pwc-archive/papers-with-abstracts huggingface dataset:
Learning Unitary Operators with Help From u(n)
Authors: Stephanie L. Hyland, Gunnar Rätsch
Date: 2016-07-17 00:00:00

A major challenge in the training of recurrent neural networks is the
so-called vanishing or exploding gradient problem. The use of a norm-preserving
transition operator can address this issue, but parametrization is challenging.
In this work we focus on unitary operators and describe a parametrization using
the Lie algebra $\mathfrak{u}(n)$ associated with the Lie group $U(n)$ of $n
\times n$ unitary matrices. The exponential map provides a correspondence
between these spaces, and allows us to define a unitary matrix using $n^2$ real
coefficients relative to a basis of the Lie algebra. The parametrization is
closed under additive updates of these coefficients, and thus provides a simple
space in which to do gradient descent. We demonstrate the effectiveness of this
parametrization on the problem of learning arbitrary unitary operators,
comparing to several baselines and outperforming a recently-proposed
lower-dimensional parametrization. We additionally use our parametrization to
generalize a recently-proposed unitary recurrent neural network to arbitrary
unitary matrices, using it to solve standard long-memory tasks.
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
Authors: Junbo Zhang, Yu Zheng, Dekang Qi
Date: 2016-10-01 00:00:00
Tasks: Crowd Flows Prediction, Management

Forecasting the flow of crowds is of great importance to traffic management
and public safety, yet a very challenging task affected by many complex
factors, such as inter-region traffic, events and weather. In this paper, we
propose a deep-learning-based approach, called ST-ResNet, to collectively
forecast the in-flow and out-flow of crowds in each and every region through a
city. We design an end-to-end structure of ST-ResNet based on unique properties
of spatio-temporal data. More specifically, we employ the framework of the
residual neural networks to model the temporal closeness, period, and trend
properties of the crowd traffic, respectively. For each property, we design a
branch of residual convolutional units, each of which models the spatial
properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the
output of the three residual neural networks based on data, assigning different
weights to different branches and regions. The aggregation is further combined
with external factors, such as weather and day of the week, to predict the
final traffic of crowds in each and every region. We evaluate ST-ResNet based
on two types of crowd flows in Beijing and NYC, finding that its performance
exceeds six well-know methods.
Efficient Image Set Classification using Linear Regression based Image Reconstruction
Authors: Syed Afaq Ali Shah, Uzair Nadeem, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
Date: 2017-01-10 00:00:00
Tasks: Classification, General Classification, Image Reconstruction, regression

We propose a novel image set classification technique using linear regression
models. Downsampled gallery image sets are interpreted as subspaces of a high
dimensional space to avoid the computationally expensive training step. We
estimate regression models for each test image using the class specific gallery
subspaces. Images of the test set are then reconstructed using the regression
models. Based on the minimum reconstruction error between the reconstructed and
the original images, a weighted voting strategy is used to classify the test
set. We performed extensive evaluation on the benchmark UCSD/Honda, CMU Mobo
and YouTube Celebrity datasets for face classification, and ETH-80 dataset for
object classification. The results demonstrate that by using only a small
amount of training data, our technique achieved competitive classification
accuracy and superior computational speed compared with the state-of-the-art
methods.
Towards Smart Proof Search for Isabelle
Authors: Yutaka Nagashima
Date: 2017-01-10 00:00:00
Tasks: Position

Despite the recent progress in automatic theorem provers, proof engineers are
still suffering from the lack of powerful proof automation. In this position
paper we first report our proof strategy language based on a meta-tool
approach. Then, we propose an AI-based approach to drastically improve proof
automation for Isabelle, while identifying three major challenges we plan to
address for this objective.
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition
Authors: Abhinav Thanda, Shankar M Venkatesan
Date: 2017-01-10 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Multi-Task Learning, speech-recognition, Speech Recognition

