pwc-archive/papers-with-abstracts huggingface dataset:
Building machines that adapt and compute like brains
Authors: Nikolaus Kriegeskorte, Robert M. Mok
Date: 2017-11-11 00:00:00

Building machines that learn and think like humans is essential not only for
cognitive science, but also for computational neuroscience, whose ultimate goal
is to understand how cognition is implemented in biological brains. A new
cognitive computational neuroscience should build cognitive-level and neural-
level models, understand their relationships, and test both types of models
with both brain and behavioral data.
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
Authors: Michael Iliadis, Haohong Wang, Rafael Molina, Aggelos K. Katsaggelos
Date: 2016-05-08 00:00:00
Tasks: Face Identification

In this paper we propose an iterative method to address the face
identification problem with block occlusions. Our approach utilizes a robust
representation based on two characteristics in order to model contiguous errors
(e.g., block occlusion) effectively. The first fits to the errors a
distribution described by a tailored loss function. The second describes the
error image as having a specific structure (resulting in low-rank in comparison
to image size). We will show that this joint characterization is effective for
describing errors with spatial continuity. Our approach is computationally
efficient due to the utilization of the Alternating Direction Method of
Multipliers (ADMM). A special case of our fast iterative algorithm leads to the
robust representation method which is normally used to handle non-contiguous
errors (e.g., pixel corruption). Extensive results on representative face
databases (in constrained and unconstrained environments) document the
effectiveness of our method over existing robust representation methods with
respect to both identification rates and computational time.
  Code is available at Github, where you can find implementations of the
F-LR-IRNNLS and F-IRNNLS (fast version of the RRC) :
https://github.com/miliadis/FIRC
Latent Constrained Correlation Filter
Authors: Baochang Zhang, Shangzhen Luan, Chen Chen, Jungong Han, Wei Wang, Alessandro Perina, Ling Shao
Date: 2017-11-11 00:00:00
Tasks: Object Recognition, Object Tracking

Correlation filters are special classifiers designed for shift-invariant
object recognition, which are robust to pattern distortions. The recent
literature shows that combining a set of sub-filters trained based on a single
or a small group of images obtains the best performance. The idea is equivalent
to estimating variable distribution based on the data sampling (bagging), which
can be interpreted as finding solutions (variable distribution approximation)
directly from sampled data space. However, this methodology fails to account
for the variations existed in the data. In this paper, we introduce an
intermediate step -- solution sampling -- after the data sampling step to form
a subspace, in which an optimal solution can be estimated. More specifically,
we propose a new method, named latent constrained correlation filters (LCCF),
by mapping the correlation filters to a given latent subspace, and develop a
new learning framework in the latent subspace that embeds distribution-related
constraints into the original problem. To solve the optimization problem, we
introduce a subspace based alternating direction method of multipliers (SADMM),
which is proven to converge at the saddle point. Our approach is successfully
applied to three different tasks, including eye localization, car detection and
object tracking. Extensive experiments demonstrate that LCCF outperforms the
state-of-the-art methods. The source code will be publicly available.
https://github.com/bczhangbczhang/.
FLAG n' FLARE: Fast Linearly-Coupled Adaptive Gradient Methods
Authors: Xiang Cheng, Farbod Roosta-Khorasani, Stefan Palombo, Peter L. Bartlett, Michael W. Mahoney
Date: 2016-05-26 00:00:00

We consider first order gradient methods for effectively optimizing a
composite objective in the form of a sum of smooth and, potentially, non-smooth
functions. We present accelerated and adaptive gradient methods, called FLAG
and FLARE, which can offer the best of both worlds. They can achieve the
optimal convergence rate by attaining the optimal first-order oracle complexity
for smooth convex optimization. Additionally, they can adaptively and
non-uniformly re-scale the gradient direction to adapt to the limited curvature
available and conform to the geometry of the domain. We show theoretically and
empirically that, through the compounding effects of acceleration and
adaptivity, FLAG and FLARE can be highly effective for many data fitting and
machine learning applications.
End-to-end 3D shape inverse rendering of different classes of objects from a single input image
Authors: Shima Kamyab, S. Zohreh Azimifar
Date: 2017-11-11 00:00:00
Tasks: 3D Reconstruction, Decoder, Inverse Rendering

