pwc-archive/papers-with-abstracts huggingface dataset:
Stochastic Matrix Factorization
Authors: Christopher Adams
Date: 2016-09-19 00:00:00
Tasks: Topic Models

This paper considers a restriction to non-negative matrix factorization in
which at least one matrix factor is stochastic. That is, the elements of the
matrix factors are non-negative and the columns of one matrix factor sum to 1.
This restriction includes topic models, a popular method for analyzing
unstructured data. It also includes a method for storing and finding pictures.
The paper presents necessary and sufficient conditions on the observed data
such that the factorization is unique. In addition, the paper characterizes
natural bounds on the parameters for any observed data and presents a
consistent least squares estimator. The results are illustrated using a topic
model analysis of PhD abstracts in economics and the problem of storing and
retrieving a set of pictures of faces.
Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis
Authors: Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao
Date: 2016-03-22 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

In aspect-based sentiment analysis, extracting aspect terms along with the
opinions being expressed from user-generated content is one of the most
important subtasks. Previous studies have shown that exploiting connections
between aspect and opinion terms is promising for this task. In this paper, we
propose a novel joint model that integrates recursive neural networks and
conditional random fields into a unified framework for explicit aspect and
opinion terms co-extraction. The proposed model learns high-level
discriminative features and double propagate information between aspect and
opinion terms, simultaneously. Moreover, it is flexible to incorporate
hand-crafted features into the proposed model to further boost its information
extraction performance. Experimental results on the SemEval Challenge 2014
dataset show the superiority of our proposed model over several baseline
methods as well as the winning systems of the challenge.
Poisson Noise Reduction with Higher-order Natural Image Prior Model
Authors: Wensen Feng, Hong Qiao, Yunjin Chen
Date: 2016-09-19 00:00:00
Tasks: Denoising, GPU, Image Restoration

Poisson denoising is an essential issue for various imaging applications,
such as night vision, medical imaging and microscopy. State-of-the-art
approaches are clearly dominated by patch-based non-local methods in recent
years. In this paper, we aim to propose a local Poisson denoising model with
both structure simplicity and good performance. To this end, we consider a
variational modeling to integrate the so-called Fields of Experts (FoE) image
prior, that has proven an effective higher-order Markov Random Fields (MRF)
model for many classic image restoration problems. We exploit several feasible
variational variants for this task. We start with a direct modeling in the
original image domain by taking into account the Poisson noise statistics,
which performs generally well for the cases of high SNR. However, this strategy
encounters problem in cases of low SNR. Then we turn to an alternative modeling
strategy by using the Anscombe transform and Gaussian statistics derived data
term. We retrain the FoE prior model directly in the transform domain. With the
newly trained FoE model, we end up with a local variational model providing
strongly competitive results against state-of-the-art non-local approaches,
meanwhile bearing the property of simple structure. Furthermore, our proposed
model comes along with an additional advantage, that the inference is very
efficient as it is well-suited for parallel computation on GPUs. For images of
size $512 \times 512$, our GPU implementation takes less than 1 second to
produce state-of-the-art Poisson denoising performance.
TODIM and TOPSIS with Z-numbers
Authors: R. A. Krohling, Artem dos Santos, A. G. C. Pacheco
Date: 2016-09-19 00:00:00
Tasks: Decision Making

In this paper, we present an approach that is able to handle with Z-numbers
in the context of Multi-Criteria Decision Making (MCDM) problems. Z-numbers are
composed of two parts, the first one is a restriction on the values that can be
assumed, and the second part is the reliability of the information. As human
beings we communicate with other people by means of natural language using
sentences like: the journey time from home to university takes about half hour,
very likely. Firstly, Z-numbers are converted to fuzzy numbers using a standard
procedure. Next, the Z-TODIM and Z-TOPSIS are presented as a direct extension
of the fuzzy TODIM and fuzzy TOPSIS, respectively. The proposed methods are
applied to two case studies and compared with the standard approach using crisp
values. Results obtained show the feasibility of the approach. In addition, a
graphical interface was built to handle with both methods Z- TODIM and Z-TOPSIS
allowing ease of use for user in other areas of knowledge.
Multi-view Dimensionality Reduction for Dialect Identification of Arabic Broadcast Speech
Authors: Sameer Khurana, Ahmed Ali, Steve Renals
Date: 2016-09-19 00:00:00
Tasks: Dialect Identification, Dimensionality Reduction

