pwc-archive/papers-with-abstracts huggingface dataset:
DeepSetNet: Predicting Sets with Deep Neural Networks
Authors: S. Hamid Rezatofighi, Vijay Kumar B G, Anton Milan, Ehsan Abbasnejad, Anthony Dick, Ian Reid
Date: 2016-11-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: image-classification, Image Classification, Object Counting, object-detection, Object Detection, Pedestrian Detection

This paper addresses the task of set prediction using deep learning. This is
important because the output of many computer vision tasks, including image
tagging and object detection, are naturally expressed as sets of entities
rather than vectors. As opposed to a vector, the size of a set is not fixed in
advance, and it is invariant to the ordering of entities within it. We define a
likelihood for a set distribution and learn its parameters using a deep neural
network. We also derive a loss for predicting a discrete distribution
corresponding to set cardinality. Set prediction is demonstrated on the problem
of multi-class image classification. Moreover, we show that the proposed
cardinality loss can also trivially be applied to the tasks of object counting
and pedestrian detection. Our approach outperforms existing methods in all
three cases on standard datasets.
Random Sampling for Fast Face Sketch Synthesis
Authors: Nannan Wang, Xinbo Gao, Jie Li
Date: 2017-01-08 00:00:00
Tasks: Face Hallucination, Face Sketch Synthesis, Hallucination

Exemplar-based face sketch synthesis plays an important role in both digital
entertainment and law enforcement. It generally consists of two parts: neighbor
selection and reconstruction weight representation. The most time-consuming or
main computation complexity for exemplar-based face sketch synthesis methods
lies in the neighbor selection process. State-of-the-art face sketch synthesis
methods perform neighbor selection online in a data-driven manner by $K$
nearest neighbor ($K$-NN) searching. Actually, the online search increases the
time consuming for synthesis. Moreover, since these methods need to traverse
the whole training dataset for neighbor selection, the computational complexity
increases with the scale of the training database and hence these methods have
limited scalability. In this paper, we proposed a simple but effective offline
random sampling in place of online $K$-NN search to improve the synthesis
efficiency. Extensive experiments on public face sketch databases demonstrate
the superiority of the proposed method in comparison to state-of-the-art
methods, in terms of both synthesis quality and time consumption. The proposed
method could be extended to other heterogeneous face image transformation
problems such as face hallucination. We release the source codes of our
proposed methods and the evaluation metrics for future study online:
http://www.ihitworld.com/RSLCR.html.
N-gram and Neural Language Models for Discriminating Similar Languages
Authors: Andre Cianflone, Leila Kosseim
Date: 2017-08-11 00:00:00
Proceeding: WS 2016 12
Tasks: BIG-bench Machine Learning

This paper describes our submission (named clac) to the 2016 Discriminating
Similar Languages (DSL) shared task. We participated in the closed Sub-task 1
(Set A) with two separate machine learning techniques. The first approach is a
character based Convolution Neural Network with a bidirectional long short term
memory (BiLSTM) layer (CLSTM), which achieved an accuracy of 78.45% with
minimal tuning. The second approach is a character-based n-gram model. This
last approach achieved an accuracy of 88.45% which is close to the accuracy of
89.38% achieved by the best submission, and allowed us to rank #7 overall.
DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image
Authors: Andrey Kurenkov, Jingwei Ji, Animesh Garg, Viraj Mehta, JunYoung Gwak, Christopher Choy, Silvio Savarese
Date: 2017-08-11 00:00:00
Tasks: 3D Reconstruction, 3D Shape Reconstruction, Form, Retrieval

3D reconstruction from a single image is a key problem in multiple
applications ranging from robotic manipulation to augmented reality. Prior
methods have tackled this problem through generative models which predict 3D
reconstructions as voxels or point clouds. However, these methods can be
computationally expensive and miss fine details. We introduce a new
differentiable layer for 3D data deformation and use it in DeformNet to learn a
model for 3D reconstruction-through-deformation. DeformNet takes an image
input, searches the nearest shape template from a database, and deforms the
template to match the query image. We evaluate our approach on the ShapeNet
dataset and show that - (a) the Free-Form Deformation layer is a powerful new
building block for Deep Learning models that manipulate 3D data (b) DeformNet
uses this FFD layer combined with shape retrieval for smooth and
detail-preserving 3D reconstruction of qualitatively plausible point clouds
with respect to a single query image (c) compared to other state-of-the-art 3D
reconstruction methods, DeformNet quantitatively matches or outperforms their
benchmarks by significant margins. For more information, visit:
https://deformnet-site.github.io/DeformNet-website/ .
GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery
Authors: Seungkyun Hong, Seongchan Kim, Minsu Joh, Sa-kwang Song
Date: 2017-08-11 00:00:00
Tasks: Prediction, regression

