pwc-archive/papers-with-abstracts huggingface dataset:
The high-conductance state enables neural sampling in networks of LIF neurons
Authors: Mihai A. Petrovici, Ilja Bytschok, Johannes Bill, Johannes Schemmel, Karlheinz Meier
Date: 2016-01-05 00:00:00
Tasks: Bayesian Inference

The apparent stochasticity of in-vivo neural circuits has long been
hypothesized to represent a signature of ongoing stochastic inference in the
brain. More recently, a theoretical framework for neural sampling has been
proposed, which explains how sample-based inference can be performed by
networks of spiking neurons. One particular requirement of this approach is
that the neural response function closely follows a logistic curve.
  Analytical approaches to calculating neural response functions have been the
subject of many theoretical studies. In order to make the problem tractable,
particular assumptions regarding the neural or synaptic parameters are usually
made. However, biologically significant activity regimes exist which are not
covered by these approaches: Under strong synaptic bombardment, as is often the
case in cortex, the neuron is shifted into a high-conductance state (HCS)
characterized by a small membrane time constant. In this regime, synaptic time
constants and refractory periods dominate membrane dynamics.
  The core idea of our approach is to separately consider two different "modes"
of spiking dynamics: burst spiking and transient quiescence, in which the
neuron does not spike for longer periods. We treat the former by propagating
the PDF of the effective membrane potential from spike to spike within a burst,
while using a diffusion approximation for the latter. We find that our
prediction of the neural response function closely matches simulation data.
Moreover, in the HCS scenario, we show that the neural response function
becomes symmetric and can be well approximated by a logistic function, thereby
providing the correct dynamics in order to perform neural sampling. We hereby
provide not only a normative framework for Bayesian inference in cortex, but
also powerful applications of low-power, accelerated neuromorphic systems to
relevant machine learning tasks.
Joint learning of ontology and semantic parser from text
Authors: Janez Starc, Dunja Mladenić
Date: 2016-01-05 00:00:00
Tasks: Semantic Parsing

Semantic parsing methods are used for capturing and representing semantic
meaning of text. Meaning representation capturing all the concepts in the text
may not always be available or may not be sufficiently complete. Ontologies
provide a structured and reasoning-capable way to model the content of a
collection of texts. In this work, we present a novel approach to joint
learning of ontology and semantic parser from text. The method is based on
semi-automatic induction of a context-free grammar from semantically annotated
text. The grammar parses the text into semantic trees. Both, the grammar and
the semantic trees are used to learn the ontology on several levels -- classes,
instances, taxonomic and non-taxonomic relations. The approach was evaluated on
the first sentences of Wikipedia pages describing people.
MMSE of probabilistic low-rank matrix estimation: Universality with respect to the output channel
Authors: Thibault Lesieur, Florent Krzakala, Lenka Zdeborová
Date: 2015-07-14 00:00:00
Tasks: Stochastic Block Model

This paper considers probabilistic estimation of a low-rank matrix from
non-linear element-wise measurements of its elements. We derive the
corresponding approximate message passing (AMP) algorithm and its state
evolution. Relying on non-rigorous but standard assumptions motivated by
statistical physics, we characterize the minimum mean squared error (MMSE)
achievable information theoretically and with the AMP algorithm. Unlike in
related problems of linear estimation, in the present setting the MMSE depends
on the output channel only trough a single parameter - its Fisher information.
We illustrate this striking finding by analysis of submatrix localization, and
of detection of communities hidden in a dense stochastic block model. For this
example we locate the computational and statistical boundaries that are not
equal for rank larger than four.
Programming in logic without logic programming
Authors: Robert Kowalski, Fariba Sadri
Date: 2016-01-04 00:00:00

In previous work, we proposed a logic-based framework in which computation is
the execution of actions in an attempt to make reactive rules of the form if
antecedent then consequent true in a canonical model of a logic program
determined by an initial state, sequence of events, and the resulting sequence
of subsequent states. In this model-theoretic semantics, reactive rules are the
driving force, and logic programs play only a supporting role.
  In the canonical model, states, actions and other events are represented with
timestamps. But in the operational semantics, for the sake of efficiency,
timestamps are omitted and only the current state is maintained. State
transitions are performed reactively by executing actions to make the
consequents of rules true whenever the antecedents become true. This
operational semantics is sound, but incomplete. It cannot make reactive rules
true by preventing their antecedents from becoming true, or by proactively
making their consequents true before their antecedents become true.
  In this paper, we characterize the notion of reactive model, and prove that
the operational semantics can generate all and only such models. In order to
focus on the main issues, we omit the logic programming component of the
framework.
On the convergence of cycle detection for navigational reinforcement learning
Authors: Tom J. Ameloot, Jan Van den Bussche
Date: 2015-11-27 00:00:00
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a reinforcement learning framework where agents have to navigate
from start states to goal states. We prove convergence of a cycle-detection
learning algorithm on a class of tasks that we call reducible. Reducible tasks
have an acyclic solution. We also syntactically characterize the form of the
final policy. This characterization can be used to precisely detect the
convergence point in a simulation. Our result demonstrates that even simple
algorithms can be successful in learning a large class of nontrivial tasks. In
addition, our framework is elementary in the sense that we only use basic
concepts to formally prove convergence.
Gamifying Video Object Segmentation
Authors: Simone Palazzo, Concetto Spampinato, Daniela Giordano
Date: 2016-01-05 00:00:00
Tasks: Interactive Video Object Segmentation, Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