In this work, we present a new Vector Space Model (VSM) of speech utterances
for the task of spoken dialect identification. Generally, DID systems are built
using two sets of features that are extracted from speech utterances; acoustic
and phonetic. The acoustic and phonetic features are used to form vector
representations of speech utterances in an attempt to encode information about
the spoken dialects. The Phonotactic and Acoustic VSMs, thus formed, are used
for the task of DID. The aim of this paper is to construct a single VSM that
encodes information about spoken dialects from both the Phonotactic and
Acoustic VSMs. Given the two views of the data, we make use of a well known
multi-view dimensionality reduction technique known as Canonical Correlation
Analysis (CCA), to form a single vector representation for each speech
utterance that encodes dialect specific discriminative information from both
the phonetic and acoustic representations. We refer to this approach as feature
space combination approach and show that our CCA based feature vector
representation performs better on the Arabic DID task than the phonetic and
acoustic feature representations used alone. We also present the feature space
combination approach as a viable alternative to the model based combination
approach, where two DID systems are built using the two VSMs (Phonotactic and
Acoustic) and the final prediction score is the output score combination from
the two systems.
Deeper Depth Prediction with Fully Convolutional Residual Networks
Authors: Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, Nassir Navab
Date: 2016-06-01 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Prediction

This paper addresses the problem of estimating the depth map of a scene given
a single RGB image. We propose a fully convolutional architecture, encompassing
residual learning, to model the ambiguous mapping between monocular images and
depth maps. In order to improve the output resolution, we present a novel way
to efficiently learn feature map up-sampling within the network. For
optimization, we introduce the reverse Huber loss that is particularly suited
for the task at hand and driven by the value distributions commonly present in
depth maps. Our model is composed of a single architecture that is trained
end-to-end and does not rely on post-processing techniques, such as CRFs or
other additional refinement steps. As a result, it runs in real-time on images
or videos. In the evaluation, we show that the proposed model contains fewer
parameters and requires fewer training data than the current state of the art,
while outperforming all approaches on depth estimation. Code and models are
publicly available.
RISAS: A Novel Rotation, Illumination, Scale Invariant Appearance and Shape Feature
Authors: Kanzhi Wu, Xiaoyang Li, Ravindra Ranasinghe, Gamini Dissanayake, Yong liu
Date: 2016-03-14 00:00:00

This paper presents a novel appearance and shape feature, RISAS, which is
robust to viewpoint, illumination, scale and rotation variations. RISAS
consists of a keypoint detector and a feature descriptor both of which utilise
texture and geometric information present in the appearance and shape channels.
A novel response function based on the surface normals is used in combination
with the Harris corner detector for selecting keypoints in the scene. A
strategy that uses the depth information for scale estimation and background
elimination is proposed to select the neighbourhood around the keypoints in
order to build precise invariant descriptors. Proposed descriptor relies on the
ordering of both grayscale intensity and shape information in the
neighbourhood. Comprehensive experiments which confirm the effectiveness of the
proposed RGB-D feature when compared with CSHOT and LOIND are presented.
Furthermore, we highlight the utility of incorporating texture and shape
information in the design of both the detector and the descriptor by
demonstrating the enhanced performance of CSHOT and LOIND when combined with
RISAS detector.
Learning with Clustering Structure
Authors: Vincent Roulet, Fajwel Fogel, Alexandre d'Aspremont, Francis Bach
Date: 2015-06-16 00:00:00
Tasks: Clustering, text-classification, Text Classification

