pwc-archive/papers-with-abstracts huggingface dataset:
Transfer learning from synthetic to real images using variational autoencoders for robotic applications
Authors: Tadanobu Inoue, Subhajit Chaudhury, Giovanni De Magistris, Sakyasingha Dasgupta
Date: 2017-09-20 00:00:00
Tasks: Object, Position, Transfer Learning

Robotic learning in simulation environments provides a faster, more scalable,
and safer training methodology than learning directly with physical robots.
Also, synthesizing images in a simulation environment for collecting
large-scale image data is easy, whereas capturing camera images in the real
world is time consuming and expensive. However, learning from only synthetic
images may not achieve the desired performance in real environments due to the
gap between synthetic and real images. We thus propose a method that transfers
learned capability of detecting object position from a simulation environment
to the real world. Our method enables us to use only a very limited dataset of
real images while leveraging a large dataset of synthetic images using multiple
variational autoencoders. It detects object positions 6 to 7 times more
precisely than the baseline of directly learning from the dataset of the real
images. Object position estimation under varying environmental conditions forms
one of the underlying requirement for standard robotic manipulation tasks. We
show that the proposed method performs robustly in different lighting
conditions or with other distractor objects present for this requirement. Using
this detected object position, we transfer pick-and-place or reaching tasks
learned in a simulation environment to an actual physical robot without
re-training.
SegFlow: Joint Learning for Video Object Segmentation and Optical Flow
Authors: Jingchun Cheng, Yi-Hsuan Tsai, Shengjin Wang, Ming-Hsuan Yang
Date: 2017-09-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Segmentation, Object, Optical Flow Estimation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation, Visual Object Tracking

This paper proposes an end-to-end trainable network, SegFlow, for
simultaneously predicting pixel-wise object segmentation and optical flow in
videos. The proposed SegFlow has two branches where useful information of
object segmentation and optical flow is propagated bidirectionally in a unified
framework. The segmentation branch is based on a fully convolutional network,
which has been proved effective in image segmentation task, and the optical
flow branch takes advantage of the FlowNet model. The unified framework is
trained iteratively offline to learn a generic notion, and fine-tuned online
for specific objects. Extensive experiments on both the video object
segmentation and optical flow datasets demonstrate that introducing optical
flow improves the performance of segmentation and vice versa, against the
state-of-the-art algorithms.
On the convergence of gradient-like flows with noisy gradient input
Authors: Panayotis Mertikopoulos, Mathias Staudigl
Date: 2016-11-21 00:00:00

In view of solving convex optimization problems with noisy gradient input, we
analyze the asymptotic behavior of gradient-like flows under stochastic
disturbances. Specifically, we focus on the widely studied class of mirror
descent schemes for convex programs with compact feasible regions, and we
examine the dynamics' convergence and concentration properties in the presence
of noise. In the vanishing noise limit, we show that the dynamics converge to
the solution set of the underlying problem (a.s.). Otherwise, when the noise is
persistent, we show that the dynamics are concentrated around interior
solutions in the long run, and they converge to boundary solutions that are
sufficiently "sharp". Finally, we show that a suitably rectified variant of the
method converges irrespective of the magnitude of the noise (or the structure
of the underlying convex program), and we derive an explicit estimate for its
rate of convergence.
Gaussian Processes with Context-Supported Priors for Active Object Localization
Authors: Anthony D. Rhodes, Jordan Witte, Melanie Mitchell, Bruno Jedynak
Date: 2017-03-25 00:00:00
Tasks: Active Object Localization, Bayesian Optimization, Gaussian Processes, Object, Object Localization, regression

We devise an algorithm using a Bayesian optimization framework in conjunction
with contextual visual data for the efficient localization of objects in still
images. Recent research has demonstrated substantial progress in object
localization and related tasks for computer vision. However, many current
state-of-the-art object localization procedures still suffer from inaccuracy
and inefficiency, in addition to failing to provide a principled and
interpretable system amenable to high-level vision tasks. We address these
issues with the current research.
  Our method encompasses an active search procedure that uses contextual data
to generate initial bounding-box proposals for a target object. We train a
convolutional neural network to approximate an offset distance from the target
object. Next, we use a Gaussian Process to model this offset response signal
over the search space of the target. We then employ a Bayesian active search
for accurate localization of the target.
  In experiments, we compare our approach to a state-of-theart bounding-box
regression method for a challenging pedestrian localization task. Our method
exhibits a substantial improvement over this baseline regression method.
Local Directional Relation Pattern for Unconstrained and Robust Face Retrieval
Authors: Shiv Ram Dubey
Date: 2017-09-20 00:00:00
Tasks: Face Recognition, Image Retrieval, Relation, Retrieval

