pwc-archive/papers-with-abstracts huggingface dataset:
A Computational Model of Afterimages based on Simultaneous and Successive Contrasts
Authors: Jinhui Yu, Kailin Wu, Kang Zhang, Xianjun Sam Zheng
Date: 2017-09-13 00:00:00

Negative afterimage appears in our vision when we shift our gaze from an over
stimulated original image to a new area with a uniform color. The colors of
negative afterimages differ from the old stimulating colors in the original
image when the color in the new area is either neutral or chromatic. The
interaction between stimulating colors in the test and inducing field in the
original image changes our color perception due to simultaneous contrast, and
the interaction between changed colors perceived in the previously-viewed field
and the color in the currently-viewed field also affects our perception of
colors in negative afterimages due to successive contrast. Based on these
observations we propose a computational model to estimate colors of negative
afterimages in more general cases where the original stimulating color in the
test field is chromatic, and the original stimulating color in the inducing
field and the new stimulating color can be either neutral or chromatic. We
validate our model with human experiments.
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection
Authors: Allison Del Giorno, J. Andrew Bagnell, Martial Hebert
Date: 2017-09-13 00:00:00
Tasks: Anomaly Detection, Descriptive

In the anomaly detection setting, the native feature embedding can be a
crucial source of bias. We present a technique, Feature Omission using Context
in Unsupervised Settings (FOCUS) to learn a feature mapping that is invariant
to changes exemplified in training sets while retaining as much descriptive
power as possible. While this method could apply to many unsupervised settings,
we focus on applications in anomaly detection, where little task-labeled data
is available. Our algorithm requires only non-anomalous sets of data, and does
not require that the contexts in the training sets match the context of the
test set. By maximizing within-set variance and minimizing between-set
variance, we are able to identify and remove distracting features while
retaining fidelity to the descriptiveness needed at test time. In the linear
case, our formulation reduces to a generalized eigenvalue problem that can be
solved quickly and applied to test sets outside the context of the training
sets. This technique allows us to align technical definitions of anomaly
detection with human definitions through appropriate mappings of the feature
space. We demonstrate that this method is able to remove uninformative parts of
the feature space for the anomaly detection setting.
Normalized Direction-preserving Adam
Authors: Zijun Zhang, Lin Ma, Zongpeng Li, Chuan Wu
Date: 2017-09-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification

Adaptive optimization algorithms, such as Adam and RMSprop, have shown better
optimization performance than stochastic gradient descent (SGD) in some
scenarios. However, recent studies show that they often lead to worse
generalization performance than SGD, especially for training deep neural
networks (DNNs). In this work, we identify the reasons that Adam generalizes
worse than SGD, and develop a variant of Adam to eliminate the generalization
gap. The proposed method, normalized direction-preserving Adam (ND-Adam),
enables more precise control of the direction and step size for updating weight
vectors, leading to significantly improved generalization performance.
Following a similar rationale, we further improve the generalization
performance in classification tasks by regularizing the softmax logits. By
bridging the gap between SGD and Adam, we also hope to shed light on why
certain optimization algorithms generalize better than others.
Leveraging Union of Subspace Structure to Improve Constrained Clustering
Authors: John Lipor, Laura Balzano
Date: 2016-08-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Clustering, Constrained Clustering

Many clustering problems in computer vision and other contexts are also
classification problems, where each cluster shares a meaningful label. Subspace
clustering algorithms in particular are often applied to problems that fit this
description, for example with face images or handwritten digits. While it is
straightforward to request human input on these datasets, our goal is to reduce
this input as much as possible. We present a pairwise-constrained clustering
algorithm that actively selects queries based on the union-of-subspaces model.
The central step of the algorithm is in querying points of minimum margin
between estimated subspaces; analogous to classifier margin, these lie near the
decision boundary. We prove that points lying near the intersection of
subspaces are points with low margin. Our procedure can be used after any
subspace clustering algorithm that outputs an affinity matrix. We demonstrate
on several datasets that our algorithm drives the clustering error down
considerably faster than the state-of-the-art active query algorithms on
datasets with subspace structure and is competitive on other datasets.
Measuring Sample Quality with Kernels
Authors: Jackson Gorham, Lester Mackey
Date: 2017-03-06 00:00:00
Proceeding: ICML 2017 8
Tasks: Two-sample testing

Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid sampling at the cost of more biased inference. Since standard MCMC diagnostics fail to detect these biases, researchers have developed computable Stein discrepancy measures that provably determine the convergence of a sample to its target distribution. This approach was recently combined with the theory of reproducing kernels to define a closed-form kernel Stein discrepancy (KSD) computable by summing kernel evaluations across pairs of sample points. We develop a theory of weak convergence for KSDs based on Stein's method, demonstrate that commonly used KSDs fail to detect non-convergence even for Gaussian targets, and show that kernels with slowly decaying tails provably determine convergence for a large class of target distributions. The resulting convergence-determining KSDs are suitable for comparing biased, exact, and deterministic sample sequences and simpler to compute and parallelize than alternative Stein discrepancies. We use our tools to compare biased samplers, select sampler hyperparameters, and improve upon existing KSD approaches to one-sample hypothesis testing and sample quality improvement.
Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale
Authors: Adrian Albert, Jasleen Kaur, Marta Gonzalez
Date: 2017-04-10 00:00:00
Tasks: General Classification, image-classification, Image Classification

Urban planning applications (energy audits, investment, etc.) require an
understanding of built infrastructure and its environment, i.e., both
low-level, physical features (amount of vegetation, building area and geometry
etc.), as well as higher-level concepts such as land use classes (which encode
expert understanding of socio-economic end uses). This kind of data is
expensive and labor-intensive to obtain, which limits its availability
(particularly in developing countries). We analyze patterns in land use in
urban neighborhoods using large-scale satellite imagery data (which is
available worldwide from third-party providers) and state-of-the-art computer
vision techniques based on deep convolutional neural networks. For supervision,
given the limited availability of standard benchmarks for remote-sensing data,
we obtain ground truth land use class labels carefully sampled from open-source
surveys, in particular the Urban Atlas land classification dataset of $20$ land
use classes across $~300$ European cities. We use this data to train and
compare deep architectures which have recently shown good performance on
standard computer vision tasks (image classification and segmentation),
including on geospatial data. Furthermore, we show that the deep
representations extracted from satellite imagery of urban environments can be
used to compare neighborhoods across several cities. We make our dataset
available for other machine learning researchers to use for remote-sensing
applications.
Workflow Complexity for Collaborative Interactions: Where are the Metrics? -- A Challenge
Authors: Kartik Talamadupula, Biplav Srivastava, Jeffrey O. Kephart
Date: 2017-09-13 00:00:00
Tasks: Diversity

In this paper, we introduce the problem of denoting and deriving the
complexity of workflows (plans, schedules) in collaborative, planner-assisted
settings where humans and agents are trying to jointly solve a task. The
interactions -- and hence the workflows that connect the human and the agents
-- may differ according to the domain and the kind of agents. We adapt insights
from prior work in human-agent teaming and workflow analysis to suggest metrics
for workflow complexity. The main motivation behind this work is to highlight
metrics for human comprehensibility of plans and schedules. The planning
community has seen its fair share of work on the synthesis of plans that take
diversity into account -- what value do such plans hold if their generation is
not guided at least in part by metrics that reflect the ease of engaging with
and using those plans?
Learning Policies for Adaptive Tracking with Deep Feature Cascades
Authors: Chen Huang, Simon Lucey, Deva Ramanan
Date: 2017-08-09 00:00:00
Proceeding: ICCV 2017 10
Tasks: CPU, Decision Making, GPU, Object Tracking, Reinforcement Learning, Visual Object Tracking

Visual object tracking is a fundamental and time-critical vision task. Recent
years have seen many shallow tracking methods based on real-time pixel-based
correlation filters, as well as deep methods that have top performance but need
a high-end GPU. In this paper, we learn to improve the speed of deep trackers
without losing accuracy. Our fundamental insight is to take an adaptive
approach, where easy frames are processed with cheap features (such as pixel
values), while challenging frames are processed with invariant but expensive
deep features. We formulate the adaptive tracking problem as a decision-making
process, and learn an agent to decide whether to locate objects with high
confidence on an early layer, or continue processing subsequent layers of a
network. This significantly reduces the feed-forward cost for easy frames with
distinct or slow-moving objects. We train the agent offline in a reinforcement
learning fashion, and further demonstrate that learning all deep layers (so as
to provide good features for adaptive tracking) can lead to near real-time
average tracking speed of 23 fps on a single CPU while achieving
state-of-the-art performance. Perhaps most tellingly, our approach provides a
100X speedup for almost 50% of the time, indicating the power of an adaptive
approach.
Incorporating Relation Paths in Neural Relation Extraction
Authors: Wenyuan Zeng, Yankai Lin, Zhiyuan Liu, Maosong Sun
Date: 2016-09-23 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Relation, Relation Extraction

