pwc-archive/papers-with-abstracts huggingface dataset:
Effectiveness of greedily collecting items in open world games
Authors: Andrej Gajduk
Date: 2016-08-17 00:00:00

Since Pokemon Go sent millions on the quest of collecting virtual monsters,
an important question has been on the minds of many people: Is going after the
closest item first a time-and-cost-effective way to play? Here, we show that
this is in fact a good strategy which performs on average only 7% worse than
the best possible solution in terms of the total distance traveled to gather
all the items. Even when accounting for errors due to the inability of people
to accurately measure distances by eye, the performance only goes down to 16%
of the optimal solution.
DeXpression: Deep Convolutional Neural Network for Expression Recognition
Authors: Peter Burkert, Felix Trier, Muhammad Zeshan Afzal, Andreas Dengel, Marcus Liwicki
Date: 2015-09-17 00:00:00
Tasks: Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER)

We propose a convolutional neural network (CNN) architecture for facial
expression recognition. The proposed architecture is independent of any
hand-crafted feature extraction and performs better than the earlier proposed
convolutional neural network based approaches. We visualize the automatically
extracted features which have been learned by the network in order to provide a
better understanding. The standard datasets, i.e. Extended Cohn-Kanade (CKP)
and MMI Facial Expression Databse are used for the quantitative evaluation. On
the CKP set the current state of the art approach, using CNNs, achieves an
accuracy of 99.2%. For the MMI dataset, currently the best accuracy for emotion
recognition is 93.33%. The proposed architecture achieves 99.6% for CKP and
98.63% for MMI, therefore performing better than the state of the art using
CNNs. Automatic facial expression recognition has a broad spectrum of
applications such as human-computer interaction and safety systems. This is due
to the fact that non-verbal cues are important forms of communication and play
a pivotal role in interpersonal communication. The performance of the proposed
architecture endorses the efficacy and reliable usage of the proposed work for
real world applications.
Amodal Instance Segmentation
Authors: Ke Li, Jitendra Malik
Date: 2016-04-27 00:00:00
Tasks: Amodal Instance Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

We consider the problem of amodal instance segmentation, the objective of
which is to predict the region encompassing both visible and occluded parts of
each object. Thus far, the lack of publicly available amodal segmentation
annotations has stymied the development of amodal segmentation methods. In this
paper, we sidestep this issue by relying solely on standard modal instance
segmentation annotations to train our model. The result is a new method for
amodal instance segmentation, which represents the first such method to the
best of our knowledge. We demonstrate the proposed method's effectiveness both
qualitatively and quantitatively.
Practical optimal experiment design with probabilistic programs
Authors: Long Ouyang, Michael Henry Tessler, Daniel Ly, Noah Goodman
Date: 2016-08-17 00:00:00
Tasks: Ingenuity, Probabilistic Programming

Scientists often run experiments to distinguish competing theories. This
requires patience, rigor, and ingenuity - there is often a large space of
possible experiments one could run. But we need not comb this space by hand -
if we represent our theories as formal models and explicitly declare the space
of experiments, we can automate the search for good experiments, looking for
those with high expected information gain. Here, we present a general and
principled approach to experiment design based on probabilistic programming
languages (PPLs). PPLs offer a clean separation between declaring problems and
solving them, which means that the scientist can automate experiment design by
simply declaring her model and experiment spaces in the PPL without having to
worry about the details of calculating information gain. We demonstrate our
system in two case studies drawn from cognitive psychology, where we use it to
design optimal experiments in the domains of sequence prediction and
categorization. We find strong empirical validation that our automatically
designed experiments were indeed optimal. We conclude by discussing a number of
interesting questions for future research.
Large Angle based Skeleton Extraction for 3D Animation
Authors: Hugo Martin, Raphael Fernandez, Yong Khoo
Date: 2016-08-17 00:00:00

