc-archive/papers-with-abstracts huggingface dataset:
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches
Authors: Philipe F. Melo, Daniel H. Dalip, Manoel M. Junior, Marcos A. Gonçalves, Fabrício Benevenuto
Date: 2017-11-21 00:00:00
Tasks: General Classification, Self-Learning, Sentiment Analysis, Transfer Learning

Sentiment analysis has become a very important tool for analysis of social
media data. There are several methods developed for this research field, many
of them working very differently from each other, covering distinct aspects of
the problem and disparate strategies. Despite the large number of existent
techniques, there is no single one which fits well in all cases or for all data
sources. Supervised approaches may be able to adapt to specific situations but
they require manually labeled training, which is very cumbersome and expensive
to acquire, mainly for a new application. In this context, in here, we propose
to combine several very popular and effective state-of-the-practice sentiment
analysis methods, by means of an unsupervised bootstrapped strategy for
polarity classification. One of our main goals is to reduce the large
variability (lack of stability) of the unsupervised methods across different
domains (datasets). Our solution was thoroughly tested considering thirteen
different datasets in several domains such as opinions, comments, and social
media. The experimental results demonstrate that our combined method (aka,
10SENT) improves the effectiveness of the classification task, but more
importantly, it solves a key problem in the field. It is consistently among the
best methods in many data types, meaning that it can produce the best (or close
to best) results in almost all considered contexts, without any additional
costs (e.g., manual labeling). Our self-learning approach is also very
independent of the base methods, which means that it is highly extensible to
incorporate any new additional method that can be envisioned in the future.
Finally, we also investigate a transfer learning approach for sentiment
analysis as a means to gather additional (unsupervised) information for the
proposed approach and we show the potential of this technique to improve our
results.
Domain Generalization by Marginal Transfer Learning
Authors: Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan, Gyemin Lee, Clayton Scott
Date: 2017-11-21 00:00:00
Tasks: Domain Generalization, General Classification, Transfer Learning

In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner. This problem arises in several applications where data distributions fluctuate because of environmental, technical, or other sources of variation. We introduce a formal framework for DG, and argue that it can be viewed as a kind of supervised learning problem by augmenting the original feature space with the marginal distribution of feature vectors. While our framework has several connections to conventional analysis of supervised learning algorithms, several unique aspects of DG require new methods of analysis. This work lays the learning theoretic foundations of domain generalization, building on our earlier conference paper where the problem of DG was introduced (Blanchard et al., 2011). We present two formal models of data generation, corresponding notions of risk, and distribution-free generalization error analysis. By focusing our attention on kernel methods, we also provide more quantitative results and a universally consistent algorithm. An efficient implementation is provided for this algorithm, which is experimentally compared to a pooling strategy on one synthetic and three real-world data sets.
Source-Target Inference Models for Spatial Instruction Understanding
Authors: Hao Tan, Mohit Bansal
Date: 2017-07-12 00:00:00
Tasks: Position, Position regression, regression, Representation Learning

Models that can execute natural language instructions for situated robotic
tasks such as assembly and navigation have several useful applications in
homes, offices, and remote scenarios. We study the semantics of
spatially-referred configuration and arrangement instructions, based on the
challenging Bisk-2016 blank-labeled block dataset. This task involves finding a
source block and moving it to the target position (mentioned via a reference
block and offset), where the blocks have no names or colors and are just
referred to via spatial location features. We present novel models for the
subtasks of source block classification and target position regression, based
on joint-loss language and spatial-world representation learning, as well as
CNN-based and dual attention models to compute the alignment between the world
blocks and the instruction phrases. For target position prediction, we compare
two inference approaches: annealed sampling via policy gradient versus
expectation inference via supervised regression. Our models achieve the new
state-of-the-art on this task, with an improvement of 47% on source block
accuracy and 22% on target position distance.
Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization
Authors: Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao
Date: 2017-11-21 00:00:00
Tasks: Diversity, Vocal Bursts Valence Prediction