Distantly supervised relation extraction has been widely used to find novel
relational facts from plain text. To predict the relation between a pair of two
target entities, existing methods solely rely on those direct sentences
containing both entities. In fact, there are also many sentences containing
only one of the target entities, which provide rich and useful information for
relation extraction. To address this issue, we build inference chains between
two target entities via intermediate entities, and propose a path-based neural
relation extraction model to encode the relational semantics from both direct
sentences and inference chains. Experimental results on real-world datasets
show that, our model can make full use of those sentences containing only one
target entity, and achieves significant and consistent improvements on relation
extraction as compared with baselines. The source code of this paper can be
obtained from https: //github.com/thunlp/PathNRE.
Deep Asymmetric Multi-task Feature Learning
Authors: Hae Beom Lee, Eunho Yang, Sung Ju Hwang
Date: 2017-08-01 00:00:00
Proceeding: ICML 2018 7
Tasks: image-classification, Image Classification, Transfer Learning

We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can
learn deep representations shared across multiple tasks while effectively
preventing negative transfer that may happen in the feature sharing process.
Specifically, we introduce an asymmetric autoencoder term that allows reliable
predictors for the easy tasks to have high contribution to the feature learning
while suppressing the influences of unreliable predictors for more difficult
tasks. This allows the learning of less noisy representations, and enables
unreliable predictors to exploit knowledge from the reliable predictors via the
shared latent features. Such asymmetric knowledge transfer through shared
features is also more scalable and efficient than inter-task asymmetric
transfer. We validate our Deep-AMTFL model on multiple benchmark datasets for
multitask learning and image classification, on which it significantly
outperforms existing symmetric and asymmetric multitask learning models, by
effectively preventing negative transfer in deep feature learning.
An Analysis of Visual Question Answering Algorithms
Authors: Kushal Kafle, Christopher Kanan
Date: 2017-03-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In visual question answering (VQA), an algorithm must answer text-based
questions about images. While multiple datasets for VQA have been created since
late 2014, they all have flaws in both their content and the way algorithms are
evaluated on them. As a result, evaluation scores are inflated and
predominantly determined by answering easier questions, making it difficult to
compare different methods. In this paper, we analyze existing VQA algorithms
using a new dataset. It contains over 1.6 million questions organized into 12
different categories. We also introduce questions that are meaningless for a
given image to force a VQA system to reason about image content. We propose new
evaluation schemes that compensate for over-represented question-types and make
it easier to study the strengths and weaknesses of algorithms. We analyze the
performance of both baseline and state-of-the-art VQA models, including
multi-modal compact bilinear pooling (MCB), neural module networks, and
recurrent answering units. Our experiments establish how attention helps
certain categories more than others, determine which models work better than
others, and explain how simple models (e.g. MLP) can surpass more complex
models (MCB) by simply learning to answer large, easy question categories.
3D Densely Convolutional Networks for Volumetric Segmentation
Authors: Toan Duc Bui, Jitae Shin, Taesup Moon
Date: 2017-09-11 00:00:00
Tasks: Brain Segmentation, Image Segmentation, Infant Brain Mri Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

In the isointense stage, the accurate volumetric image segmentation is a
challenging task due to the low contrast between tissues. In this paper, we
propose a novel very deep network architecture based on a densely convolutional
network for volumetric brain segmentation. The proposed network architecture
provides a dense connection between layers that aims to improve the information
flow in the network. By concatenating features map of fine and coarse dense
blocks, it allows capturing multi-scale contextual information. Experimental
results demonstrate significant advantages of the proposed method over existing
methods, in terms of both segmentation accuracy and parameter efficiency in
MICCAI grand challenge on 6-month infant brain MRI segmentation.
Learning Edge Representations via Low-Rank Asymmetric Projections
Authors: Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou
Date: 2017-05-16 00:00:00
Tasks: Link Prediction

We propose a new method for embedding graphs while preserving directed edge
information. Learning such continuous-space vector representations (or
embeddings) of nodes in a graph is an important first step for using network
information (from social networks, user-item graphs, knowledge bases, etc.) in
many machine learning tasks.
  Unlike previous work, we (1) explicitly model an edge as a function of node
embeddings, and we (2) propose a novel objective, the "graph likelihood", which
contrasts information from sampled random walks with non-existent edges.
Individually, both of these contributions improve the learned representations,
especially when there are memory constraints on the total size of the
embeddings. When combined, our contributions enable us to significantly improve
the state-of-the-art by learning more concise representations that better
preserve the graph structure.
  We evaluate our method on a variety of link-prediction task including social
networks, collaboration networks, and protein interactions, showing that our
proposed method learn representations with error reductions of up to 76% and
55%, on directed and undirected graphs. In addition, we show that the
representations learned by our method are quite space efficient, producing
embeddings which have higher structure-preserving accuracy but are 10 times
smaller.
Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis
Authors: Łukasz Augustyniak, Krzysztof Rajda, Tomasz Kajdanowicz
Date: 2017-09-13 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Descriptive, Sentiment Analysis

