pwc-archive/papers-with-abstracts huggingface dataset:
Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets
Authors: Charles B. Delahunt, Jeffrey A. Riffell, J. Nathan Kutz
Date: 2018-02-08 00:00:00

The insect olfactory system, which includes the antennal lobe (AL), mushroom
body (MB), and ancillary structures, is a relatively simple neural system
capable of learning. Its structural features, which are widespread in
biological neural systems, process olfactory stimuli through a cascade of
networks where large dimension shifts occur from stage to stage and where
sparsity and randomness play a critical role in coding. Learning is partly
enabled by a neuromodulatory reward mechanism of octopamine stimulation of the
AL, whose increased activity induces rewiring of the MB through Hebbian
plasticity. Enforced sparsity in the MB focuses Hebbian growth on neurons that
are the most important for the representation of the learned odor. Based upon
current biophysical knowledge, we have constructed an end-to-end computational
model of the Manduca sexta moth olfactory system which includes the interaction
of the AL and MB under octopamine stimulation. Our model is able to robustly
learn new odors, and our simulations of integrate-and-fire neurons match the
statistical features of in-vivo firing rate data. From a biological
perspective, the model provides a valuable tool for examining the role of
neuromodulators, like octopamine, in learning, and gives insight into critical
interactions between sparsity, Hebbian growth, and stimulation during learning.
Our simulations also inform predictions about structural details of the
olfactory system that are not currently well-characterized. From a machine
learning perspective, the model yields bio-inspired mechanisms that are
potentially useful in constructing neural nets for rapid learning from very few
samples. These mechanisms include high-noise layers, sparse layers as noise
filters, and a biologically-plausible optimization method to train the network
based on octopamine stimulation, sparse layers, and Hebbian growth.
On Encoding Temporal Evolution for Real-time Action Prediction
Authors: Fahimeh Rezazadegan, Sareh Shirazi, Mahsa Baktashmotlagh, Larry S. Davis
Date: 2017-09-22 00:00:00

Anticipating future actions is a key component of intelligence, specifically
when it applies to real-time systems, such as robots or autonomous cars. While
recent works have addressed prediction of raw RGB pixel values, we focus on
anticipating the motion evolution in future video frames. To this end, we
construct dynamic images (DIs) by summarising moving pixels through a sequence
of future frames. We train a convolutional LSTMs to predict the next DIs based
on an unsupervised learning process, and then recognise the activity associated
with the predicted DI. We demonstrate the effectiveness of our approach on 3
benchmark action datasets showing that despite running on videos with complex
activities, our approach is able to anticipate the next human action with high
accuracy and obtain better results than the state-of-the-art methods.
An Ontology Based Modeling Framework for Design of Educational Technologies
Authors: Sridhar Chimalakonda, Kesav V. Nori
Date: 2018-02-07 00:00:00

Despite rapid progress, most of the educational technologies today lack a
strong instructional design knowledge basis leading to questionable quality of
instruction. In addition, a major challenge is to customize these educational
technologies for a wide range of instructional designs. Ontologies are one of
the pertinent mechanisms to represent instructional design in the literature.
However, existing approaches do not support modeling of flexible instructional
designs. To address this problem, in this paper, we propose an ontology based
framework for systematic modeling of different aspects of instructional design
knowledge based on domain patterns. As part of the framework, we present
ontologies for modeling goals, instructional processes and instructional
materials. We demonstrate the ontology framework by presenting instances of the
ontology for the large scale case study of adult literacy in India (287 million
learners spread across 22 Indian Languages), which requires creation of 1000
similar but varied eLearning Systems based on flexible instructional designs.
The implemented framework is available at http://rice.iiit.ac.in and is
transferred to National Literacy Mission of Government of India. This framework
could be used for modeling instructional design knowledge of systems for
skills, school education and beyond.
Efficient collective swimming by harnessing vortices through deep reinforcement learning
Authors: Siddhartha Verma, Guido Novati, Petros Koumoutsakos
Date: 2018-02-07 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Fish in schooling formations navigate complex flow-fields replete with
mechanical energy in the vortex wakes of their companions. Their schooling
behaviour has been associated with evolutionary advantages including collective
energy savings. How fish harvest energy from their complex fluid environment
and the underlying physical mechanisms governing energy-extraction during
collective swimming, is still unknown. Here we show that fish can improve their
sustained propulsive efficiency by actively following, and judiciously
intercepting, vortices in the wake of other swimmers. This swimming strategy
leads to collective energy-savings and is revealed through the first ever
combination of deep reinforcement learning with high-fidelity flow simulations.
We find that a `smart-swimmer' can adapt its position and body deformation to
synchronise with the momentum of the oncoming vortices, improving its average
swimming-efficiency at no cost to the leader. The results show that fish may
harvest energy deposited in vortices produced by their peers, and support the
conjecture that swimming in formation is energetically advantageous. Moreover,
this study demonstrates that deep reinforcement learning can produce navigation
algorithms for complex flow-fields, with promising implications for energy
savings in autonomous robotic swarms.
Combining Multiple Cues for Visual Madlibs Question Answering
Authors: Tatiana Tommasi, Arun Mallya, Bryan Plummer, Svetlana Lazebnik, Alexander C. Berg, Tamara L. Berg
Date: 2016-11-01 00:00:00
Tasks: Attribute, General Classification, Multiple-choice, Question Answering, Scene Recognition