We study supervised learning problems using clustering constraints to impose
structure on either features or samples, seeking to help both prediction and
interpretation. The problem of clustering features arises naturally in text
classification for instance, to reduce dimensionality by grouping words
together and identify synonyms. The sample clustering problem on the other
hand, applies to multiclass problems where we are allowed to make multiple
predictions and the performance of the best answer is recorded. We derive a
unified optimization formulation highlighting the common structure of these
problems and produce algorithms whose core iteration complexity amounts to a
k-means clustering step, which can be approximated efficiently. We extend these
results to combine sparsity and clustering constraints, and develop a new
projection algorithm on the set of clustered sparse vectors. We prove
convergence of our algorithms on random instances, based on a union of
subspaces interpretation of the clustering structure. Finally, we test the
robustness of our methods on artificial data sets as well as real data
extracted from movie reviews.
Coarse-to-fine Surgical Instrument Detection for Cataract Surgery Monitoring
Authors: Hassan Al Hajj, Gwenolé Quellec, Mathieu Lamard, Guy Cazuguel, Béatrice Cochener
Date: 2016-09-19 00:00:00

The amount of surgical data, recorded during video-monitored surgeries, has
extremely increased. This paper aims at improving existing solutions for the
automated analysis of cataract surgeries in real time. Through the analysis of
a video recording the operating table, it is possible to know which instruments
exit or enter the operating table, and therefore which ones are likely being
used by the surgeon. Combining these observations with observations from the
microscope video should enhance the overall performance of the systems. To this
end, the proposed solution is divided into two main parts: one to detect the
instruments at the beginning of the surgery and one to update the list of
instruments every time a change is detected in the scene. In the first part,
the goal is to separate the instruments from the background and from irrelevant
objects. For the second, we are interested in detecting the instruments that
appear and disappear whenever the surgeon interacts with the table. Experiments
on a dataset of 36 cataract surgeries validate the good performance of the
proposed solution.
Enhancing LambdaMART Using Oblivious Trees
Authors: Michal Ferov, Marek Modrý
Date: 2016-09-19 00:00:00
Tasks: Collaborative Filtering, Learning-To-Rank, Question Answering, regression, Retrieval

Learning to rank is a machine learning technique broadly used in many areas
such as document retrieval, collaborative filtering or question answering. We
present experimental results which suggest that the performance of the current
state-of-the-art learning to rank algorithm LambdaMART, when used for document
retrieval for search engines, can be improved if standard regression trees are
replaced by oblivious trees. This paper provides a comparison of both variants
and our results demonstrate that the use of oblivious trees can improve the
performance by more than $2.2\%$. Additional experimental analysis of the
influence of a number of features and of a size of the training set is also
provided and confirms the desirability of properties of oblivious decision
trees.
Fast Single Shot Detection and Pose Estimation
Authors: Patrick Poirson, Phil Ammirato, Cheng-Yang Fu, Wei Liu, Jana Kosecka, Alexander C. Berg
Date: 2016-09-19 00:00:00
Tasks: GPU, Object Tracking, Pose Estimation

For applications in navigation and robotics, estimating the 3D pose of
objects is as important as detection. Many approaches to pose estimation rely
on detecting or tracking parts or keypoints [11, 21]. In this paper we build on
a recent state-of-the-art convolutional network for slidingwindow detection
[10] to provide detection and rough pose estimation in a single shot, without
intermediate stages of detecting parts or initial bounding boxes. While not the
first system to treat pose estimation as a categorization problem, this is the
first attempt to combine detection and pose estimation at the same level using
a deep learning approach. The key to the architecture is a deep convolutional
network where scores for the presence of an object category, the offset for its
location, and the approximate pose are all estimated on a regular grid of
locations in the image. The resulting system is as accurate as recent work on
pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly
faster (46 frames per second (FPS) on a TITAN X GPU). This approach to
detection and rough pose estimation is fast and accurate enough to be widely
applied as a pre-processing step for tasks including high-accuracy pose
estimation, object tracking and localization, and vSLAM.
Tensor Completion by Alternating Minimization under the Tensor Train (TT) Model
Authors: Wenqi Wang, Vaneet Aggarwal, Shuchin Aeron
Date: 2016-09-19 00:00:00
Tasks: Matrix Completion