Video object segmentation can be considered as one of the most challenging
computer vision problems. Indeed, so far, no existing solution is able to
effectively deal with the peculiarities of real-world videos, especially in
cases of articulated motion and object occlusions; limitations that appear more
evident when we compare their performance with the human one. However, manually
segmenting objects in videos is largely impractical as it requires a lot of
human time and concentration. To address this problem, in this paper we propose
an interactive video object segmentation method, which exploits, on one hand,
the capability of humans to identify correctly objects in visual scenes, and on
the other hand, the collective human brainpower to solve challenging tasks. In
particular, our method relies on a web game to collect human inputs on object
locations, followed by an accurate segmentation phase achieved by optimizing an
energy function encoding spatial and temporal constraints between object
regions as well as human-provided input. Performance analysis carried out on
challenging video datasets with some users playing the game demonstrated that
our method shows a better trade-off between annotation times and segmentation
accuracy than interactive video annotation and automated video object
segmentation approaches.
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Authors: Julio Cesar Ferreira, Elif Vural, Christine Guillemot
Date: 2015-05-06 00:00:00
Tasks: Clustering, Deblurring, Denoising, Graph Clustering, Image Reconstruction, Image Super-Resolution, Super-Resolution

Local learning of sparse image models has proven to be very effective to
solve inverse problems in many computer vision applications. To learn such
models, the data samples are often clustered using the K-means algorithm with
the Euclidean distance as a dissimilarity metric. However, the Euclidean
distance may not always be a good dissimilarity measure for comparing data
samples lying on a manifold. In this paper, we propose two algorithms for
determining a local subset of training samples from which a good local model
can be computed for reconstructing a given input test sample, where we take
into account the underlying geometry of the data. The first algorithm, called
Adaptive Geometry-driven Nearest Neighbor search (AGNN), is an adaptive scheme
which can be seen as an out-of-sample extension of the replicator graph
clustering method for local model learning. The second method, called
Geometry-driven Overlapping Clusters (GOC), is a less complex nonadaptive
alternative for training subset selection. The proposed AGNN and GOC methods
are evaluated in image super-resolution, deblurring and denoising applications
and shown to outperform spectral clustering, soft clustering, and geodesic
distance based subset selection in most settings.
Open challenges in understanding development and evolution of speech forms: The roles of embodied self-organization, motivation and active exploration
Authors: Pierre-Yves Oudeyer
Date: 2016-01-05 00:00:00

This article discusses open scientific challenges for understanding
development and evolution of speech forms, as a commentary to Moulin-Frier et
al. (Moulin-Frier et al., 2015). Based on the analysis of mathematical models
of the origins of speech forms, with a focus on their assumptions , we study
the fundamental question of how speech can be formed out of non--speech, at
both developmental and evolutionary scales. In particular, we emphasize the
importance of embodied self-organization , as well as the role of mechanisms of
motivation and active curiosity-driven exploration in speech formation. Finally
, we discuss an evolutionary-developmental perspective of the origins of
speech.
Probabilistic Programming with Gaussian Process Memoization
Authors: Ulrich Schaechtle, Ben Zinberg, Alexey Radul, Kostas Stathis, Vikash K. Mansinghka
Date: 2015-12-17 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, Probabilistic Programming, regression, Time Series, Time Series Analysis

Gaussian Processes (GPs) are widely used tools in statistics, machine
learning, robotics, computer vision, and scientific computation. However,
despite their popularity, they can be difficult to apply; all but the simplest
classification or regression applications require specification and inference
over complex covariance functions that do not admit simple analytical
posteriors. This paper shows how to embed Gaussian processes in any
higher-order probabilistic programming language, using an idiom based on
memoization, and demonstrates its utility by implementing and extending classic
and state-of-the-art GP applications. The interface to Gaussian processes,
called gpmem, takes an arbitrary real-valued computational process as input and
returns a statistical emulator that automatically improve as the original
process is invoked and its input-output behavior is recorded. The flexibility
of gpmem is illustrated via three applications: (i) robust GP regression with
hierarchical hyper-parameter learning, (ii) discovering symbolic expressions
from time-series data by fully Bayesian structure learning over kernels
generated by a stochastic grammar, and (iii) a bandit formulation of Bayesian
optimization with automatic inference and action selection. All applications
share a single 50-line Python library and require fewer than 20 lines of
probabilistic code each.
Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features
Authors: Grzegorz Kurzejamski, Jacek Zawistowski, Grzegorz Sarwas
Date: 2016-01-05 00:00:00
Tasks: Clustering, object-detection, Object Detection