In this paper a semi-supervised deep framework is proposed for the problem of
3D shape inverse rendering from a single 2D input image. The main structure of
proposed framework consists of unsupervised pre-trained components which
significantly reduce the need to labeled data for training the whole framework.
using labeled data has the advantage of achieving to accurate results without
the need to predefined assumptions about image formation process. Three main
components are used in the proposed network: an encoder which maps 2D input
image to a representation space, a 3D decoder which decodes a representation to
a 3D structure and a mapping component in order to map 2D to 3D representation.
The only part that needs label for training is the mapping part with not too
many parameters. The other components in the network can be pre-trained
unsupervised using only 2D images or 3D data in each case. The way of
reconstructing 3D shapes in the decoder component, inspired by the model based
methods for 3D reconstruction, maps a low dimensional representation to 3D
shape space with the advantage of extracting the basis vectors of shape space
from training data itself and is not restricted to a small set of examples as
used in predefined models. Therefore, the proposed framework deals directly
with coordinate values of the point cloud representation which leads to achieve
dense 3D shapes in the output. The experimental results on several benchmark
datasets of objects and human faces and comparing with recent similar methods
shows the power of proposed network in recovering more details from single 2D
images.
MixedPeds: Pedestrian Detection in Unannotated Videos using Synthetically Generated Human-agents for Training
Authors: Ernest C. Cheung, Tsan Kwong Wong, Aniket Bera, Dinesh Manocha
Date: 2017-07-28 00:00:00
Tasks: Mixed Reality, Pedestrian Detection

We present a new method for training pedestrian detectors on an unannotated
set of images. We produce a mixed reality dataset that is composed of
real-world background images and synthetically generated static human-agents.
Our approach is general, robust, and makes no other assumptions about the
unannotated dataset regarding the number or location of pedestrians. We
automatically extract from the dataset: i) the vanishing point to calibrate the
virtual camera, and ii) the pedestrians' scales to generate a Spawn Probability
Map, which is a novel concept that guides our algorithm to place the
pedestrians at appropriate locations. After putting synthetic human-agents in
the unannotated images, we use these augmented images to train a Pedestrian
Detector, with the annotations generated along with the synthetic agents. We
conducted our experiments using Faster R-CNN by comparing the detection results
on the unannotated dataset performed by the detector trained using our approach
and detectors trained with other manually labeled datasets. We showed that our
approach improves the average precision by 5-13% over these detectors.
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Authors: Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
Date: 2017-06-26 00:00:00
Tasks: Active Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Bayesian neural networks (BNNs) with latent variables are probabilistic
models which can automatically identify complex stochastic patterns in the
data. We describe and study in these models a decomposition of predictive
uncertainty into its epistemic and aleatoric components. First, we show how
such a decomposition arises naturally in a Bayesian active learning scenario by
following an information theoretic approach. Second, we use a similar
decomposition to develop a novel risk sensitive objective for safe
reinforcement learning (RL). This objective minimizes the effect of model bias
in environments whose stochastic dynamics are described by BNNs with latent
variables. Our experiments illustrate the usefulness of the resulting
decomposition in active learning and safe RL settings.
CUR Decompositions, Similarity Matrices, and Subspace Clustering
Authors: Akram Aldroubi, Keaton Hamm, Ahmet Bugra Koku, Ali Sekmen
Date: 2017-11-11 00:00:00
Tasks: Clustering, Motion Segmentation