Face recognition is still a very demanding area of research. This problem becomes more challenging in unconstrained environment and in the presence of several variations like pose, illumination, expression, etc. Local descriptors are widely used for this task. The most of the existing local descriptors consider only few immediate local neighbors and not able to utilize the wider local information to make the descriptor more discriminative. The wider local information based descriptors mainly suffer due to the increased dimensionality. In this paper, this problem is solved by encoding the relationship among directional neighbors in an efficient manner. The relationship between the center pixel and the encoded directional neighbors is utilized further to form the proposed local directional relation pattern (LDRP). The descriptor is inherently uniform illumination invariant. The multi-scale mechanism is also adapted to further boost the discriminative ability of the descriptor. The proposed descriptor is evaluated under the image retrieval framework over face databases. Very challenging databases like PaSC, LFW, PubFig, ESSEX, FERET, AT&T, and FaceScrub are used to test the discriminative ability and robustness of LDRP descriptor. Results are also compared with the recent state-of-the-art face descriptors such as LBP, LTP, LDP, LDN, LVP, DCP, LDGP and LGHP. Very promising performance is observed using the proposed descriptor over very appealing face databases as compared to the existing face descriptors. The proposed LDRP descriptor also outperforms the pre-trained ImageNet CNN models over large-scale FaceScrub face dataset. Moreover, it also outperforms the deep learning based DLib face descriptor in many scenarios.
Identifying Restaurant Features via Sentiment Analysis on Yelp Reviews
Authors: Boya Yu, Jiaxu Zhou, Yi Zhang, Yunong Cao
Date: 2017-09-20 00:00:00
Tasks: Common Sense Reasoning, Sentiment Analysis

Many people use Yelp to find a good restaurant. Nonetheless, with only an
overall rating for each restaurant, Yelp offers not enough information for
independently judging its various aspects such as environment, service or
flavor. In this paper, we introduced a machine learning based method to
characterize such aspects for particular types of restaurants. The main
approach used in this paper is to use a support vector machine (SVM) model to
decipher the sentiment tendency of each review from word frequency. Word scores
generated from the SVM models are further processed into a polarity index
indicating the significance of each word for special types of restaurant.
Customers overall tend to express more sentiment regarding service. As for the
distinction between different cuisines, results that match the common sense are
obtained: Japanese cuisines are usually fresh, some French cuisines are
overpriced while Italian Restaurants are often famous for their pizzas.
E$^2$BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network
Authors: Xiaobin Liu, Shiliang Zhang, Tiejun Huang, Qi Tian
Date: 2017-09-18 00:00:00
Tasks: Image Retrieval, Quantization, Retrieval

Traditional Bag-of-visual Words (BoWs) model is commonly generated with many
steps including local feature extraction, codebook generation, and feature
quantization, etc. Those steps are relatively independent with each other and
are hard to be jointly optimized. Moreover, the dependency on hand-crafted
local feature makes BoWs model not effective in conveying high-level semantics.
These issues largely hinder the performance of BoWs model in large-scale image
applications. To conquer these issues, we propose an End-to-End BoWs
(E$^2$BoWs) model based on Deep Convolutional Neural Network (DCNN). Our model
takes an image as input, then identifies and separates the semantic objects in
it, and finally outputs the visual words with high semantic discriminative
power. Specifically, our model firstly generates Semantic Feature Maps (SFMs)
corresponding to different object categories through convolutional layers, then
introduces Bag-of-Words Layers (BoWL) to generate visual words for each
individual feature map. We also introduce a novel learning algorithm to
reinforce the sparsity of the generated E$^2$BoWs model, which further ensures
the time and memory efficiency. We evaluate the proposed E$^2$BoWs model on
several image search datasets including CIFAR-10, CIFAR-100, MIRFLICKR-25K and
NUS-WIDE. Experimental results show that our method achieves promising accuracy
and efficiency compared with recent deep learning based retrieval works.
Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis
Authors: Nikolay Doudchenko, Guido W. Imbens
Date: 2016-10-25 00:00:00
Tasks: regression

In a seminal paper Abadie, Diamond, and Hainmueller [2010] (ADH), see also
Abadie and Gardeazabal [2003], Abadie et al. [2014], develop the synthetic
control procedure for estimating the effect of a treatment, in the presence of
a single treated unit and a number of control units, with pre-treatment
outcomes observed for all units. The method constructs a set of weights such
that selected covariates and pre-treatment outcomes of the treated unit are
approximately matched by a weighted average of control units (the synthetic
control). The weights are restricted to be nonnegative and sum to one, which is
important because it allows the procedure to obtain unique weights even when
the number of lagged outcomes is modest relative to the number of control
units, a common setting in applications. In the current paper we propose a
generalization that allows the weights to be negative, and their sum to differ
from one, and that allows for a permanent additive difference between the
treated unit and the controls, similar to difference-in-difference procedures.
The weights directly minimize the distance between the lagged outcomes for the
treated and the control units, using regularization methods to deal with a
potentially large number of possible control units.
Adaptive Low-Rank Kernel Subspace Clustering
Authors: Pan Ji, Ian Reid, Ravi Garg, Hongdong Li, Mathieu Salzmann
Date: 2017-07-17 00:00:00
Tasks: Clustering, Image Clustering, Motion Segmentation