When solving constrained multi-objective optimization problems, an important
issue is how to balance convergence, diversity and feasibility simultaneously.
To address this issue, this paper proposes a parameter-free constraint handling
technique, two-archive evolutionary algorithm, for constrained multi-objective
optimization. It maintains two co-evolving populations simultaneously: one,
denoted as convergence archive, is the driving force to push the population
toward the Pareto front; the other one, denoted as diversity archive, mainly
tends to maintain the population diversity. In particular, to complement the
behavior of the convergence archive and provide as much diversified information
as possible, the diversity archive aims at exploring areas under-exploited by
the convergence archive including the infeasible regions. To leverage the
complementary effects of both archives, we develop a restricted mating
selection mechanism that adaptively chooses appropriate mating parents from
them according to their evolution status. Comprehensive experiments on a series
of benchmark problems and a real-world case study fully demonstrate the
competitiveness of our proposed algorithm, comparing to five state-of-the-art
constrained evolutionary multi-objective optimizers.
Quantifying Performance of Bipedal Standing with Multi-channel EMG
Authors: Yanan Sui, Kun Ho Kim, Joel W. Burdick
Date: 2017-11-21 00:00:00
Tasks: Electromyography (EMG)

Spinal cord stimulation has enabled humans with motor complete spinal cord
injury (SCI) to independently stand and recover some lost autonomic function.
Quantifying the quality of bipedal standing under spinal stimulation is
important for spinal rehabilitation therapies and for new strategies that seek
to combine spinal stimulation and rehabilitative robots (such as exoskeletons)
in real time feedback. To study the potential for automated electromyography
(EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients
undergoing electrical spinal cord stimulation using both video and
multi-channel surface EMG recordings during spinal stimulation therapy
sessions. The quality of standing under different stimulation settings was
quantified manually by experienced clinicians. By correlating features of the
recorded EMG activity with the expert evaluations, we show that multi-channel
EMG recording can provide accurate, fast, and robust estimation for the quality
of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis
shows that the total number of EMG channels needed to effectively predict
standing quality can be reduced while maintaining high estimation accuracy,
which provides more flexibility for rehabilitation robotic systems to
incorporate EMG recordings.
Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks
Authors: Lei Huang, Xianglong Liu, Bo Lang, Adams Wei Yu, Yongliang Wang, Bo Li
Date: 2017-09-16 00:00:00
Tasks: Image Classification

Orthogonal matrix has shown advantages in training Recurrent Neural Networks
(RNNs), but such matrix is limited to be square for the hidden-to-hidden
transformation in RNNs. In this paper, we generalize such square orthogonal
matrix to orthogonal rectangular matrix and formulating this problem in
feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent
Stiefel Manifolds (OMDSM). We show that the rectangular orthogonal matrix can
stabilize the distribution of network activations and regularize FNNs. We also
propose a novel orthogonal weight normalization method to solve OMDSM.
Particularly, it constructs orthogonal transformation over proxy parameters to
ensure the weight matrix is orthogonal and back-propagates gradient information
through the transformation during training. To guarantee stability, we minimize
the distortions between proxy parameters and canonical weights over all
tractable orthogonal transformations. In addition, we design an orthogonal
linear module (OLM) to learn orthogonal filter banks in practice, which can be
used as an alternative to standard linear module. Extensive experiments
demonstrate that by simply substituting OLM for standard linear module without
revising any experimental protocols, our method largely improves the
performance of the state-of-the-art networks, including Inception and residual
networks on CIFAR and ImageNet datasets. In particular, we have reduced the
test error of wide residual network on CIFAR-100 from 20.04% to 18.61% with
such simple substitution. Our code is available online for result reproduction.
R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering
Authors: Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei zhang, Shiyu Chang, Gerald Tesauro, Bo-Wen Zhou, Jing Jiang
Date: 2017-08-31 00:00:00
Tasks: Answer Generation, Information Retrieval, Open-Domain Question Answering, Question Answering, Reading Comprehension, Reinforcement Learning, Retrieval

In recent years researchers have achieved considerable success applying
neural network methods to question answering (QA). These approaches have
achieved state of the art results in simplified closed-domain settings such as
the SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selected
passage, from which the answer to a given question may be extracted. More
recently, researchers have begun to tackle open-domain QA, in which the model
is given a question and access to a large corpus (e.g., wikipedia) instead of a
pre-selected passage (Chen et al., 2017a). This setting is more complex as it
requires large-scale search for relevant passages by an information retrieval
component, combined with a reading comprehension model that "reads" the
passages to generate an answer to the question. Performance in this setting
lags considerably behind closed-domain performance. In this paper, we present a
novel open-domain QA system called Reinforced Ranker-Reader $(R^3)$, based on
two algorithmic innovations. First, we propose a new pipeline for open-domain
QA with a Ranker component, which learns to rank retrieved passages in terms of
likelihood of generating the ground-truth answer to a given question. Second,
we propose a novel method that jointly trains the Ranker along with an
answer-generation Reader model, based on reinforcement learning. We report
extensive experimental results showing that our method significantly improves
on the state of the art for multiple open-domain QA datasets.
SilNet : Single- and Multi-View Reconstruction by Learning from Silhouettes
Authors: Olivia Wiles, Andrew Zisserman
Date: 2017-11-21 00:00:00