A general framework for solving the subspace clustering problem using the CUR
decomposition is presented. The CUR decomposition provides a natural way to
construct similarity matrices for data that come from a union of unknown
subspaces $\mathscr{U}=\underset{i=1}{\overset{M}\bigcup}S_i$. The similarity
matrices thus constructed give the exact clustering in the noise-free case.
Additionally, this decomposition gives rise to many distinct similarity
matrices from a given set of data, which allow enough flexibility to perform
accurate clustering of noisy data. We also show that two known methods for
subspace clustering can be derived from the CUR decomposition. An algorithm
based on the theoretical construction of similarity matrices is presented, and
experiments on synthetic and real data are presented to test the method.
  Additionally, an adaptation of our CUR based similarity matrices is utilized
to provide a heuristic algorithm for subspace clustering; this algorithm yields
the best overall performance to date for clustering the Hopkins155 motion
segmentation dataset.
3D Randomized Connection Network with Graph-based Label Inference
Authors: Siqi Bao, Pei Wang, Tony C. W. Mok, Albert C. S. Chung
Date: 2017-11-11 00:00:00
Tasks: Deep Learning, Image Segmentation, Semantic Segmentation

In this paper, a novel 3D deep learning network is proposed for brain MR
image segmentation with randomized connection, which can decrease the
dependency between layers and increase the network capacity. The convolutional
LSTM and 3D convolution are employed as network units to capture the long-term
and short-term 3D properties respectively. To assemble these two kinds of
spatial-temporal information and refine the deep learning outcomes, we further
introduce an efficient graph-based node selection and label inference method.
Experiments have been carried out on two publicly available databases and
results demonstrate that the proposed method can obtain competitive
performances as compared with other state-of-the-art methods.
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction
Authors: Wenting Ye, Xiang Liu, Tianwei Yue, Wenping Wang
Date: 2017-11-11 00:00:00

While linear mixed model (LMM) has shown a competitive performance in correcting spurious associations raised by population stratification, family structures, and cryptic relatedness, more challenges are still to be addressed regarding the complex structure of genotypic and phenotypic data. For example, geneticists have discovered that some clusters of phenotypes are more co-expressed than others. Hence, a joint analysis that can utilize such relatedness information in a heterogeneous data set is crucial for genetic modeling. We proposed the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction. Our method is capable of uncovering the genetic associations of a large number of phenotypes together while considering the relatedness of these phenotypes. Through extensive simulation experiments, we show that the proposed model outperforms other existing approaches and can model correlation from both population structure and shared signals. Further, we validate the effectiveness of sGLMM in the real-world genomic dataset on two different species from plants and humans. In Arabidopsis thaliana data, sGLMM behaves better than all other baseline models for 63.4% traits. We also discuss the potential causal genetic variation of Human Alzheimer's disease discovered by our model and justify some of the most important genetic loci.
Interpretable probabilistic embeddings: bridging the gap between topic models and neural networks
Authors: Anna Potapenko, Artem Popov, Konstantin Vorontsov
Date: 2017-11-11 00:00:00
Tasks: Topic Models, Word Similarity

We consider probabilistic topic models and more recent word embedding
techniques from a perspective of learning hidden semantic representations.
Inspired by a striking similarity of the two approaches, we merge them and
learn probabilistic embeddings with online EM-algorithm on word co-occurrence
data. The resulting embeddings perform on par with Skip-Gram Negative Sampling
(SGNS) on word similarity tasks and benefit in the interpretability of the
components. Next, we learn probabilistic document embeddings that outperform
paragraph2vec on a document similarity task and require less memory and time
for training. Finally, we employ multimodal Additive Regularization of Topic
Models (ARTM) to obtain a high sparsity and learn embeddings for other
modalities, such as timestamps and categories. We observe further improvement
of word similarity performance and meaningful inter-modality similarities.
STWalk: Learning Trajectory Representations in Temporal Graphs
Authors: Supriya Pandhre, Himangi Mittal, Manish Gupta, Vineeth N. Balasubramanian
Date: 2017-11-11 00:00:00
Tasks: Change Point Detection, Outlier Detection

