pwc-archive/papers-with-abstracts huggingface dataset:
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
Authors: Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
Date: 2016-02-09 00:00:00
Tasks: GPU

We introduce a method to train Binarized Neural Networks (BNNs) - neural
networks with binary weights and activations at run-time. At training-time the
binary weights and activations are used for computing the parameters gradients.
During the forward pass, BNNs drastically reduce memory size and accesses, and
replace most arithmetic operations with bit-wise operations, which is expected
to substantially improve power-efficiency. To validate the effectiveness of
BNNs we conduct two sets of experiments on the Torch7 and Theano frameworks. On
both, BNNs achieved nearly state-of-the-art results over the MNIST, CIFAR-10
and SVHN datasets. Last but not least, we wrote a binary matrix multiplication
GPU kernel with which it is possible to run our MNIST BNN 7 times faster than
with an unoptimized GPU kernel, without suffering any loss in classification
accuracy. The code for training and running our BNNs is available on-line.
Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information
Authors: Francesco Renna, Liming Wang, Xin Yuan, Jianbo Yang, Galen Reeves, Robert Calderbank, Lawrence Carin, Miguel R. D. Rodrigues
Date: 2014-12-01 00:00:00
Tasks: General Classification

This paper offers a characterization of fundamental limits on the
classification and reconstruction of high-dimensional signals from
low-dimensional features, in the presence of side information. We consider a
scenario where a decoder has access both to linear features of the signal of
interest and to linear features of the side information signal; while the side
information may be in a compressed form, the objective is recovery or
classification of the primary signal, not the side information. The signal of
interest and the side information are each assumed to have (distinct) latent
discrete labels; conditioned on these two labels, the signal of interest and
side information are drawn from a multivariate Gaussian distribution. With
joint probabilities on the latent labels, the overall signal-(side information)
representation is defined by a Gaussian mixture model. We then provide sharp
sufficient and/or necessary conditions for these quantities to approach zero
when the covariance matrices of the Gaussians are nearly low-rank. These
conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv
conditions, are a function of the number of linear features extracted from the
signal of interest, the number of linear features extracted from the side
information signal, and the geometry of these signals and their interplay.
Moreover, on assuming that the signal of interest and the side information obey
such an approximately low-rank model, we derive expansions of the
reconstruction error as a function of the deviation from an exactly low-rank
model; such expansions also allow identification of operational regimes where
the impact of side information on signal reconstruction is most relevant. Our
framework, which offers a principled mechanism to integrate side information in
high-dimensional data problems, is also tested in the context of imaging
applications.
Variable-Length Hashing
Authors: Honghai Yu, Pierre Moulin, Hong Wei Ng, XiaoLi Li
Date: 2016-03-17 00:00:00
Tasks: Code Search, Retrieval

Hashing has emerged as a popular technique for large-scale similarity search.
Most learning-based hashing methods generate compact yet correlated hash codes.
However, this redundancy is storage-inefficient. Hence we propose a lossless
variable-length hashing (VLH) method that is both storage- and
search-efficient. Storage efficiency is achieved by converting the fixed-length
hash code into a variable-length code. Search efficiency is obtained by using a
multiple hash table structure. With VLH, we are able to deliberately add
redundancy into hash codes to improve retrieval performance with little
sacrifice in storage efficiency or search complexity. In particular, we propose
a block K-means hashing (B-KMH) method to obtain significantly improved
retrieval performance with no increase in storage and marginal increase in
computational cost.
Cooking in the kitchen: Recognizing and Segmenting Human Activities in Videos
Authors: Hilde Kuehne, Juergen Gall, Thomas Serre
Date: 2015-08-25 00:00:00
Tasks: Action Recognition, Temporal Action Localization