Using the matrix product state (MPS) representation of tensor train
decompositions, in this paper we propose a tensor completion algorithm which
alternates over the matrices (tensors) in the MPS representation. This
development is motivated in part by the success of matrix completion algorithms
which alternate over the (low-rank) factors. We comment on the computational
complexity of the proposed algorithm and numerically compare it with existing
methods employing low rank tensor train approximation for data completion as
well as several other recently proposed methods. We show that our method is
superior to existing ones for a variety of real settings.
Color: A Crucial Factor for Aesthetic Quality Assessment in a Subjective Dataset of Paintings
Authors: Seyed Ali Amirshahi, Gregor Uwe Hayn-Leichsenring, Joachim Denzler, Christoph Redies
Date: 2016-09-19 00:00:00
Tasks: General Classification

Computational aesthetics is an emerging field of research which has attracted
different research groups in the last few years. In this field, one of the main
approaches to evaluate the aesthetic quality of paintings and photographs is a
feature-based approach. Among the different features proposed to reach this
goal, color plays an import role. In this paper, we introduce a novel dataset
that consists of paintings of Western provenance from 36 well-known painters
from the 15th to the 20th century. As a first step and to assess this dataset,
using a classifier, we investigate the correlation between the subjective
scores and two widely used features that are related to color perception and in
different aesthetic quality assessment approaches. Results show a
classification rate of up to 73% between the color features and the subjective
scores.
Topic Aware Neural Response Generation
Authors: Chen Xing, Wei Wu, Yu Wu, Jie Liu, YaLou Huang, Ming Zhou, Wei-Ying Ma
Date: 2016-06-21 00:00:00
Tasks: Response Generation

We consider incorporating topic information into the sequence-to-sequence
framework to generate informative and interesting responses for chatbots. To
this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The
model utilizes topics to simulate prior knowledge of human that guides them to
form informative and interesting responses in conversation, and leverages the
topic information in generation by a joint attention mechanism and a biased
generation probability. The joint attention mechanism summarizes the hidden
vectors of an input message as context vectors by message attention,
synthesizes topic vectors by topic attention from the topic words of the
message obtained from a pre-trained LDA model, and let these vectors jointly
affect the generation of words in decoding. To increase the possibility of
topic words appearing in responses, the model modifies the generation
probability of topic words by adding an extra probability item to bias the
overall distribution. Empirical study on both automatic evaluation metrics and
human annotations shows that TA-Seq2Seq can generate more informative and
interesting responses, and significantly outperform the-state-of-the-art
response generation models.
Typical Stability
Authors: Raef Bassily, Yoav Freund
Date: 2016-04-12 00:00:00

In this paper, we introduce a notion of algorithmic stability called typical
stability. When our goal is to release real-valued queries (statistics)
computed over a dataset, this notion does not require the queries to be of
bounded sensitivity -- a condition that is generally assumed under differential
privacy [DMNS06, Dwork06] when used as a notion of algorithmic stability
[DFHPRR15a, DFHPRR15b, BNSSSU16] -- nor does it require the samples in the
dataset to be independent -- a condition that is usually assumed when
generalization-error guarantees are sought. Instead, typical stability requires
the output of the query, when computed on a dataset drawn from the underlying
distribution, to be concentrated around its expected value with respect to that
distribution.
  We discuss the implications of typical stability on the generalization error
(i.e., the difference between the value of the query computed on the dataset
and the expected value of the query with respect to the true data
distribution). We show that typical stability can control generalization error
in adaptive data analysis even when the samples in the dataset are not
necessarily independent and when queries to be computed are not necessarily of
bounded-sensitivity as long as the results of the queries over the dataset
(i.e., the computed statistics) follow a distribution with a "light" tail.
Examples of such queries include, but not limited to, subgaussian and
subexponential queries.
  We also discuss the composition guarantees of typical stability and prove
composition theorems that characterize the degradation of the parameters of
typical stability under $k$-fold adaptive composition. We also give simple
noise-addition algorithms that achieve this notion. These algorithms are
similar to their differentially private counterparts, however, the added noise
is calibrated differently.
Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
Authors: Russell Stewart, Stefano Ermon
Date: 2016-09-18 00:00:00