The objective of this paper is 3D shape understanding from single and
multiple images. To this end, we introduce a new deep-learning architecture and
loss function, SilNet, that can handle multiple views in an order-agnostic
manner. The architecture is fully convolutional, and for training we use a
proxy task of silhouette prediction, rather than directly learning a mapping
from 2D images to 3D shape as has been the target in most recent work.
  We demonstrate that with the SilNet architecture there is generalisation over
the number of views -- for example, SilNet trained on 2 views can be used with
3 or 4 views at test-time; and performance improves with more views.
  We introduce two new synthetics datasets: a blobby object dataset useful for
pre-training, and a challenging and realistic sculpture dataset; and
demonstrate on these datasets that SilNet has indeed learnt 3D shape. Finally,
we show that SilNet exceeds the state of the art on the ShapeNet benchmark
dataset, and use SilNet to generate novel views of the sculpture dataset.
Training large margin host-pathogen protein-protein interaction predictors
Authors: Abdul Hannan Basit, Wajid Arshad Abbasi, Amina Asif, Fayyaz ul Amir Afsar Minhas
Date: 2017-11-21 00:00:00

Detection of protein-protein interactions (PPIs) plays a vital role in
molecular biology. Particularly, infections are caused by the interactions of
host and pathogen proteins. It is important to identify host-pathogen
interactions (HPIs) to discover new drugs to counter infectious diseases.
Conventional wet lab PPI prediction techniques have limitations in terms of
large scale application and budget. Hence, computational approaches are
developed to predict PPIs. This study aims to develop large margin machine
learning models to predict interspecies PPIs with a special interest in
host-pathogen protein interactions (HPIs). Especially, we focus on seeking
answers to three queries that arise while developing an HPI predictor. 1) How
should we select negative samples? 2) What should be the size of negative
samples as compared to the positive samples? 3) What type of margin violation
penalty should be used to train the predictor? We compare two available methods
for negative sampling. Moreover, we propose a new method of assigning weights
to each training example in weighted SVM depending on the distance of the
negative examples from the positive examples. We have also developed a web
server for our HPI predictor called HoPItor (Host Pathogen Interaction
predicTOR) that can predict interactions between human and viral proteins. This
webserver can be accessed at the URL:
http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor.
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction
Authors: Benoît Massé, Silèye Ba, Radu Horaud
Date: 2017-03-14 00:00:00

The visual focus of attention (VFOA) has been recognized as a prominent
conversational cue. We are interested in estimating and tracking the VFOAs
associated with multi-party social interactions. We note that in this type of
situations the participants either look at each other or at an object of
interest; therefore their eyes are not always visible. Consequently both gaze
and VFOA estimation cannot be based on eye detection and tracking. We propose a
method that exploits the correlation between eye gaze and head movements. Both
VFOA and gaze are modeled as latent variables in a Bayesian switching
state-space model. The proposed formulation leads to a tractable learning
procedure and to an efficient algorithm that simultaneously tracks gaze and
visual focus. The method is tested and benchmarked using two publicly available
datasets that contain typical multi-party human-robot and human-human
interactions.
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Authors: Hongyuan Mei, Jason Eisner
Date: 2016-12-29 00:00:00
Proceeding: NeurIPS 2017 12

Many events occur in the world. Some event types are stochastically excited
or inhibited---in the sense of having their probabilities elevated or
decreased---by patterns in the sequence of previous events. Discovering such
patterns can help us predict which type of event will happen next and when. We
model streams of discrete events in continuous time, by constructing a neurally
self-modulating multivariate point process in which the intensities of multiple
event types evolve according to a novel continuous-time LSTM. This generative
model allows past events to influence the future in complex and realistic ways,
by conditioning future event intensities on the hidden state of a recurrent
neural network that has consumed the stream of past events. Our model has
desirable qualitative properties. It achieves competitive likelihood and
predictive accuracy on real and synthetic datasets, including under
missing-data conditions.
In Defense of the Triplet Loss for Person Re-Identification
Authors: Alexander Hermans, Lucas Beyer, Bastian Leibe
Date: 2017-03-22 00:00:00
Tasks: General Classification, Metric Learning, Person Re-Identification, Triplet