As research on action recognition matures, the focus is shifting away from
categorizing basic task-oriented actions using hand-segmented video datasets to
understanding complex goal-oriented daily human activities in real-world
settings. Temporally structured models would seem obvious to tackle this set of
problems, but so far, cases where these models have outperformed simpler
unstructured bag-of-word types of models are scarce. With the increasing
availability of large human activity datasets, combined with the development of
novel feature coding techniques that yield more compact representations, it is
time to revisit structured generative approaches.
  Here, we describe an end-to-end generative approach from the encoding of
features to the structural modeling of complex human activities by applying
Fisher vectors and temporal models for the analysis of video sequences.
  We systematically evaluate the proposed approach on several available
datasets (ADL, MPIICooking, and Breakfast datasets) using a variety of
performance metrics. Through extensive system evaluations, we demonstrate that
combining compact video representations based on Fisher Vectors with HMM-based
modeling yields very significant gains in accuracy and when properly trained
with sufficient training samples, structured temporal models outperform
unstructured bag-of-word types of models by a large margin on the tested
performance metric.
An end-to-end generative framework for video segmentation and recognition
Authors: Hilde Kuehne, Juergen Gall, Thomas Serre
Date: 2015-09-07 00:00:00
Tasks: Video Segmentation, Video Semantic Segmentation

We describe an end-to-end generative approach for the segmentation and
recognition of human activities. In this approach, a visual representation
based on reduced Fisher Vectors is combined with a structured temporal model
for recognition. We show that the statistical properties of Fisher Vectors make
them an especially suitable front-end for generative models such as Gaussian
mixtures. The system is evaluated for both the recognition of complex
activities as well as their parsing into action units. Using a variety of video
datasets ranging from human cooking activities to animal behaviors, our
experiments demonstrate that the resulting architecture outperforms
state-of-the-art approaches for larger datasets, i.e. when sufficient amount of
data is available for training structured generative models.
Convolutional Neural Networks using Logarithmic Data Representation
Authors: Daisuke Miyashita, Edward H. Lee, Boris Murmann
Date: 2016-03-03 00:00:00
Tasks: General Classification

Recent advances in convolutional neural networks have considered model
complexity and hardware efficiency to enable deployment onto embedded systems
and mobile devices. For example, it is now well-known that the arithmetic
operations of deep networks can be encoded down to 8-bit fixed-point without
significant deterioration in performance. However, further reduction in
precision down to as low as 3-bit fixed-point results in significant losses in
performance. In this paper we propose a new data representation that enables
state-of-the-art networks to be encoded to 3 bits with negligible loss in
classification performance. To perform this, we take advantage of the fact that
the weights and activations in a trained network naturally have non-uniform
distributions. Using non-uniform, base-2 logarithmic representation to encode
weights, communicate activations, and perform dot-products enables networks to
1) achieve higher classification accuracies than fixed-point at the same
resolution and 2) eliminate bulky digital multipliers. Finally, we propose an
end-to-end training procedure that uses log representation at 5-bits, which
achieves higher final test accuracy than linear at 5-bits.
Saliency Detection with Spaces of Background-based Distribution
Authors: Tong Zhao, Lin Li, Xinghao Ding, Yue Huang, Delu Zeng
Date: 2016-03-17 00:00:00
Tasks: Saliency Detection

In this letter, an effective image saliency detection method is proposed by
constructing some novel spaces to model the background and redefine the
distance of the salient patches away from the background. Concretely, given the
backgroundness prior, eigendecomposition is utilized to create four spaces of
background-based distribution (SBD) to model the background, in which a more
appropriate metric (Mahalanobis distance) is quoted to delicately measure the
saliency of every image patch away from the background. After that, a coarse
saliency map is obtained by integrating the four adjusted Mahalanobis distance
maps, each of which is formed by the distances between all the patches and
background in the corresponding SBD. To be more discriminative, the coarse
saliency map is further enhanced into the posterior probability map within
Bayesian perspective. Finally, the final saliency map is generated by properly
refining the posterior probability map with geodesic distance. Experimental
results on two usual datasets show that the proposed method is effective
compared with the state-of-the-art algorithms.
Detecting events and key actors in multi-person videos
Authors: Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, Li Fei-Fei
Date: 2015-11-09 00:00:00
Proceeding: CVPR 2016 6
Tasks: Event Detection, General Classification