This paper presents an approach for answering fill-in-the-blank multiple
choice questions from the Visual Madlibs dataset. Instead of generic and
commonly used representations trained on the ImageNet classification task, our
approach employs a combination of networks trained for specialized tasks such
as scene recognition, person activity classification, and attribute prediction.
We also present a method for localizing phrases from candidate answers in order
to provide spatial support for feature extraction. We map each of these
features, together with candidate answers, to a joint embedding space through
normalized canonical correlation analysis (nCCA). Finally, we solve an
optimization problem to learn to combine scores from nCCA models trained on
multiple cues to select the best answer. Extensive experimental results show a
significant improvement over the previous state of the art and confirm that
answering questions from a wide range of types benefits from examining a
variety of image cues and carefully choosing the spatial support for feature
extraction.
Estimating Mutual Information for Discrete-Continuous Mixtures
Authors: Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Date: 2017-09-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Clustering, Mutual Information Estimation

Estimating mutual information from observed samples is a basic primitive,
useful in several machine learning tasks including correlation mining,
information bottleneck clustering, learning a Chow-Liu tree, and conditional
independence testing in (causal) graphical models. While mutual information is
a well-defined quantity in general probability spaces, existing estimators can
only handle two special cases of purely discrete or purely continuous pairs of
random variables. The main challenge is that these methods first estimate the
(differential) entropies of X, Y and the pair (X;Y) and add them up with
appropriate signs to get an estimate of the mutual information. These
3H-estimators cannot be applied in general mixture spaces, where entropy is not
well-defined. In this paper, we design a novel estimator for mutual information
of discrete-continuous mixtures. We prove that the proposed estimator is
consistent. We provide numerical experiments suggesting superiority of the
proposed estimator compared to other heuristics of adding small continuous
noise to all the samples and applying standard estimators tailored for purely
continuous variables, and quantizing the samples and applying standard
estimators tailored for purely discrete variables. This significantly widens
the applicability of mutual information estimation in real-world applications,
where some variables are discrete, some continuous, and others are a mixture
between continuous and discrete components.
Reasoning in a Hierarchical System with Missing Group Size Information
Authors: Subhash Kak
Date: 2018-02-07 00:00:00

The paper analyzes the problem of judgments or preferences subsequent to
initial analysis by autonomous agents in a hierarchical system where the higher
level agents does not have access to group size information. We propose methods
that reduce instances of preference reversal of the kind encountered in
Simpson's paradox.
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images
Authors: Yi Zhu, Xueqing Deng, Shawn Newsam
Date: 2018-02-07 00:00:00
Tasks: General Classification

We perform fine-grained land use mapping at the city scale using ground-level
images. Mapping land use is considerably more difficult than mapping land cover
and is generally not possible using overhead imagery as it requires close-up
views and seeing inside buildings. We postulate that the growing collections of
georeferenced, ground-level images suggest an alternate approach to this
geographic knowledge discovery problem. We develop a general framework that
uses Flickr images to map 45 different land-use classes for the City of San
Francisco. Individual images are classified using a novel convolutional neural
network containing two streams, one for recognizing objects and another for
recognizing scenes. This network is trained in an end-to-end manner directly on
the labeled training images. We propose several strategies to overcome the
noisiness of our user-generated data including search-based training set
augmentation and online adaptive training. We derive a ground truth map of San
Francisco in order to evaluate our method. We demonstrate the effectiveness of
our approach through geo-visualization and quantitative analysis. Our framework
achieves over 29% recall at the individual land parcel level which represents a
strong baseline for the challenging 45-way land use classification problem
especially given the noisiness of the image data.
Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition
Authors: Xuesong Yang, Kartik Audhkhasi, Andrew Rosenberg, Samuel Thomas, Bhuvana Ramabhadran, Mark Hasegawa-Johnson
Date: 2018-02-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The performance of automatic speech recognition systems degrades with
increasing mismatch between the training and testing scenarios. Differences in
speaker accents are a significant source of such mismatch. The traditional
approach to deal with multiple accents involves pooling data from several
accents during training and building a single model in multi-task fashion,
where tasks correspond to individual accents. In this paper, we explore an
alternate model where we jointly learn an accent classifier and a multi-task
acoustic model. Experiments on the American English Wall Street Journal and
British English Cambridge corpora demonstrate that our joint model outperforms
the strong multi-task acoustic model baseline. We obtain a 5.94% relative
improvement in word error rate on British English, and 9.47% relative
improvement on American English. This illustrates that jointly modeling with
accent information improves acoustic model performance.
Expressive power of recurrent neural networks
Authors: Valentin Khrulkov, Alexander Novikov, Ivan Oseledets
Date: 2017-11-02 00:00:00
Proceeding: ICLR 2018 1
Tasks: Tensor Decomposition