Multi-task learning (MTL) involves the simultaneous training of two or more
related tasks over shared representations. In this work, we apply MTL to
audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn
a mapping between audio-visual fused features and frame labels obtained from
acoustic GMM/HMM model. This is combined with an auxiliary task which maps
visual features to frame labels obtained from a separate visual GMM/HMM model.
The MTL model is tested at various levels of babble noise and the results are
compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate
that MTL is especially useful at higher level of noise. Compared to base-line,
upto 7\% relative improvement in WER is reported at -3 SNR dB
Machine Learning of Linear Differential Equations using Gaussian Processes
Authors: Maziar Raissi, George Em. Karniadakis
Date: 2017-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Gaussian Processes

This work leverages recent advances in probabilistic machine learning to
discover conservation laws expressed by parametric linear equations. Such
equations involve, but are not limited to, ordinary and partial differential,
integro-differential, and fractional order operators. Here, Gaussian process
priors are modified according to the particular form of such operators and are
employed to infer parameters of the linear equations from scarce and possibly
noisy observations. Such observations may come from experiments or "black-box"
computer simulations.
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation
Authors: Varghese Alex, Kiran Vaidhya, Subramaniam Thirunavukkarasu, Chandrasekharan Kesavdas, Ganapathy Krishnamurthi
Date: 2016-11-26 00:00:00
Tasks: Denoising, Lesion Detection, Segmentation, Transfer Learning

The work presented explores the use of denoising autoencoders (DAE) for brain
lesion detection, segmentation and false positive reduction. Stacked denoising
autoencoders (SDAE) were pre-trained using a large number of unlabeled patient
volumes and fine tuned with patches drawn from a limited number of patients
(n=20, 40, 65). The results show negligible loss in performance even when SDAE
was fine tuned using 20 patients. Low grade glioma (LGG) segmentation was
achieved using a transfer learning approach wherein a network pre-trained with
High Grade Glioma (HGG) data was fine tuned using LGG image patches. The weakly
supervised SDAE (for HGG) and transfer learning based LGG network were also
shown to generalize well and provide good segmentation on unseen BraTS 2013 &
BraTS 2015 test data. An unique contribution includes a single layer DAE,
referred to as novelty detector(ND). ND was trained to accurately reconstruct
non-lesion patches using a mean squared error loss function. The reconstruction
error maps of test data were used to identify regions containing lesions. The
error maps were shown to assign unique error distributions to various
constituents of the glioma, enabling localization. The ND learns the non-lesion
brain accurately as it was also shown to provide good segmentation performance
on ischemic brain lesions in images from a different database.
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
Authors: Pooya Khorrami, Tom Le Paine, Kevin Brady, Charlie Dagli, Thomas S. Huang
Date: 2016-02-24 00:00:00
Tasks: Emotion Recognition

We consider the task of dimensional emotion recognition on video data using
deep learning. While several previous methods have shown the benefits of
training temporal neural network models such as recurrent neural networks
(RNNs) on hand-crafted features, few works have considered combining
convolutional neural networks (CNNs) with RNNs. In this work, we present a
system that performs emotion recognition on video data using both CNNs and
RNNs, and we also analyze how much each neural network component contributes to
the system's overall performance. We present our findings on videos from the
Audio/Visual+Emotion Challenge (AV+EC2015). In our experiments, we analyze the
effects of several hyperparameters on overall performance while also achieving
superior performance to the baseline and other competing methods.
Inertial Regularization and Selection (IRS): Sequential Regression in High-Dimension and Sparsity
Authors: Chitta Ranjan, Samaneh Ebrahimi, Kamran Paynabar
Date: 2016-10-23 00:00:00
Tasks: Demand Forecasting, Model Selection, regression, State Space Models