In this paper, we present a kernel subspace clustering method that can handle
non-linear models. In contrast to recent kernel subspace clustering methods
which use predefined kernels, we propose to learn a low-rank kernel matrix,
with which mapped data in feature space are not only low-rank but also
self-expressive. In this manner, the low-dimensional subspace structures of the
(implicitly) mapped data are retained and manifested in the high-dimensional
feature space. We evaluate the proposed method extensively on both motion
segmentation and image clustering benchmarks, and obtain superior results,
outperforming the kernel subspace clustering method that uses standard
kernels[Patel 2014] and other state-of-the-art linear subspace clustering
methods.
A Voting-Based System for Ethical Decision Making
Authors: Ritesh Noothigattu, Snehalkumar 'Neil' S. Gaikwad, Edmond Awad, Sohan Dsouza, Iyad Rahwan, Pradeep Ravikumar, Ariel D. Procaccia
Date: 2017-09-20 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

We present a general approach to automating ethical decisions, drawing on
machine learning and computational social choice. In a nutshell, we propose to
learn a model of societal preferences, and, when faced with a specific ethical
dilemma at runtime, efficiently aggregate those preferences to identify a
desirable choice. We provide a concrete algorithm that instantiates our
approach; some of its crucial steps are informed by a new theory of
swap-dominance efficient voting rules. Finally, we implement and evaluate a
system for ethical decision making in the autonomous vehicle domain, using
preference data collected from 1.3 million people through the Moral Machine
website.
Property Testing in High Dimensional Ising models
Authors: Matey Neykov, Han Liu
Date: 2017-09-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper explores the information-theoretic limitations of graph property
testing in zero-field Ising models. Instead of learning the entire graph
structure, sometimes testing a basic graph property such as connectivity, cycle
presence or maximum clique size is a more relevant and attainable objective.
Since property testing is more fundamental than graph recovery, any necessary
conditions for property testing imply corresponding conditions for graph
recovery, while custom property tests can be statistically and/or
computationally more efficient than graph recovery based algorithms.
Understanding the statistical complexity of property testing requires the
distinction of ferromagnetic (i.e., positive interactions only) and general
Ising models. Using combinatorial constructs such as graph packing and strong
monotonicity, we characterize how target properties affect the corresponding
minimax upper and lower bounds within the realm of ferromagnets. On the other
hand, by studying the detection of an antiferromagnetic (i.e., negative
interactions only) Curie-Weiss model buried in Rademacher noise, we show that
property testing is strictly more challenging over general Ising models. In
terms of methodological development, we propose two types of correlation based
tests: computationally efficient screening for ferromagnets, and score type
tests for general models, including a fast cycle presence test. Our correlation
screening tests match the information-theoretic bounds for property testing in
ferromagnets.
Deep Lattice Networks and Partial Monotonic Functions
Authors: Seungil You, David Ding, Kevin Canini, Jan Pfeifer, Maya Gupta
Date: 2017-09-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: General Classification, regression

We propose learning deep models that are monotonic with respect to a
user-specified set of inputs by alternating layers of linear embeddings,
ensembles of lattices, and calibrators (piecewise linear functions), with
appropriate constraints for monotonicity, and jointly training the resulting
network. We implement the layers and projections with new computational graph
nodes in TensorFlow and use the ADAM optimizer and batched stochastic
gradients. Experiments on benchmark and real-world datasets show that six-layer
monotonic deep lattice networks achieve state-of-the art performance for
classification and regression with monotonicity guarantees.
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words
Authors: Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi
Date: 2017-09-19 00:00:00
Tasks: General Classification, Relation Classification, Semantic Similarity, Semantic Textual Similarity, text-classification, Text Classification, Word Embeddings

Distributed word embeddings have shown superior performances in numerous
Natural Language Processing (NLP) tasks. However, their performances vary
significantly across different tasks, implying that the word embeddings learnt
by those methods capture complementary aspects of lexical semantics. Therefore,
we believe that it is important to combine the existing word embeddings to
produce more accurate and complete \emph{meta-embeddings} of words. For this
purpose, we propose an unsupervised locally linear meta-embedding learning
method that takes pre-trained word embeddings as the input, and produces more
accurate meta embeddings. Unlike previously proposed meta-embedding learning
methods that learn a global projection over all words in a vocabulary, our
proposed method is sensitive to the differences in local neighbourhoods of the
individual source word embeddings. Moreover, we show that vector concatenation,
a previously proposed highly competitive baseline approach for integrating word
embeddings, can be derived as a special case of the proposed method.
Experimental results on semantic similarity, word analogy, relation
classification, and short-text classification tasks show that our
meta-embeddings to significantly outperform prior methods in several benchmark
datasets, establishing a new state of the art for meta-embeddings.
A textual transform of multivariate time-series for prognostics
Authors: Abhay Harpale, Abhishek Srivastav
Date: 2017-09-19 00:00:00
Tasks: Time Series, Time Series Analysis