Deep neural networks are surprisingly efficient at solving practical tasks,
but the theory behind this phenomenon is only starting to catch up with the
practice. Numerous works show that depth is the key to this efficiency. A
certain class of deep convolutional networks -- namely those that correspond to
the Hierarchical Tucker (HT) tensor decomposition -- has been proven to have
exponentially higher expressive power than shallow networks. I.e. a shallow
network of exponential width is required to realize the same score function as
computed by the deep architecture. In this paper, we prove the expressive power
theorem (an exponential lower bound on the width of the equivalent shallow
network) for a class of recurrent neural networks -- ones that correspond to
the Tensor Train (TT) decomposition. This means that even processing an image
patch by patch with an RNN can be exponentially more efficient than a (shallow)
convolutional network with one hidden layer. Using theoretical results on the
relation between the tensor decompositions we compare expressive powers of the
HT- and TT-Networks. We also implement the recurrent TT-Networks and provide
numerical evidence of their expressivity.
Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks
Authors: Nathalia Nascimento, Carlos Lucena, Paulo Alencar, Donald Cowan
Date: 2018-02-04 00:00:00
Tasks: BIG-bench Machine Learning, Management

Several papers have recently contained reports on applying machine learning
(ML) to the automation of software engineering (SE) tasks, such as project
management, modeling and development. However, there appear to be no approaches
comparing how software engineers fare against machine-learning algorithms as
applied to specific software development tasks. Such a comparison is essential
to gain insight into which tasks are better performed by humans and which by
machine learning and how cooperative work or human-in-the-loop processes can be
implemented more effectively. In this paper, we present an empirical study that
compares how software engineers and machine-learning algorithms perform and
reuse tasks. The empirical study involves the synthesis of the control
structure of an autonomous streetlight application. Our approach consists of
four steps. First, we solved the problem using machine learning to determine
specific performance and reuse tasks. Second, we asked software engineers with
different domain knowledge levels to provide a solution to the same tasks.
Third, we compared how software engineers fare against machine-learning
algorithms when accomplishing the performance and reuse tasks based on criteria
such as energy consumption and safety. Finally, we analyzed the results to
understand which tasks are better performed by either humans or algorithms so
that they can work together more effectively. Such an understanding and the
resulting human-in-the-loop approaches, which take into account the strengths
and weaknesses of humans and machine-learning algorithms, are fundamental not
only to provide a basis for cooperative work in support of software
engineering, but also, in other areas.
Gradient conjugate priors and multi-layer neural networks
Authors: Pavel Gurevich, Hannes Stuke
Date: 2018-02-07 00:00:00

The paper deals with learning probability distributions of observed data by
artificial neural networks. We suggest a so-called gradient conjugate prior
(GCP) update appropriate for neural networks, which is a modification of the
classical Bayesian update for conjugate priors. We establish a connection
between the gradient conjugate prior update and the maximization of the
log-likelihood of the predictive distribution. Unlike for the Bayesian neural
networks, we use deterministic weights of neural networks, but rather assume
that the ground truth distribution is normal with unknown mean and variance and
learn by the neural networks the parameters of a prior (normal-gamma
distribution) for these unknown mean and variance. The update of the parameters
is done, using the gradient that, at each step, directs towards minimizing the
Kullback--Leibler divergence from the prior to the posterior distribution (both
being normal-gamma). We obtain a corresponding dynamical system for the prior's
parameters and analyze its properties. In particular, we study the limiting
behavior of all the prior's parameters and show how it differs from the case of
the classical full Bayesian update. The results are validated on synthetic and
real world data sets.
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate
Authors: Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro
Date: 2016-11-01 00:00:00