In this paper, we develop a new sequential regression modeling approach for
data streams. Data streams are commonly found around us, e.g in a retail
enterprise sales data is continuously collected every day. A demand forecasting
model is an important outcome from the data that needs to be continuously
updated with the new incoming data. The main challenge in such modeling arises
when there is a) high dimensional and sparsity, b) need for an adaptive use of
prior knowledge, and/or c) structural changes in the system. The proposed
approach addresses these challenges by incorporating an adaptive L1-penalty and
inertia terms in the loss function, and thus called Inertial Regularization and
Selection (IRS). The former term performs model selection to handle the first
challenge while the latter is shown to address the last two challenges. A
recursive estimation algorithm is developed, and shown to outperform the
commonly used state-space models, such as Kalman Filters, in experimental
studies and real data.
Understanding Neural Networks through Representation Erasure
Authors: Jiwei Li, Will Monroe, Dan Jurafsky
Date: 2016-12-24 00:00:00
Tasks: Reinforcement Learning, Sentence, Sentiment Analysis

While neural networks have been successfully applied to many natural language
processing tasks, they come at the cost of interpretability. In this paper, we
propose a general methodology to analyze and interpret decisions from a neural
model by observing the effects on the model of erasing various parts of the
representation, such as input word-vector dimensions, intermediate hidden
units, or input words. We present several approaches to analyzing the effects
of such erasure, from computing the relative difference in evaluation metrics,
to using reinforcement learning to erase the minimum set of input words in
order to flip a neural model's decision. In a comprehensive analysis of
multiple NLP tasks, including linguistic feature classification, sentence-level
sentiment analysis, and document level sentiment aspect prediction, we show
that the proposed methodology not only offers clear explanations about neural
model decisions, but also provides a way to conduct error analysis on neural
models.
Reinforcement Learning via Recurrent Convolutional Neural Networks
Authors: Tanmay Shankar, Santosha K. Dwivedy, Prithwijit Guha
Date: 2017-01-09 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning has enabled the learning of policies for complex
tasks in partially observable environments, without explicitly learning the
underlying model of the tasks. While such model-free methods achieve
considerable performance, they often ignore the structure of task. We present a
natural representation of to Reinforcement Learning (RL) problems using
Recurrent Convolutional Neural Networks (RCNNs), to better exploit this
inherent structure. We define 3 such RCNNs, whose forward passes execute an
efficient Value Iteration, propagate beliefs of state in partially observable
environments, and choose optimal actions respectively. Backpropagating
gradients through these RCNNs allows the system to explicitly learn the
Transition Model and Reward Function associated with the underlying MDP,
serving as an elegant alternative to classical model-based RL. We evaluate the
proposed algorithms in simulation, considering a robot planning problem. We
demonstrate the capability of our framework to reduce the cost of replanning,
learn accurate MDP models, and finally re-plan with learnt models to achieve
near-optimal policies.
Single-Molecule Protein Identification by Sub-Nanopore Sensors
Authors: Mikhail Kolmogorov, Eamonn Kennedy, Zhuxin Dong, Gregory Timp, Pavel Pevzner
Date: 2016-04-08 00:00:00

Recent advances in top-down mass spectrometry enabled identification of
intact proteins, but this technology still faces challenges. For example,
top-down mass spectrometry suffers from a lack of sensitivity since the ion
counts for a single fragmentation event are often low. In contrast, nanopore
technology is exquisitely sensitive to single intact molecules, but it has only
been successfully applied to DNA sequencing, so far. Here, we explore the
potential of sub-nanopores for single-molecule protein identification (SMPI)
and describe an algorithm for identification of the electrical current blockade
signal (nanospectrum) resulting from the translocation of a denaturated,
linearly charged protein through a sub-nanopore. The analysis of identification
p-values suggests that the current technology is already sufficient for
matching nanospectra against small protein databases, e.g., protein
identification in bacterial proteomes.
Measuring and Predicting Tag Importance for Image Retrieval
Authors: Shang-Wen Li, Sanjay Purushotham, Chen Chen, Yuzhuo Ren, C. -C. Jay Kuo
Date: 2016-02-28 00:00:00
Tasks: Image Retrieval, Prediction, Retrieval, Sentence, TAG