Advances in remote sensing technologies have made it possible to use
high-resolution visual data for weather observation and forecasting tasks. We
propose the use of multi-layer neural networks for understanding complex
atmospheric dynamics based on multichannel satellite images. The capability of
our model was evaluated by using a linear regression task for single typhoon
coordinates prediction. A specific combination of models and different
activation policies enabled us to obtain an interesting prediction result in
the northeastern hemisphere (ENH).
Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation
Authors: Xinghao Chen, Guijin Wang, Hengkai Guo, Cairong Zhang
Date: 2017-08-11 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

Hand pose estimation from a single depth image is an essential topic in
computer vision and human computer interaction. Despite recent advancements in
this area promoted by convolutional neural network, accurate hand pose
estimation is still a challenging problem. In this paper we propose a Pose
guided structured Region Ensemble Network (Pose-REN) to boost the performance
of hand pose estimation. The proposed method extracts regions from the feature
maps of convolutional neural network under the guide of an initially estimated
pose, generating more optimal and representative features for hand pose
estimation. The extracted feature regions are then integrated hierarchically
according to the topology of hand joints by employing tree-structured fully
connections. A refined estimation of hand pose is directly regressed by the
proposed network and the final hand pose is obtained by utilizing an iterative
cascaded method. Comprehensive experiments on public hand pose datasets
demonstrate that our proposed method outperforms state-of-the-art algorithms.
Machine Teaching: A New Paradigm for Building Machine Learning Systems
Authors: Patrice Y. Simard, Saleema Amershi, David M. Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, John Wernsing
Date: 2017-07-21 00:00:00
Tasks: BIG-bench Machine Learning

The current processes for building machine learning systems require
practitioners with deep knowledge of machine learning. This significantly
limits the number of machine learning systems that can be created and has led
to a mismatch between the demand for machine learning systems and the ability
for organizations to build them. We believe that in order to meet this growing
demand for machine learning systems we must significantly increase the number
of individuals that can teach machines. We postulate that we can achieve this
goal by making the process of teaching machines easy, fast and above all,
universally accessible.
  While machine learning focuses on creating new algorithms and improving the
accuracy of "learners", the machine teaching discipline focuses on the efficacy
of the "teachers". Machine teaching as a discipline is a paradigm shift that
follows and extends principles of software engineering and programming
languages. We put a strong emphasis on the teacher and the teacher's
interaction with data, as well as crucial components such as techniques and
design principles of interaction and visualization.
  In this paper, we present our position regarding the discipline of machine
teaching and articulate fundamental machine teaching principles. We also
describe how, by decoupling knowledge about machine learning algorithms from
the process of teaching, we can accelerate innovation and empower millions of
new uses for machine learning models.
Towards Understanding Sparse Filtering: A Theoretical Perspective
Authors: Fabio Massimo Zennaro, Ke Chen
Date: 2016-03-29 00:00:00

In this paper we present a theoretical analysis to understand sparse
filtering, a recent and effective algorithm for unsupervised learning. The aim
of this research is not to show whether or how well sparse filtering works, but
to understand why and when sparse filtering does work. We provide a thorough
theoretical analysis of sparse filtering and its properties, and further offer
an experimental validation of the main outcomes of our theoretical analysis. We
show that sparse filtering works by explicitly maximizing the entropy of the
learned representation through the maximization of the proxy of sparsity, and
by implicitly preserving mutual information between original and learned
representations through the constraint of preserving a structure of the data,
specifically the structure defined by relations of neighborhoodness under the
cosine distance. Furthermore, we empirically validate our theoretical results
with artificial and real data sets, and we apply our theoretical understanding
to explain the success of sparse filtering on real-world problems. Our work
provides a strong theoretical basis for understanding sparse filtering: it
highlights assumptions and conditions for success behind this feature
distribution learning algorithm, and provides insights for developing new
feature distribution learning algorithms.
Jumping across biomedical contexts using compressive data fusion
Authors: Marinka Zitnik, Blaz Zupan
Date: 2017-08-10 00:00:00