In this paper, we present a solution for arbitrary 3D character deformation
by investigating rotation angle of decomposition and preserving the mesh
topology structure. In computer graphics, skeleton extraction and
skeleton-driven animation is an active areas and gains increasing interests
from researchers. The accuracy is critical for realistic animation and related
applications. There have been extensive studies on skeleton based 3D
deformation. However for the scenarios of large angle rotation of different
body parts, it has been relatively less addressed by the state-of-the-art,
which often yield unsatisfactory results. Besides 3D animation problems, we
also notice for many 3D skeleton detection or tracking applications from a
video or depth streams, large angle rotation is also a critical factor in the
regression accuracy and robustness. We introduced a distortion metric function
to quantify the surface curviness before and after deformation, which is a
major clue for large angle rotation detection. The intensive experimental
results show that our method is suitable for 3D modeling, animation, skeleton
based tracking applications.
Simulation of an Optional Strategy in the Prisoner's Dilemma in Spatial and Non-spatial Environments
Authors: Marcos Cardinot, Maud Gibbons, Colm O'Riordan, Josephine Griffith
Date: 2016-08-17 00:00:00

This paper presents research comparing the effects of different environments
on the outcome of an extended Prisoner's Dilemma, in which agents have the
option to abstain from playing the game. We consider three different pure
strategies: cooperation, defection and abstinence. We adopt an evolutionary
game theoretic approach and consider two different environments: the first
which imposes no spatial constraints and the second in which agents are placed
on a lattice grid. We analyse the performance of the three strategies as we
vary the loner's payoff in both structured and unstructured environments.
Furthermore we also present the results of simulations which identify scenarios
in which cooperative clusters of agents emerge and persist in both
environments.
Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel K-Means
Authors: Twan van Laarhoven, Elena Marchiori
Date: 2016-01-21 00:00:00
Tasks: Community Detection, Local Community Detection

Local network community detection is the task of finding a single community
of nodes concentrated around few given seed nodes in a localized way.
Conductance is a popular objective function used in many algorithms for local
community detection. This paper studies a continuous relaxation of conductance.
We show that continuous optimization of this objective still leads to discrete
communities. We investigate the relation of conductance with weighted kernel
k-means for a single community, which leads to the introduction of a new
objective function, $\sigma$-conductance. Conductance is obtained by setting
$\sigma$ to $0$. Two algorithms, EMc and PGDc, are proposed to locally optimize
$\sigma$-conductance and automatically tune the parameter $\sigma$. They are
based on expectation maximization and projected gradient descent, respectively.
We prove locality and give performance guarantees for EMc and PGDc for a class
of dense and well separated communities centered around the seeds. Experiments
are conducted on networks with ground-truth communities, comparing to
state-of-the-art graph diffusion algorithms for conductance optimization. On
large graphs, results indicate that EMc and PGDc stay localized and produce
communities most similar to the ground, while graph diffusion algorithms
generate large communities of lower quality.
Zero-Shot Learning via Joint Latent Similarity Embedding
Authors: Ziming Zhang, Venkatesh Saligrama
Date: 2015-11-14 00:00:00
Proceeding: CVPR 2016 6
Tasks: Dictionary Learning, Retrieval, Zero-Shot Learning

Zero-shot recognition (ZSR) deals with the problem of predicting class labels
for target domain instances based on source domain side information (e.g.
attributes) of unseen classes. We formulate ZSR as a binary prediction problem.
Our resulting classifier is class-independent. It takes an arbitrary pair of
source and target domain instances as input and predicts whether or not they
come from the same class, i.e. whether there is a match. We model the posterior
probability of a match since it is a sufficient statistic and propose a latent
probabilistic model in this context. We develop a joint discriminative learning
framework based on dictionary learning to jointly learn the parameters of our
model for both domains, which ultimately leads to our class-independent
classifier. Many of the existing embedding methods can be viewed as special
cases of our probabilistic model. On ZSR our method shows 4.90\% improvement
over the state-of-the-art in accuracy averaged across four benchmark datasets.
We also adapt ZSR method for zero-shot retrieval and show 22.45\% improvement
accordingly in mean average precision (mAP).
Object Detection Networks on Convolutional Feature Maps
Authors: Shaoqing Ren, Kaiming He, Ross Girshick, Xiangyu Zhang, Jian Sun
Date: 2015-04-23 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object, object-detection, Object Detection