Recently, there has been growing interest in developing optimization methods
for solving large-scale machine learning problems. Most of these problems boil
down to the problem of minimizing an average of a finite set of smooth and
strongly convex functions where the number of functions $n$ is large. Gradient
descent method (GD) is successful in minimizing convex problems at a fast
linear rate; however, it is not applicable to the considered large-scale
optimization setting because of the high computational complexity. Incremental
methods resolve this drawback of gradient methods by replacing the required
gradient for the descent direction with an incremental gradient approximation.
They operate by evaluating one gradient per iteration and executing the average
of the $n$ available gradients as a gradient approximate. Although, incremental
methods reduce the computational cost of GD, their convergence rates do not
justify their advantage relative to GD in terms of the total number of gradient
evaluations until convergence. In this paper, we introduce a Double Incremental
Aggregated Gradient method (DIAG) that computes the gradient of only one
function at each iteration, which is chosen based on a cyclic scheme, and uses
the aggregated average gradient of all the functions to approximate the full
gradient. The iterates of the proposed DIAG method uses averages of both
iterates and gradients in oppose to classic incremental methods that utilize
gradient averages but do not utilize iterate averages. We prove that not only
the proposed DIAG method converges linearly to the optimal solution, but also
its linear convergence factor justifies the advantage of incremental methods on
GD. In particular, we prove that the worst case performance of DIAG is better
than the worst case performance of GD.
Tight Lower Bounds for Locally Differentially Private Selection
Authors: Jonathan Ullman
Date: 2018-02-07 00:00:00
Tasks: PAC learning

We prove a tight lower bound (up to constant factors) on the sample complexity of any non-interactive local differentially private protocol for optimizing a linear function over the simplex. This lower bound also implies a tight lower bound (again, up to constant factors) on the sample complexity of any non-interactive local differentially private protocol implementing the exponential mechanism. These results reveal that any local protocol for these problems has exponentially worse dependence on the dimension than corresponding algorithms in the central model. Previously, Kasiviswanathan et al. (FOCS 2008) proved an exponential separation between local and central model algorithms for PAC learning the class of parity functions. In contrast, our lower bound are quantitatively tight, apply to a simple and natural class of linear optimization problems, and our techniques are arguably simpler.
Spatially adaptive image compression using a tiled deep network
Authors: David Minnen, George Toderici, Michele Covell, Troy Chinen, Nick Johnston, Joel Shor, Sung Jin Hwang, Damien Vincent, Saurabh Singh
Date: 2018-02-07 00:00:00
Tasks: Image Compression

Deep neural networks represent a powerful class of function approximators
that can learn to compress and reconstruct images. Existing image compression
algorithms based on neural networks learn quantized representations with a
constant spatial bit rate across each image. While entropy coding introduces
some spatial variation, traditional codecs have benefited significantly by
explicitly adapting the bit rate based on local image complexity and visual
saliency. This paper introduces an algorithm that combines deep neural networks
with quality-sensitive bit rate adaptation using a tiled network. We
demonstrate the importance of spatial context prediction and show improved
quantitative (PSNR) and qualitative (subjective rater assessment) results
compared to a non-adaptive baseline and a recently published image compression
model based on fully-convolutional neural networks.
$α$-Variational Inference with Statistical Guarantees
Authors: Yun Yang, Debdeep Pati, Anirban Bhattacharya
Date: 2017-10-09 00:00:00
Tasks: Variational Inference

We propose a family of variational approximations to Bayesian posterior
distributions, called $\alpha$-VB, with provable statistical guarantees. The
standard variational approximation is a special case of $\alpha$-VB with
$\alpha=1$. When $\alpha \in(0,1]$, a novel class of variational inequalities
are developed for linking the Bayes risk under the variational approximation to
the objective function in the variational optimization problem, implying that
maximizing the evidence lower bound in variational inference has the effect of
minimizing the Bayes risk within the variational density family. Operating in a
frequentist setup, the variational inequalities imply that point estimates
constructed from the $\alpha$-VB procedure converge at an optimal rate to the
true parameter in a wide range of problems. We illustrate our general theory
with a number of examples, including the mean-field variational approximation
to (low)-high-dimensional Bayesian linear regression with spike and slab
priors, mixture of Gaussian models, latent Dirichlet allocation, and (mixture
of) Gaussian variational approximation in regular parametric models.
Recognition of Acoustic Events Using Masked Conditional Neural Networks
Authors: Fady Medhat, David Chesmore, John Robinson
Date: 2018-02-07 00:00:00

Automatic feature extraction using neural networks has accomplished
remarkable success for images, but for sound recognition, these models are
usually modified to fit the nature of the multi-dimensional temporal
representation of the audio signal in spectrograms. This may not efficiently
harness the time-frequency representation of the signal. The ConditionaL Neural
Network (CLNN) takes into consideration the interrelation between the temporal
frames, and the Masked ConditionaL Neural Network (MCLNN) extends upon the CLNN
by forcing a systematic sparseness over the network's weights using a binary
mask. The masking allows the network to learn about frequency bands rather than
bins, mimicking a filterbank used in signal transformations such as MFCC.
Additionally, the Mask is designed to consider various combinations of
features, which automates the feature hand-crafting process. We applied the
MCLNN for the Environmental Sound Recognition problem using the Urbansound8k,
YorNoise, ESC-10 and ESC-50 datasets. The MCLNN have achieved competitive
performance compared to state-of-the-art Convolutional Neural Networks and
hand-crafted attempts.