Motivation: The rapid growth of diverse biological data allows us to consider
interactions between a variety of objects, such as genes, chemicals, molecular
signatures, diseases, pathways and environmental exposures. Often, any pair of
objects--such as a gene and a disease--can be related in different ways, for
example, directly via gene-disease associations or indirectly via functional
annotations, chemicals and pathways. Different ways of relating these objects
carry different semantic meanings. However, traditional methods disregard these
semantics and thus cannot fully exploit their value in data modeling.
  Results: We present Medusa, an approach to detect size-k modules of objects
that, taken together, appear most significant to another set of objects. Medusa
operates on large-scale collections of heterogeneous data sets and explicitly
distinguishes between diverse data semantics. It advances research along two
dimensions: it builds on collective matrix factorization to derive different
semantics, and it formulates the growing of the modules as a submodular
optimization program. Medusa is flexible in choosing or combining semantic
meanings and provides theoretical guarantees about detection quality. In a
systematic study on 310 complex diseases, we show the effectiveness of Medusa
in associating genes with diseases and detecting disease modules. We
demonstrate that in predicting gene-disease associations Medusa compares
favorably to methods that ignore diverse semantic meanings. We find that the
utility of different semantics depends on disease categories and that, overall,
Medusa recovers disease modules more accurately when combining different
semantics.
Making Sense of Word Embeddings
Authors: Maria Pelevina, Nikolay Arefyev, Chris Biemann, Alexander Panchenko
Date: 2017-08-10 00:00:00
Proceeding: WS 2016 8
Tasks: Clustering, Word Embeddings

We present a simple yet effective approach for learning word sense
embeddings. In contrast to existing techniques, which either directly learn
sense representations from corpora or rely on sense inventories from lexical
resources, our approach can induce a sense inventory from existing word
embeddings via clustering of ego-networks of related words. An integrated WSD
mechanism enables labeling of words in context with learned sense vectors,
which gives rise to downstream applications. Experiments show that the
performance of our method is comparable to state-of-the-art unsupervised WSD
systems.
A probabilistic model for the numerical solution of initial value problems
Authors: Michael Schober, Simo Särkkä, Philipp Hennig
Date: 2016-10-17 00:00:00

Like many numerical methods, solvers for initial value problems (IVPs) on
ordinary differential equations estimate an analytically intractable quantity,
using the results of tractable computations as inputs. This structure is
closely connected to the notion of inference on latent variables in statistics.
We describe a class of algorithms that formulate the solution to an IVP as
inference on a latent path that is a draw from a Gaussian process probability
measure (or equivalently, the solution of a linear stochastic differential
equation). We then show that certain members of this class are connected
precisely to generalized linear methods for ODEs, a number of Runge--Kutta
methods, and Nordsieck methods. This probabilistic formulation of classic
methods is valuable in two ways: analytically, it highlights implicit prior
assumptions favoring certain approximate solutions to the IVP over others, and
gives a precise meaning to the old observation that these methods act like
filters. Practically, it endows the classic solvers with `docking points' for
notions of uncertainty and prior information about the initial value, the value
of the ODE itself, and the solution of the problem.
Joint Multi-Person Pose Estimation and Semantic Part Segmentation
Authors: Fangting Xia, Peng Wang, Xianjie Chen, Alan Yuille
Date: 2017-08-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Human Detection, Multi-Person Pose Estimation, Pose Estimation

Human pose estimation and semantic part segmentation are two complementary
tasks in computer vision. In this paper, we propose to solve the two tasks
jointly for natural multi-person images, in which the estimated pose provides
object-level shape prior to regularize part segments while the part-level
segments constrain the variation of pose locations. Specifically, we first
train two fully convolutional neural networks (FCNs), namely Pose FCN and Part
FCN, to provide initial estimation of pose joint potential and semantic part
potential. Then, to refine pose joint location, the two types of potentials are
fused with a fully-connected conditional random field (FCRF), where a novel
segment-joint smoothness term is used to encourage semantic and spatial
consistency between parts and joints. To refine part segments, the refined pose
and the original part potential are integrated through a Part FCN, where the
skeleton feature from pose serves as additional regularization cues for part
segments. Finally, to reduce the complexity of the FCRF, we induce human
detection boxes and infer the graph inside each box, making the inference forty
times faster.
  Since there's no dataset that contains both part segments and pose labels, we
extend the PASCAL VOC part dataset with human pose joints and perform extensive
experiments to compare our method against several most recent strategies. We
show that on this dataset our algorithm surpasses competing methods by a large
margin in both tasks.
Topical Behavior Prediction from Massive Logs
Authors: Shih-Chieh Su
Date: 2017-08-10 00:00:00
Tasks: Prediction