Most object detectors contain two important components: a feature extractor
and an object classifier. The feature extractor has rapidly evolved with
significant research efforts leading to better deep convolutional
architectures. The object classifier, however, has not received much attention
and many recent systems (like SPPnet and Fast/Faster R-CNN) use simple
multi-layer perceptrons. This paper demonstrates that carefully designing deep
networks for object classification is just as important. We experiment with
region-wise classifier networks that use shared, region-independent
convolutional features. We call them "Networks on Convolutional feature maps"
(NoCs). We discover that aside from deep feature maps, a deep and convolutional
per-region classifier is of particular importance for object detection, whereas
latest superior image classification models (such as ResNets and GoogLeNets) do
not directly lead to good detection accuracy without using such a per-region
classifier. We show by experiments that despite the effective ResNets and
Faster R-CNN systems, the design of NoCs is an essential element for the
1st-place winning entries in ImageNet and MS COCO challenges 2015.
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Authors: Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Date: 2016-08-17 00:00:00
Tasks: Decision Making, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Planning plays an important role in the broad class of decision theory.
Planning has drawn much attention in recent work in the robotics and sequential
decision making areas. Recently, Reinforcement Learning (RL), as an
agent-environment interaction problem, has brought further attention to
planning methods. Generally in RL, one can assume a generative model, e.g.
graphical models, for the environment, and then the task for the RL agent is to
learn the model parameters and find the optimal strategy based on these learnt
parameters. Based on environment behavior, the agent can assume various types
of generative models, e.g. Multi Armed Bandit for a static environment, or
Markov Decision Process (MDP) for a dynamic environment. The advantage of these
popular models is their simplicity, which results in tractable methods of
learning the parameters and finding the optimal policy. The drawback of these
models is again their simplicity: these models usually underfit and
underestimate the actual environment behavior. For example, in robotics, the
agent usually has noisy observations of the environment inner state and MDP is
not a suitable model.
  More complex models like Partially Observable Markov Decision Process (POMDP)
can compensate for this drawback. Fitting this model to the environment, where
the partial observation is given to the agent, generally gives dramatic
performance improvement, sometimes unbounded improvement, compared to MDP. In
general, finding the optimal policy for the POMDP model is computationally
intractable and fully non convex, even for the class of memoryless policies.
The open problem is to come up with a method to find an exact or an approximate
optimal stochastic memoryless policy for POMDP models.
Ensemble of Jointly Trained Deep Neural Network-Based Acoustic Models for Reverberant Speech Recognition
Authors: Jeehye Lee, Myungin Lee, Joon-Hyuk Chang
Date: 2016-08-17 00:00:00
Tasks: Distant Speech Recognition, speech-recognition, Speech Recognition

Distant speech recognition is a challenge, particularly due to the corruption
of speech signals by reverberation caused by large distances between the
speaker and microphone. In order to cope with a wide range of reverberations in
real-world situations, we present novel approaches for acoustic modeling
including an ensemble of deep neural networks (DNNs) and an ensemble of jointly
trained DNNs. First, multiple DNNs are established, each of which corresponds
to a different reverberation time 60 (RT60) in a setup step. Also, each model
in the ensemble of DNN acoustic models is further jointly trained, including
both feature mapping and acoustic modeling, where the feature mapping is
designed for the dereverberation as a front-end. In a testing phase, the two
most likely DNNs are chosen from the DNN ensemble using maximum a posteriori
(MAP) probabilities, computed in an online fashion by using maximum likelihood
(ML)-based blind RT60 estimation and then the posterior probability outputs
from two DNNs are combined using the ML-based weights as a simple average.
Extensive experiments demonstrate that the proposed approach leads to
substantial improvements in speech recognition accuracy over the conventional
DNN baseline systems under diverse reverberant conditions.
Mollifying Networks
Authors: Caglar Gulcehre, Marcin Moczulski, Francesco Visin, Yoshua Bengio
Date: 2016-08-17 00:00:00