Prognostics or early detection of incipient faults is an important industrial
challenge for condition-based and preventive maintenance. Physics-based
approaches to modeling fault progression are infeasible due to multiple
interacting components, uncontrolled environmental factors and observability
constraints. Moreover, such approaches to prognostics do not generalize to new
domains. Consequently, domain-agnostic data-driven machine learning approaches
to prognostics are desirable. Damage progression is a path-dependent process
and explicitly modeling the temporal patterns is critical for accurate
estimation of both the current damage state and its progression leading to
total failure. In this paper, we present a novel data-driven approach to
prognostics that employs a novel textual representation of multivariate
temporal sensor observations for predicting the future health state of the
monitored equipment early in its life. This representation enables us to
utilize well-understood concepts from text-mining for modeling, prediction and
understanding distress patterns in a domain agnostic way. The approach has been
deployed and successfully tested on large scale multivariate time-series data
from commercial aircraft engines. We report experiments on well-known publicly
available benchmark datasets and simulation datasets. The proposed approach is
shown to be superior in terms of prediction accuracy, lead time to prediction
and interpretability.
Beyond Skip Connections: Top-Down Modulation for Object Detection
Authors: Abhinav Shrivastava, Rahul Sukthankar, Jitendra Malik, Abhinav Gupta
Date: 2016-12-20 00:00:00
Tasks: Object, object-detection, Object Detection

In recent years, we have seen tremendous progress in the field of object
detection. Most of the recent improvements have been achieved by targeting
deeper feedforward networks. However, many hard object categories such as
bottle, remote, etc. require representation of fine details and not just
coarse, semantic representations. But most of these fine details are lost in
the early convolutional layers. What we need is a way to incorporate finer
details from lower layers into the detection architecture. Skip connections
have been proposed to combine high-level and low-level features, but we argue
that selecting the right features from low-level requires top-down contextual
information. Inspired by the human visual pathway, in this paper we propose
top-down modulations as a way to incorporate fine details into the detection
framework. Our approach supplements the standard bottom-up, feedforward ConvNet
with a top-down modulation (TDM) network, connected using lateral connections.
These connections are responsible for the modulation of lower layer filters,
and the top-down network handles the selection and integration of contextual
information and low-level features. The proposed TDM architecture provides a
significant boost on the COCO testdev benchmark, achieving 28.6 AP for VGG16,
35.2 AP for ResNet101, and 37.3 for InceptionResNetv2 network, without any
bells and whistles (e.g., multi-scale, iterative box refinement, etc.).
WOAH: Preliminaries to Zero-shot Ontology Learning for Conversational Agents
Authors: Gonzalo Estrán Buyo
Date: 2017-09-15 00:00:00

The present paper presents the Weighted Ontology Approximation Heuristic
(WOAH), a novel zero-shot approach to ontology estimation for conversational
agents development environments. This methodology extracts verbs and nouns
separately from data by distilling the dependencies obtained and applying
similarity and sparsity metrics to generate an ontology estimation configurable
in terms of the level of generalization.
Using Human Brain Activity to Guide Machine Learning
Authors: Ruth Fong, Walter Scheirer, David Cox
Date: 2017-03-16 00:00:00
Tasks: BIG-bench Machine Learning, Hybrid Machine Learning, Object Recognition

Machine learning is a field of computer science that builds algorithms that
learn. In many cases, machine learning algorithms are used to recreate a human
ability like adding a caption to a photo, driving a car, or playing a game.
While the human brain has long served as a source of inspiration for machine
learning, little effort has been made to directly use data collected from
working brains as a guide for machine learning algorithms. Here we demonstrate
a new paradigm of "neurally-weighted" machine learning, which takes fMRI
measurements of human brain activity from subjects viewing images, and infuses
these data into the training process of an object recognition learning
algorithm to make it more consistent with the human brain. After training,
these neurally-weighted classifiers are able to classify images without
requiring any additional neural data. We show that our neural-weighting
approach can lead to large performance gains when used with traditional machine
vision features, as well as to significant improvements with already
high-performing convolutional neural network features. The effectiveness of
this approach points to a path forward for a new class of hybrid machine
learning algorithms which take both inspiration and direct constraints from
neuronal data.