Textual data such as tags, sentence descriptions are combined with visual
cues to reduce the semantic gap for image retrieval applications in today's
Multimodal Image Retrieval (MIR) systems. However, all tags are treated as
equally important in these systems, which may result in misalignment between
visual and textual modalities during MIR training. This will further lead to
degenerated retrieval performance at query time. To address this issue, we
investigate the problem of tag importance prediction, where the goal is to
automatically predict the tag importance and use it in image retrieval. To
achieve this, we first propose a method to measure the relative importance of
object and scene tags from image sentence descriptions. Using this as the
ground truth, we present a tag importance prediction model to jointly exploit
visual, semantic and context cues. The Structural Support Vector Machine (SSVM)
formulation is adopted to ensure efficient training of the prediction model.
Then, the Canonical Correlation Analysis (CCA) is employed to learn the
relation between the image visual feature and tag importance to obtain robust
retrieval performance. Experimental results on three real-world datasets show a
significant performance improvement of the proposed MIR with Tag Importance
Prediction (MIR/TIP) system over other MIR systems.
The principle of cognitive action - Preliminary experimental analysis
Authors: Marco Gori, Marco Maggini, Alessandro Rossi
Date: 2017-01-09 00:00:00
Tasks: BIG-bench Machine Learning

In this document we shows a first implementation and some preliminary results
of a new theory, facing Machine Learning problems in the frameworks of
Classical Mechanics and Variational Calculus. We give a general formulation of
the problem and then we studies basic behaviors of the model on simple
practical implementations.
Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema
Authors: Patrick Verga, Arvind Neelakantan, Andrew McCallum
Date: 2016-06-18 00:00:00
Proceeding: EACL 2017 4
Tasks: Matrix Completion

Universal schema predicts the types of entities and relations in a knowledge
base (KB) by jointly embedding the union of all available schema types---not
only types from multiple structured databases (such as Freebase or Wikipedia
infoboxes), but also types expressed as textual patterns from raw text. This
prediction is typically modeled as a matrix completion problem, with one type
per column, and either one or two entities per row (in the case of entity types
or binary relation types, respectively). Factorizing this sparsely observed
matrix yields a learned vector embedding for each row and each column. In this
paper we explore the problem of making predictions for entities or entity-pairs
unseen at training time (and hence without a pre-learned row embedding). We
propose an approach having no per-row parameters at all; rather we produce a
row vector on the fly using a learned aggregation function of the vectors of
the observed columns for that row. We experiment with various aggregation
functions, including neural network attention models. Our approach can be
understood as a natural language database, in that questions about KB entities
are answered by attending to textual or database evidence. In experiments
predicting both relations and entity types, we demonstrate that despite having
an order of magnitude fewer parameters than traditional universal schema, we
can match the accuracy of the traditional model, and more importantly, we can
now make predictions about unseen rows with nearly the same accuracy as rows
available at training time.
Visualizing Residual Networks
Authors: Brian Chu, Daylen Yang, Ravi Tadinada
Date: 2017-01-09 00:00:00

Residual networks are the current state of the art on ImageNet. Similar work
in the direction of utilizing shortcut connections has been done extremely
recently with derivatives of residual networks and with highway networks. This
work potentially challenges our understanding that CNNs learn layers of local
features that are followed by increasingly global features. Through qualitative
visualization and empirical analysis, we explore the purpose that residual skip
connections serve. Our assessments show that the residual shortcut connections
force layers to refine features, as expected. We also provide alternate
visualizations that confirm that residual networks learn what is already
intuitively known about CNNs in general.
Son of Zorn's Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation
Authors: Carlos Castillo, Soham De, Xintong Han, Bharat Singh, Abhay Kumar Yadav, Tom Goldstein
Date: 2017-01-09 00:00:00
Tasks: LEMMA, Object, Semantic Segmentation, Style Transfer