Analyzing the temporal behavior of nodes in time-varying graphs is useful for
many applications such as targeted advertising, community evolution and outlier
detection. In this paper, we present a novel approach, STWalk, for learning
trajectory representations of nodes in temporal graphs. The proposed framework
makes use of structural properties of graphs at current and previous time-steps
to learn effective node trajectory representations. STWalk performs random
walks on a graph at a given time step (called space-walk) as well as on graphs
from past time-steps (called time-walk) to capture the spatio-temporal behavior
of nodes. We propose two variants of STWalk to learn trajectory
representations. In one algorithm, we perform space-walk and time-walk as part
of a single step. In the other variant, we perform space-walk and time-walk
separately and combine the learned representations to get the final trajectory
embedding. Extensive experiments on three real-world temporal graph datasets
validate the effectiveness of the learned representations when compared to
three baseline methods. We also show the goodness of the learned trajectory
embeddings for change point detection, as well as demonstrate that arithmetic
operations on these trajectory representations yield interesting and
interpretable results.
Deep Residual Text Detection Network for Scene Text
Authors: Xiangyu Zhu, Yingying Jiang, Shuli Yang, Xiaobing Wang, Wei Li, Pei Fu, Hua Wang, Zhenbo Luo
Date: 2017-11-11 00:00:00
Tasks: General Classification, object-detection, Object Detection, regression, Scene Text Detection, Text Detection

Scene text detection is a challenging problem in computer vision. In this
paper, we propose a novel text detection network based on prevalent object
detection frameworks. In order to obtain stronger semantic feature, we adopt
ResNet as feature extraction layers and exploit multi-level feature by
combining hierarchical convolutional networks. A vertical proposal mechanism is
utilized to avoid proposal classification, while regression layer remains
working to improve localization accuracy. Our approach evaluated on ICDAR2013
dataset achieves F-measure of 0.91, which outperforms previous state-of-the-art
results in scene text detection.
ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
Authors: Baoguang Shi, Cong Yao, Minghui Liao, Mingkun Yang, Pei Xu, Linyan Cui, Serge Belongie, Shijian Lu, Xiang Bai
Date: 2017-08-31 00:00:00
Tasks: valid

Chinese is the most widely used language in the world. Algorithms that read
Chinese text in natural images facilitate applications of various kinds.
Despite the large potential value, datasets and competitions in the past
primarily focus on English, which bares very different characteristics than
Chinese. This report introduces RCTW, a new competition that focuses on Chinese
text reading. The competition features a large-scale dataset with 12,263
annotated images. Two tasks, namely text localization and end-to-end
recognition, are set up. The competition took place from January 20 to May 31,
2017. 23 valid submissions were received from 19 teams. This report includes
dataset description, task definitions, evaluation protocols, and results
summaries and analysis. Through this competition, we call for more future
research on the Chinese text reading problem. The official website for the
competition is http://rctw.vlrlab.net
Changing Views on Curves and Surfaces
Authors: Kathlén Kohn, Bernd Sturmfels, Matthew Trager
Date: 2017-07-06 00:00:00

Visual events in computer vision are studied from the perspective of
algebraic geometry. Given a sufficiently general curve or surface in 3-space,
we consider the image or contour curve that arises by projecting from a
viewpoint. Qualitative changes in that curve occur when the viewpoint crosses
the visual event surface. We examine the components of this ruled surface, and
observe that these coincide with the iterated singular loci of the coisotropic
hypersurfaces associated with the original curve or surface. We derive
formulas, due to Salmon and Petitjean, for the degrees of these surfaces, and
show how to compute exact representations for all visual event surfaces using
algebraic methods.
Discovering conversational topics and emotions associated with Demonetization tweets in India
Authors: Mitodru Niyogi, Asim K. Pal
Date: 2017-11-11 00:00:00