In many machine learning applications, labeled data is scarce and obtaining
more labels is expensive. We introduce a new approach to supervising neural
networks by specifying constraints that should hold over the output space,
rather than direct examples of input-output pairs. These constraints are
derived from prior domain knowledge, e.g., from known laws of physics. We
demonstrate the effectiveness of this approach on real world and simulated
computer vision tasks. We are able to train a convolutional neural network to
detect and track objects without any labeled examples. Our approach can
significantly reduce the need for labeled training data, but introduces new
challenges for encoding prior knowledge into appropriate loss functions.
From Multiview Image Curves to 3D Drawings
Authors: Anil Usumezbas, Ricardo Fabbri, Benjamin B. Kimia
Date: 2016-09-18 00:00:00

Reconstructing 3D scenes from multiple views has made impressive strides in
recent years, chiefly by correlating isolated feature points, intensity
patterns, or curvilinear structures. In the general setting - without
controlled acquisition, abundant texture, curves and surfaces following
specific models or limiting scene complexity - most methods produce unorganized
point clouds, meshes, or voxel representations, with some exceptions producing
unorganized clouds of 3D curve fragments. Ideally, many applications require
structured representations of curves, surfaces and their spatial relationships.
This paper presents a step in this direction by formulating an approach that
combines 2D image curves into a collection of 3D curves, with topological
connectivity between them represented as a 3D graph. This results in a 3D
drawing, which is complementary to surface representations in the same sense as
a 3D scaffold complements a tent taut over it. We evaluate our results against
truth on synthetic and real datasets.
Opponent Modeling in Deep Reinforcement Learning
Authors: He He, Jordan Boyd-Graber, Kevin Kwok, Hal Daumé III
Date: 2016-09-18 00:00:00
Tasks: Deep Reinforcement Learning, Mixture-of-Experts, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Opponent modeling is necessary in multi-agent settings where secondary agents
with competing goals also adapt their strategies, yet it remains challenging
because strategies interact with each other and change. Most previous work
focuses on developing probabilistic models or parameterized strategies for
specific applications. Inspired by the recent success of deep reinforcement
learning, we present neural-based models that jointly learn a policy and the
behavior of opponents. Instead of explicitly predicting the opponent's action,
we encode observation of the opponents into a deep Q-Network (DQN); however, we
retain explicit modeling (if desired) using multitasking. By using a
Mixture-of-Experts architecture, our model automatically discovers different
strategy patterns of opponents without extra supervision. We evaluate our
models on a simulated soccer game and a popular trivia game, showing superior
performance over DQN and its variants.
Learning Personalized Optimal Control for Repeatedly Operated Systems
Authors: Theja Tulabandhula
Date: 2016-09-18 00:00:00

We consider the problem of online learning of optimal control for repeatedly
operated systems in the presence of parametric uncertainty. During each round
of operation, environment selects system parameters according to a fixed but
unknown probability distribution. These parameters govern the dynamics of a
plant. An agent chooses a control input to the plant and is then revealed the
cost of the choice. In this setting, we design an agent that personalizes the
control input to this plant taking into account the stochasticity involved. We
demonstrate the effectiveness of our approach on a simulated system.