In this paper, we study the topical behavior in a large scale. We use the
network logs where each entry contains the entity ID, the timestamp, and the
meta data about the activity. Both the temporal and the spatial relationships
of the behavior are explored with the deep learning architectures combing the
recurrent neural network (RNN) and the convolutional neural network (CNN). To
make the behavioral data appropriate for the spatial learning in the CNN, we
propose several reduction steps to form the topical metrics and to place them
homogeneously like pixels in the images. The experimental result shows both
temporal and spatial gains when compared against a multilayer perceptron (MLP)
network. A new learning framework called the spatially connected convolutional
networks (SCCN) is introduced to predict the topical metrics more efficiently.
An Empirical Study on Writer Identification & Verification from Intra-variable Individual Handwriting
Authors: Chandranath Adak, Bidyut. B. Chaudhuri, Michael Blumenstein
Date: 2017-08-10 00:00:00
Tasks: Data Augmentation

The handwriting of an individual may vary substantially with factors such as
mood, time, space, writing speed, writing medium and tool, writing topic, etc.
It becomes challenging to perform automated writer verification/identification
on a particular set of handwritten patterns (e.g., speedy handwriting) of a
person, especially when the system is trained using a different set of writing
patterns (e.g., normal speed) of that same person. However, it would be
interesting to experimentally analyze if there exists any implicit
characteristic of individuality which is insensitive to high intra-variable
handwriting. In this paper, we study some handcrafted features and auto-derived
features extracted from intra-variable writing. Here, we work on writer
identification/verification from offline Bengali handwriting of high
intra-variability. To this end, we use various models mainly based on
handcrafted features with SVM (Support Vector Machine) and features
auto-derived by the convolutional network. For experimentation, we have
generated two handwritten databases from two different sets of 100 writers and
enlarged the dataset by a data-augmentation technique. We have obtained some
interesting results.
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Authors: Riashat Islam, Peter Henderson, Maziar Gomrokchi, Doina Precup
Date: 2017-08-10 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient methods in reinforcement learning have become increasingly
prevalent for state-of-the-art performance in continuous control tasks. Novel
methods typically benchmark against a few key algorithms such as deep
deterministic policy gradients and trust region policy optimization. As such,
it is important to present and use consistent baselines experiments. However,
this can be difficult due to general variance in the algorithms,
hyper-parameter tuning, and environment stochasticity. We investigate and
discuss: the significance of hyper-parameters in policy gradients for
continuous control, general variance in the algorithms, and reproducibility of
reported results. We provide guidelines on reporting novel results as
comparisons against baseline methods such that future researchers can make
informed decisions when investigating novel methods.
Technical Problems With "Programmable self-assembly in a thousand-robot swarm"
Authors: Muaz A. Niazi
Date: 2017-08-10 00:00:00

Rubenstein et al. present an interesting system of programmable
self-assembled structure formation using 1000 Kilobot robots. The paper claims
to advance work in artificial swarms similar to capabilities of natural systems
besides being highly robust. However, the system lacks in terms of matching
motility and complex shapes with holes, thereby limiting practical similarity
to self-assembly in living systems.
Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese
Authors: Yuanzhi Ke, Masafumi Hagiwara
Date: 2017-08-10 00:00:00
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

The character vocabulary can be very large in non-alphabetic languages such
as Chinese and Japanese, which makes neural network models huge to process such
languages. We explored a model for sentiment classification that takes the
embeddings of the radicals of the Chinese characters, i.e, hanzi of Chinese and
kanji of Japanese. Our model is composed of a CNN word feature encoder and a
bi-directional RNN document feature encoder. The results achieved are on par
with the character embedding-based models, and close to the state-of-the-art
word embedding-based models, with 90% smaller vocabulary, and at least 13% and
80% fewer parameters than the character embedding-based models and word
embedding-based models respectively. The results suggest that the radical
embedding-based approach is cost-effective for machine learning on Chinese and
Japanese.
A System for Accessible Artificial Intelligence
Authors: Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore
Date: 2017-05-01 00:00:00
Tasks: BIG-bench Machine Learning

While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.
How should we evaluate supervised hashing?
Authors: Alexandre Sablayrolles, Matthijs Douze, Hervé Jégou, Nicolas Usunier
Date: 2016-09-21 00:00:00
Tasks: General Classification, Retrieval, Transfer Learning

Hashing produces compact representations for documents, to perform tasks like
classification or retrieval based on these short codes. When hashing is
supervised, the codes are trained using labels on the training data. This paper
first shows that the evaluation protocols used in the literature for supervised
hashing are not satisfactory: we show that a trivial solution that encodes the
output of a classifier significantly outperforms existing supervised or
semi-supervised methods, while using much shorter codes. We then propose two
alternative protocols for supervised hashing: one based on retrieval on a
disjoint set of classes, and another based on transfer learning to new classes.
We provide two baseline methods for image-related tasks to assess the
performance of (semi-)supervised hashing: without coding and with unsupervised
codes. These baselines give a lower- and upper-bound on the performance of a
supervised hashing scheme.