Style transfer is an important task in which the style of a source image is
mapped onto that of a target image. The method is useful for synthesizing
derivative works of a particular artist or specific painting. This work
considers targeted style transfer, in which the style of a template image is
used to alter only part of a target image. For example, an artist may wish to
alter the style of only one particular object in a target image without
altering the object's general morphology or surroundings. This is useful, for
example, in augmented reality applications (such as the recently released
Pokemon GO), where one wants to alter the appearance of a single real-world
object in an image frame to make it appear as a cartoon. Most notably, the
rendering of real-world objects into cartoon characters has been used in a
number of films and television show, such as the upcoming series Son of Zorn.
We present a method for targeted style transfer that simultaneously segments
and stylizes single objects selected by the user. The method uses a Markov
random field model to smooth and anti-alias outlier pixels near object
boundaries, so that stylized objects naturally blend into their surroundings.
A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments
Authors: Omer Levy, Anders Søgaard, Yoav Goldberg
Date: 2016-08-18 00:00:00
Proceeding: EACL 2017 4
Tasks: Cross-Lingual Word Embeddings, Sentence, Word Embeddings

While cross-lingual word embeddings have been studied extensively in recent
years, the qualitative differences between the different algorithms remain
vague. We observe that whether or not an algorithm uses a particular feature
set (sentence IDs) accounts for a significant performance gap among these
algorithms. This feature set is also used by traditional alignment algorithms,
such as IBM Model-1, which demonstrate similar performance to state-of-the-art
embedding algorithms on a variety of benchmarks. Overall, we observe that
different algorithmic approaches for utilizing the sentence ID feature space
result in similar performance. This paper draws both empirical and theoretical
parallels between the embedding and alignment literature, and suggests that
adding additional sources of information, which go beyond the traditional
signal of bilingual sentence-aligned corpora, may substantially improve
cross-lingual word embeddings, and that future baselines should at least take
such features into account.
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing
Authors: Ehsan Jahangiri, Erdem Yoruk, Rene Vidal, Laurent Younes, Donald Geman
Date: 2017-01-09 00:00:00
Tasks: object-detection, Object Detection, Scene Parsing

Despite enormous progress in object detection and classification, the problem
of incorporating expected contextual relationships among object instances into
modern recognition systems remains a key challenge. In this work we propose
Information Pursuit, a Bayesian framework for scene parsing that combines prior
models for the geometry of the scene and the spatial arrangement of objects
instances with a data model for the output of high-level image classifiers
trained to answer specific questions about the scene. In the proposed
framework, the scene interpretation is progressively refined as evidence
accumulates from the answers to a sequence of questions. At each step, we
choose the question to maximize the mutual information between the new answer
and the full interpretation given the current evidence obtained from previous
inquiries. We also propose a method for learning the parameters of the model
from synthesized, annotated scenes obtained by top-down sampling from an
easy-to-learn generative scene model. Finally, we introduce a database of
annotated indoor scenes of dining room tables, which we use to evaluate the
proposed approach.
Stratification of patient trajectories using covariate latent variable models
Authors: Kieran R. Campbell, Christopher Yau
Date: 2016-10-27 00:00:00
Tasks: Variational Inference

Standard models assign disease progression to discrete categories or stages
based on well-characterized clinical markers. However, such a system is
potentially at odds with our understanding of the underlying biology, which in
highly complex systems may support a (near-)continuous evolution of disease
from inception to terminal state. To learn such a continuous disease score one
could infer a latent variable from dynamic "omics" data such as RNA-seq that
correlates with an outcome of interest such as survival time. However, such
analyses may be confounded by additional data such as clinical covariates
measured in electronic health records (EHRs). As a solution to this we
introduce covariate latent variable models, a novel type of latent variable
model that learns a low-dimensional data representation in the presence of two
(asymmetric) views of the same data source. We apply our model to TCGA
colorectal cancer RNA-seq data and demonstrate how incorporating
microsatellite-instability (MSI) status as an external covariate allows us to
identify genes that stratify patients on an immune-response trajectory.
Finally, we propose an extension termed Covariate Gaussian Process Latent
Variable Models for learning nonparametric, nonlinear representations. An R
package implementing variational inference for covariate latent variable models
is available at http://github.com/kieranrcampbell/clvm.