This paper fills a gap in aspect-based sentiment analysis and aims to present
a new method for preparing and analysing texts concerning opinion and
generating user-friendly descriptive reports in natural language. We present a
comprehensive set of techniques derived from Rhetorical Structure Theory and
sentiment analysis to extract aspects from textual opinions and then build an
abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
graphs to evaluate the importance of aspects and to filter out unimportant ones
from the summary. Additionally, the paper presents a prototype solution of data
flow with interesting and valuable results. The proposed method's results
proved the high accuracy of aspect detection when applied to the gold standard
dataset.
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems
Authors: Yonatan Belinkov, James Glass
Date: 2017-09-13 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), General Classification, speech-recognition, Speech Recognition

Neural models have become ubiquitous in automatic speech recognition systems.
While neural networks are typically used as acoustic models in more complex
systems, recent studies have explored end-to-end speech recognition systems
based on neural networks, which can be trained to directly predict text from
input acoustic features. Although such systems are conceptually elegant and
simpler than traditional systems, it is less obvious how to interpret the
trained models. In this work, we analyze the speech representations learned by
a deep end-to-end model that is based on convolutional and recurrent layers,
and trained with a connectionist temporal classification (CTC) loss. We use a
pre-trained model to generate frame-level features which are given to a
classifier that is trained on frame classification into phones. We evaluate
representations from different layers of the deep model and compare their
quality for predicting phone labels. Our experiments shed light on important
aspects of the end-to-end model such as layer depth, model complexity, and
other design choices.
Learning Graph Topological Features via GAN
Authors: Weiyi Liu, Hal Cooper, Min Hwan Oh, Sailung Yeung, Pin-Yu Chen, Toyotaro Suzumura, Lingli Chen
Date: 2017-09-11 00:00:00

Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs. The hierarchical architecture consisting of multiple GANs preserves both local and global topological features and automatically partitions the input graph into representative stages for feature learning. The stages facilitate reconstruction and can be used as indicators of the importance of the associated topological structures. Experiments show that our method produces subgraphs retaining a wide range of topological features, even in early reconstruction stages (unlike a single GAN, which cannot easily identify such features, let alone reconstruct the original graph). This paper is firstline research on combining the use of GANs and graph topological analysis.
Network Classification and Categorization
Authors: James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz, Nesreen K. Ahmed, Ryan A. Rossi, Karl R. B. Schmitt, Sucheta Soundarajan
Date: 2017-09-13 00:00:00
Tasks: Classification, General Classification

To the best of our knowledge, this paper presents the first large-scale study
that tests whether network categories (e.g., social networks vs. web graphs)
are distinguishable from one another (using both categories of real-world
networks and synthetic graphs). A classification accuracy of $94.2\%$ was
achieved using a random forest classifier with both real and synthetic
networks. This work makes two important findings. First, real-world networks
from various domains have distinct structural properties that allow us to
predict with high accuracy the category of an arbitrary network. Second,
classifying synthetic networks is trivial as our models can easily distinguish
between synthetic graphs and the real-world networks they are supposed to
model.
Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
Authors: Wuming Zhang, Zhixin Shu, Dimitris Samaras, Liming Chen
Date: 2017-09-08 00:00:00
Tasks: Face Recognition, Heterogeneous Face Recognition, Image Reconstruction

Heterogeneous face recognition between color image and depth image is a much
desired capacity for real world applications where shape information is looked
upon as merely involved in gallery. In this paper, we propose a cross-modal
deep learning method as an effective and efficient workaround for this
challenge. Specifically, we begin with learning two convolutional neural
networks (CNNs) to extract 2D and 2.5D face features individually. Once
trained, they can serve as pre-trained models for another two-way CNN which
explores the correlated part between color and depth for heterogeneous
matching. Compared with most conventional cross-modal approaches, our method
additionally conducts accurate depth image reconstruction from single color
image with Conditional Generative Adversarial Nets (cGAN), and further enhances
the recognition performance by fusing multi-modal matching results. Through
both qualitative and quantitative experiments on benchmark FRGC 2D/3D face
database, we demonstrate that the proposed pipeline outperforms
state-of-the-art performance on heterogeneous face recognition and ensures a
drastically efficient on-line stage.