The optimization of deep neural networks can be more challenging than
traditional convex optimization problems due to the highly non-convex nature of
the loss function, e.g. it can involve pathological landscapes such as
saddle-surfaces that can be difficult to escape for algorithms based on simple
gradient descent. In this paper, we attack the problem of optimization of
highly non-convex neural networks by starting with a smoothed -- or
\textit{mollified} -- objective function that gradually has a more non-convex
energy landscape during the training. Our proposition is inspired by the recent
studies in continuation methods: similar to curriculum methods, we begin
learning an easier (possibly convex) objective function and let it evolve
during the training, until it eventually goes back to being the original,
difficult to optimize, objective function. The complexity of the mollified
networks is controlled by a single hyperparameter which is annealed during the
training. We show improvements on various difficult optimization tasks and
establish a relationship with recent works on continuation methods for neural
networks and mollifiers.
A Three Spatial Dimension Wave Latent Force Model for Describing Excitation Sources and Electric Potentials Produced by Deep Brain Stimulation
Authors: Pablo A. Alvarado, Mauricio A. Álvarez, Álvaro A. Orozco
Date: 2016-08-17 00:00:00
Tasks: Gaussian Processes

Deep brain stimulation (DBS) is a surgical treatment for Parkinson's Disease.
Static models based on quasi-static approximation are common approaches for DBS
modeling. While this simplification has been validated for bioelectric sources,
its application to rapid stimulation pulses, which contain more high-frequency
power, may not be appropriate, as DBS therapeutic results depend on stimulus
parameters such as frequency and pulse width, which are related to time
variations of the electric field. We propose an alternative hybrid approach
based on probabilistic models and differential equations, by using Gaussian
processes and wave equation. Our model avoids quasi-static approximation,
moreover, it is able to describe dynamic behavior of DBS. Therefore, the
proposed model may be used to obtain a more realistic phenomenon description.
The proposed model can also solve inverse problems, i.e. to recover the
corresponding source of excitation, given electric potential distribution. The
electric potential produced by a time-varying source was predicted using
proposed model. For static sources, the electric potential produced by
different electrode configurations were modeled. Four different sources of
excitation were recovered by solving the inverse problem. We compare our
outcomes with the electric potential obtained by solving Poisson's equation
using the Finite Element Method (FEM). Our approach is able to take into
account time variations of the source and the produced field. Also, inverse
problem can be addressed using the proposed model. The electric potential
calculated with the proposed model is close to the potential obtained by
solving Poisson's equation using FEM.
Multi-Person Tracking by Multicut and Deep Matching
Authors: Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Bernt Schiele
Date: 2016-08-17 00:00:00

In [1], we proposed a graph-based formulation that links and clusters person
hypotheses over time by solving a minimum cost subgraph multicut problem. In
this paper, we modify and extend [1] in three ways: 1) We introduce a novel
local pairwise feature based on local appearance matching that is robust to
partial occlusion and camera motion. 2) We perform extensive experiments to
compare different pairwise potentials and to analyze the robustness of the
tracking formulation. 3) We consider a plain multicut problem and remove
outlying clusters from its solution. This allows us to employ an efficient
primal feasible optimization algorithm that is not applicable to the subgraph
multicut problem of [1]. Unlike the branch-and-cut algorithm used there, this
efficient algorithm used here is applicable to long videos and many detections.
Together with the novel feature, it eliminates the need for the intermediate
tracklet representation of [1]. We demonstrate the effectiveness of our overall
approach on the MOT16 benchmark [2], achieving state-of-art performance.
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation
Authors: Rakshith Shetty, Jorma Laaksonen
Date: 2016-08-17 00:00:00
Tasks: Caption Generation, Decoder, Diversity, Video Captioning