Social media platforms contain great wealth of information which provides us
opportunities explore hidden patterns or unknown correlations, and understand
people's satisfaction with what they are discussing. As one showcase, in this
paper, we summarize the data set of Twitter messages related to recent
demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights
from Twitter's data. Our proposed system automatically extracts the popular
latent topics in conversations regarding demonetization discussed in Twitter
via the Latent Dirichlet Allocation (LDA) based topic model and also identifies
the correlated topics across different categories. Additionally, it also
discovers people's opinions expressed through their tweets related to the event
under consideration via the emotion analyzer. The system also employs an
intuitive and informative visualization to show the uncovered insight.
Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI),
to select the best LDA models. The obtained LDA results show that the tool can
be effectively used to extract discussion topics and summarize them for further
manual analysis.
Phrase-based Image Captioning with Hierarchical LSTM Model
Authors: Ying Hua Tan, Chee Seng Chan
Date: 2017-11-11 00:00:00
Tasks: Decoder, Image Captioning, Image Description, model, Sentence

Automatic generation of caption to describe the content of an image has been
gaining a lot of research interests recently, where most of the existing works
treat the image caption as pure sequential data. Natural language, however
possess a temporal hierarchy structure, with complex dependencies between each
subsequence. In this paper, we propose a phrase-based hierarchical Long
Short-Term Memory (phi-LSTM) model to generate image description. In contrast
to the conventional solutions that generate caption in a pure sequential
manner, our proposed model decodes image caption from phrase to sentence. It
consists of a phrase decoder at the bottom hierarchy to decode noun phrases of
variable length, and an abbreviated sentence decoder at the upper hierarchy to
decode an abbreviated form of the image description. A complete image caption
is formed by combining the generated phrases with sentence during the inference
stage. Empirically, our proposed model shows a better or competitive result on
the Flickr8k, Flickr30k and MS-COCO datasets in comparison to the state-of-the
art models. We also show that our proposed model is able to generate more novel
captions (not seen in the training data) which are richer in word contents in
all these three datasets.
A Machine Learning Approach to Routing
Authors: Asaf Valadarsky, Michael Schapira, Dafna Shahaf, Aviv Tamar
Date: 2017-08-10 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Can ideas and techniques from machine learning be leveraged to automatically
generate "good" routing configurations? We investigate the power of data-driven
routing protocols. Our results suggest that applying ideas and techniques from
deep reinforcement learning to this context yields high performance, motivating
further research along these lines.
Recommender Systems with Random Walks: A Survey
Authors: Laknath Semage
Date: 2017-11-11 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Survey

Recommender engines have become an integral component in today's e-commerce
systems. From recommending books in Amazon to finding friends in social
networks such as Facebook, they have become omnipresent.
  Generally, recommender systems can be classified into two main categories:
content based and collaborative filtering based models. Both these models build
relationships between users and items to provide recommendations. Content based
systems achieve this task by utilizing features extracted from the context
available, whereas collaborative systems use shared interests between user-item
subsets.
  There is another relatively unexplored approach for providing recommendations
that utilizes a stochastic process named random walks. This study is a survey
exploring use cases of random walks in recommender systems and an attempt at
classifying them.
Optimal and Adaptive Off-policy Evaluation in Contextual Bandits
Authors: Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudik
Date: 2016-12-04 00:00:00
Proceeding: ICML 2017 8
Tasks: Multi-Armed Bandits, Off-policy evaluation

We study the off-policy evaluation problem---estimating the value of a target
policy using data collected by another policy---under the contextual bandit
model. We consider the general (agnostic) setting without access to a
consistent model of rewards and establish a minimax lower bound on the mean
squared error (MSE). The bound is matched up to constants by the inverse
propensity scoring (IPS) and doubly robust (DR) estimators. This highlights the
difficulty of the agnostic contextual setting, in contrast with multi-armed
bandits and contextual bandits with access to a consistent reward model, where
IPS is suboptimal. We then propose the SWITCH estimator, which can use an
existing reward model (not necessarily consistent) to achieve a better
bias-variance tradeoff than IPS and DR. We prove an upper bound on its MSE and
demonstrate its benefits empirically on a diverse collection of data sets,
often outperforming prior work by orders of magnitude.