This paper presents a method for analysis of the vote space created from the
local features extraction process in a multi-detection system. The method is
opposed to the classic clustering approach and gives a high level of control
over the clusters composition for further verification steps. Proposed method
comprises of the graphical vote space presentation, the proposition generation,
the two-pass iterative vote aggregation and the cascade filters for
verification of the propositions. Cascade filters contain all of the minor
algorithms needed for effective object detection verification. The new approach
does not have the drawbacks of the classic clustering approaches and gives a
substantial control over process of detection. Method exhibits an exceptionally
high detection rate in conjunction with a low false detection chance in
comparison to alternative methods.
Estimating Absolute-Phase Maps Using ESPIRiT and Virtual Conjugate Coils
Authors: Martin Uecker, Michael Lustig
Date: 2015-07-17 00:00:00
Tasks: Image Reconstruction

Purpose: To develop an ESPIRiT-based method to estimate coil sensitivities
with image phase as a building block for efficient and robust image
reconstruction with phase constraints. Theory and Methods: ESPIRiT is a new
framework for calibration of the coil sensitivities and reconstruction in
parallel Magnetic Resonance Imaging (MRI). Applying ESPIRiT to a combined set
of physical and virtual conjugate coils (VCC-ESPIRiT) implicitly exploits
conjugate symmetry in k-space similar to VCC-GRAPPA. Based on this method, a
new post-processing step is proposed for the explicit computation of coil
sensitivities that include the absolute phase of the image. The accuracy of the
computed maps is directly validated using a test based on projection onto fully
sampled coil images and also indirectly in phase-constrained parallel-imaging
reconstructions. Results: The proposed method can estimate accurate
sensitivities which include low-resolution image phase. In case of
high-frequency phase variations VCC-ESPIRiT yields an additional set of maps
that indicates the existence of a high-frequency phase component. Taking this
additional set of maps into account can improve the robustness of
phase-constrained parallel imaging. Conclusion: The extended VCC-ESPIRiT is a
useful tool for phase-constrained imaging.
Efficient Background Modeling Based on Sparse Representation and Outlier Iterative Removal
Authors: Linhao Li, Ping Wang, QinGhua Hu, Sijia Cai
Date: 2014-01-23 00:00:00

Background modeling is a critical component for various vision-based
applications. Most traditional methods tend to be inefficient when solving
large-scale problems. In this paper, we introduce sparse representation into
the task of large scale stable background modeling, and reduce the video size
by exploring its 'discriminative' frames. A cyclic iteration process is then
proposed to extract the background from the discriminative frame set. The two
parts combine to form our Sparse Outlier Iterative Removal (SOIR) algorithm.
The algorithm operates in tensor space to obey the natural data structure of
videos. Experimental results show that a few discriminative frames determine
the performance of the background extraction. Further, SOIR can achieve high
accuracy and high speed simultaneously when dealing with real video sequences.
Thus, SOIR has an advantage in solving large-scale tasks.
Sharp Time--Data Tradeoffs for Linear Inverse Problems
Authors: Samet Oymak, Benjamin Recht, Mahdi Soltanolkotabi
Date: 2015-07-16 00:00:00

In this paper we characterize sharp time-data tradeoffs for optimization
problems used for solving linear inverse problems. We focus on the minimization
of a least-squares objective subject to a constraint defined as the sub-level
set of a penalty function. We present a unified convergence analysis of the
gradient projection algorithm applied to such problems. We sharply characterize
the convergence rate associated with a wide variety of random measurement
ensembles in terms of the number of measurements and structural complexity of
the signal with respect to the chosen penalty function. The results apply to
both convex and nonconvex constraints, demonstrating that a linear convergence
rate is attainable even though the least squares objective is not strongly
convex in these settings. When specialized to Gaussian measurements our results
show that such linear convergence occurs when the number of measurements is
merely 4 times the minimal number required to recover the desired signal at all
(a.k.a. the phase transition). We also achieve a slower but geometric rate of
convergence precisely above the phase transition point. Extensive numerical
results suggest that the derived rates exactly match the empirical performance.
Learning Preferences for Manipulation Tasks from Online Coactive Feedback
Authors: Ashesh Jain, Shikhar Sharma, Thorsten Joachims, Ashutosh Saxena
Date: 2016-01-05 00:00:00