We present our submission to the Microsoft Video to Language Challenge of
generating short captions describing videos in the challenge dataset. Our model
is based on the encoder--decoder pipeline, popular in image and video
captioning systems. We propose to utilize two different kinds of video
features, one to capture the video content in terms of objects and attributes,
and the other to capture the motion and action information. Using these diverse
features we train models specializing in two separate input sub-domains. We
then train an evaluator model which is used to pick the best caption from the
pool of candidates generated by these domain expert models. We argue that this
approach is better suited for the current video captioning task, compared to
using a single model, due to the diversity in the dataset.
  Efficacy of our method is proven by the fact that it was rated best in MSR
Video to Language Challenge, as per human evaluation. Additionally, we were
ranked second in the automatic evaluation metrics based table.
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version
Authors: Marc Peter Deisenroth, Shakir Mohamed
Date: 2012-07-12 00:00:00
Proceeding: NeurIPS 2012
Tasks: Time Series, Time Series Analysis

Rich and complex time-series data, such as those generated from engineering
systems, financial markets, videos or neural recordings, are now a common
feature of modern data analysis. Explaining the phenomena underlying these
diverse data sets requires flexible and accurate models. In this paper, we
promote Gaussian process dynamical systems (GPDS) as a rich model class that is
appropriate for such analysis. In particular, we present a message passing
algorithm for approximate inference in GPDSs based on expectation propagation.
By posing inference as a general message passing problem, we iterate
forward-backward smoothing. Thus, we obtain more accurate posterior
distributions over latent structures, resulting in improved predictive
performance compared to state-of-the-art GPDS smoothers, which are special
cases of our general message passing algorithm. Hence, we provide a unifying
approach within which to contextualize message passing in GPDSs.
Reinforcement Learning algorithms for regret minimization in structured Markov Decision Processes
Authors: K J Prabuchandran, Tejas Bodas, Theja Tulabandhula
Date: 2016-08-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A recent goal in the Reinforcement Learning (RL) framework is to choose a
sequence of actions or a policy to maximize the reward collected or minimize
the regret incurred in a finite time horizon. For several RL problems in
operation research and optimal control, the optimal policy of the underlying
Markov Decision Process (MDP) is characterized by a known structure. The
current state of the art algorithms do not utilize this known structure of the
optimal policy while minimizing regret. In this work, we develop new RL
algorithms that exploit the structure of the optimal policy to minimize regret.
Numerical experiments on MDPs with structured optimal policies show that our
algorithms have better performance, are easy to implement, have a smaller
run-time and require less number of random number generations.
Geometry-aware Similarity Learning on SPD Manifolds for Visual Recognition
Authors: Zhiwu Huang, Ruiping Wang, Xianqiu Li, Wenxian Liu, Shiguang Shan, Luc van Gool, Xilin Chen
Date: 2016-08-17 00:00:00

Symmetric Positive Definite (SPD) matrices have been widely used for data
representation in many visual recognition tasks. The success mainly attributes
to learning discriminative SPD matrices with encoding the Riemannian geometry
of the underlying SPD manifold. In this paper, we propose a geometry-aware SPD
similarity learning (SPDSL) framework to learn discriminative SPD features by
directly pursuing manifold-manifold transformation matrix of column full-rank.
Specifically, by exploiting the Riemannian geometry of the manifold of
fixed-rank Positive Semidefinite (PSD) matrices, we present a new solution to
reduce optimizing over the space of column full-rank transformation matrices to
optimizing on the PSD manifold which has a well-established Riemannian
structure. Under this solution, we exploit a new supervised SPD similarity
learning technique to learn the transformation by regressing the similarities
of selected SPD data pairs to their ground-truth similarities on the target SPD
manifold. To optimize the proposed objective function, we further derive an
algorithm on the PSD manifold. Evaluations on three visual classification tasks
show the advantages of the proposed approach over the existing SPD-based
discriminant learning methods.