In the past few years, the field of computer vision has gone through a
revolution fueled mainly by the advent of large datasets and the adoption of
deep convolutional neural networks for end-to-end learning. The person
re-identification subfield is no exception to this. Unfortunately, a prevailing
belief in the community seems to be that the triplet loss is inferior to using
surrogate losses (classification, verification) followed by a separate metric
learning step. We show that, for models trained from scratch as well as
pretrained ones, using a variant of the triplet loss to perform end-to-end deep
metric learning outperforms most other published methods by a large margin.
Application of generative autoencoder in de novo molecular design
Authors: Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, Hongming Chen
Date: 2017-11-21 00:00:00
Tasks: Computational chemistry

A major challenge in computational chemistry is the generation of novel
molecular structures with desirable pharmacological and physiochemical
properties. In this work, we investigate the potential use of autoencoder, a
deep learning methodology, for de novo molecular design. Various generative
autoencoders were used to map molecule structures into a continuous latent
space and vice versa and their performance as structure generator was assessed.
Our results show that the latent space preserves chemical similarity principle
and thus can be used for the generation of analogue structures. Furthermore,
the latent space created by autoencoders were searched systematically to
generate novel compounds with predicted activity against dopamine receptor type
2 and compounds similar to known active compounds not included in the training
set were identified.
Adversarial Network Embedding
Authors: Quanyu Dai, Qiang Li, Jian Tang, Dan Wang
Date: 2017-11-21 00:00:00
Tasks: Link Prediction, Network Embedding, Node Classification, Representation Learning

Learning low-dimensional representations of networks has proved effective in
a variety of tasks such as node classification, link prediction and network
visualization. Existing methods can effectively encode different structural
properties into the representations, such as neighborhood connectivity
patterns, global structural role similarities and other high-order proximities.
However, except for objectives to capture network structural properties, most
of them suffer from lack of additional constraints for enhancing the robustness
of representations. In this paper, we aim to exploit the strengths of
generative adversarial networks in capturing latent features, and investigate
its contribution in learning stable and robust graph representations.
Specifically, we propose an Adversarial Network Embedding (ANE) framework,
which leverages the adversarial learning principle to regularize the
representation learning. It consists of two components, i.e., a structure
preserving component and an adversarial learning component. The former
component aims to capture network structural properties, while the latter
contributes to learning robust representations by matching the posterior
distribution of the latent representations to given priors. As shown by the
empirical results, our method is competitive with or superior to
state-of-the-art approaches on benchmark network embedding tasks.
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
Authors: Simon Meister, Junhwa Hur, Stefan Roth
Date: 2017-11-21 00:00:00
Tasks: Optical Flow Estimation

In the era of end-to-end deep learning, many advances in computer vision are
driven by large amounts of labeled data. In the optical flow setting, however,
obtaining dense per-pixel ground truth for real scenes is difficult and thus
such data is rare. Therefore, recent end-to-end convolutional networks for
optical flow rely on synthetic datasets for supervision, but the domain
mismatch between training and test scenarios continues to be a challenge.
Inspired by classical energy-based optical flow methods, we design an
unsupervised loss based on occlusion-aware bidirectional flow estimation and
the robust census transform to circumvent the need for ground truth flow. On
the KITTI benchmarks, our unsupervised approach outperforms previous
unsupervised deep networks by a large margin, and is even more accurate than
similar supervised methods trained on synthetic datasets alone. By optionally
fine-tuning on the KITTI training data, our method achieves competitive optical
flow accuracy on the KITTI 2012 and 2015 benchmarks, thus in addition enabling
generic pre-training of supervised networks for datasets with limited amounts
of ground truth.
Robust Object Tracking Based on Self-adaptive Search Area
Authors: Taihang Dong, Sheng Zhong
Date: 2017-11-21 00:00:00
Tasks: Computational Efficiency, Object, Object Tracking

Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating the object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.
Discussion among Different Methods of Updating Model Filter in Object Tracking
Authors: Taihang Dong, Sheng Zhong
Date: 2017-11-21 00:00:00
Tasks: Object, Object Tracking, Visual Object Tracking

Discriminative correlation filters (DCF) have recently shown excellent performance in visual object tracking area. In this paper, we summarize the methods of updating model filter from discriminative correlation filter (DCF) based tracking algorithms and analyzes similarities and differences among these methods. We deduce the relationship between updating coefficient in high dimension (kernel trick), updating filter in frequency domain and updating filter in spatial domain, and analyze the difference among these different ways. We also analyze the difference between the updating filter directly and updating filter's numerator (object response power) with updating filter's denominator (filter's power). The experiments about comparing different updating methods and visualizing the template filters are used to prove our derivation.