We consider the problem of learning preferences over trajectories for mobile
manipulators such as personal robots and assembly line robots. The preferences
we learn are more intricate than simple geometric constraints on trajectories;
they are rather governed by the surrounding context of various objects and
human interactions in the environment. We propose a coactive online learning
framework for teaching preferences in contextually rich environments. The key
novelty of our approach lies in the type of feedback expected from the user:
the human user does not need to demonstrate optimal trajectories as training
data, but merely needs to iteratively provide trajectories that slightly
improve over the trajectory currently proposed by the system. We argue that
this coactive preference feedback can be more easily elicited than
demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds
of our algorithm match the asymptotic rates of optimal trajectory algorithms.
  We implement our algorithm on two high degree-of-freedom robots, PR2 and
Baxter, and present three intuitive mechanisms for providing such incremental
feedback. In our experimental evaluation we consider two context rich settings
-- household chores and grocery store checkout -- and show that users are able
to train the robot with just a few feedbacks (taking only a few
minutes).\footnote{Parts of this work has been published at NIPS and ISRR
conferences~\citep{Jain13,Jain13b}. This journal submission presents a
consistent full paper, and also includes the proof of regret bounds, more
details of the robotic system, and a thorough related work.}
PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback
Authors: Ashesh Jain, Debarghya Das, Jayesh K. Gupta, Ashutosh Saxena
Date: 2014-06-10 00:00:00

We consider the problem of learning user preferences over robot trajectories
for environments rich in objects and humans. This is challenging because the
criterion defining a good trajectory varies with users, tasks and interactions
in the environment. We represent trajectory preferences using a cost function
that the robot learns and uses it to generate good trajectories in new
environments. We design a crowdsourcing system - PlanIt, where non-expert users
label segments of the robot's trajectory. PlanIt allows us to collect a large
amount of user feedback, and using the weak and noisy labels from PlanIt we
learn the parameters of our model. We test our approach on 122 different
environments for robotic navigation and manipulation tasks. Our extensive
experiments show that the learned cost function generates preferred
trajectories in human environments. Our crowdsourcing system is publicly
available for the visualization of the learned costs and for providing
preference feedback: \url{http://planit.cs.cornell.edu}
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
Authors: Ashesh Jain, Hema S. Koppula, Shane Soh, Bharad Raghavan, Avi Singh, Ashutosh Saxena
Date: 2016-01-05 00:00:00

Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers beforehand can alert drivers before they perform the maneuver and
also give ADAS more time to avoid or prepare for the danger.
  In this work we propose a vehicular sensor-rich platform and learning
algorithms for maneuver anticipation. For this purpose we equip a car with
cameras, Global Positioning System (GPS), and a computing device to capture the
driving context from both inside and outside of the car. In order to anticipate
maneuvers, we propose a sensory-fusion deep learning architecture which jointly
learns to anticipate and fuse multiple sensory streams. Our architecture
consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory
(LSTM) units to capture long temporal dependencies. We propose a novel training
procedure which allows the network to predict the future given only a partial
temporal context. We introduce a diverse data set with 1180 miles of natural
freeway and city driving, and show that we can anticipate maneuvers 3.5 seconds
before they occur in real-time with a precision and recall of 90.5\% and 87.4\%
respectively.
Resource Sharing for Multi-Tenant NoSQL Data Store in Cloud
Authors: Jiaan Zeng
Date: 2016-01-05 00:00:00
Tasks: Scheduling

Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud
providers because it enables resource sharing at low operating cost.
Multi-tenancy takes several forms depending on whether the back-end file system
is a local file system (LFS) or a parallel file system (PFS), and on whether
tenants are independent or share data across tenants. In this thesis I focus on
and propose solutions to two cases: independent data-local file system, and
shared data-parallel file system.
  In the independent data-local file system case, resource contention occurs
under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL
stores, causing performance degradation for one tenant by another. We
investigate the interference and propose two approaches. The first provides a
scheduling scheme that can approximate resource consumption, adapt to workload
dynamics and work in a distributed fashion. The second introduces a
workload-aware resource reservation approach to prevent interference. The
approach relies on a performance model obtained offline and plans the
reservation according to different workload resource demands. Results show the
approaches together can prevent interference and adapt to dynamic workloads
under multi-tenancy.
  In the shared data-parallel file system case, it has been shown that running
a distributed NoSQL store over PFS for shared data across tenants is not cost
effective. Overheads are introduced due to the unawareness of the NoSQL store
of PFS. This dissertation targets the key-value store (KVS), a specific form of
NoSQL stores, and proposes a lightweight KVS over a parallel file system to
improve efficiency. The solution is built on an embedded KVS for high
performance but uses novel data structures to support concurrent writes.
Results show the proposed system outperforms Cassandra and Voldemort in several
different workloads.