Multi-person event recognition is a challenging task, often with many people
active in the scene but only a small subset contributing to an actual event. In
this paper, we propose a model which learns to detect events in such videos
while automatically "attending" to the people responsible for the event. Our
model does not use explicit annotations regarding who or where those people are
during training and testing. In particular, we track people in videos and use a
recurrent neural network (RNN) to represent the track features. We learn
time-varying attention weights to combine these features at each time-instant.
The attended features are then processed using another RNN for event
detection/classification. Since most video datasets with multiple people are
restricted to a small number of videos, we also collected a new basketball
dataset comprising 257 basketball games with 14K event annotations
corresponding to 11 event classes. Our model outperforms state-of-the-art
methods for both event classification and detection on this new dataset.
Additionally, we show that the attention mechanism is able to consistently
localize the relevant players.
Hardware Acceleration for Boolean Satisfiability Solver by Applying Belief Propagation Algorithm
Authors: Te-Hsuan Chen, Ju-Yi Lu
Date: 2016-03-16 00:00:00
Tasks: Decoder

Boolean satisfiability (SAT) has an extensive application domain in computer
science, especially in electronic design automation applications. Circuit
synthesis, optimization, and verification problems can be solved by
transforming original problems to SAT problems. However, the SAT problem is
known as NP-complete, which means there is no efficient method to solve it.
Therefore, an efficient SAT solver to enhance the performance is always
desired. We propose a hardware acceleration method for SAT problems. By
surveying the properties of SAT problems and the decoding of low-density
parity-check (LDPC) codes, a special class of error-correcting codes, we
discover that both of them are constraint satisfaction problems. The belief
propagation algorithm has been successfully applied to the decoding of LDPC,
and the corresponding decoder hardware designs are extensively studied.
Therefore, we proposed a belief propagation based algorithm to solve SAT
problems. With this algorithm, the SAT solver can be accelerated by hardware. A
software simulator is implemented to verify the proposed algorithm and the
performance improvement is estimated. Our experiment results show that time
complexity does not increase with the size of SAT problems and the proposed
method can achieve at least 30x speedup compared to MiniSat.
On the Covariance of ICP-based Scan-matching Techniques
Authors: Silvère Bonnabel, Martin Barczyk, François Goulette
Date: 2014-10-16 00:00:00
Tasks: valid

This paper considers the problem of estimating the covariance of
roto-translations computed by the Iterative Closest Point (ICP) algorithm. The
problem is relevant for localization of mobile robots and vehicles equipped
with depth-sensing cameras (e.g., Kinect) or Lidar (e.g., Velodyne). The
closed-form formulas for covariance proposed in previous literature generally
build upon the fact that the solution to ICP is obtained by minimizing a linear
least-squares problem. In this paper, we show this approach needs caution
because the rematching step of the algorithm is not explicitly accounted for,
and applying it to the point-to-point version of ICP leads to completely
erroneous covariances. We then provide a formal mathematical proof why the
approach is valid in the point-to-plane version of ICP, which validates the
intuition and experimental results of practitioners.
Persistent Homology of Attractors For Action Recognition
Authors: Vinay Venkataraman, Karthikeyan Natesan Ramamurthy, Pavan Turaga
Date: 2016-03-16 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Time Series, Time Series Analysis, Topological Data Analysis

In this paper, we propose a novel framework for dynamical analysis of human
actions from 3D motion capture data using topological data analysis. We model
human actions using the topological features of the attractor of the dynamical
system. We reconstruct the phase-space of time series corresponding to actions
using time-delay embedding, and compute the persistent homology of the
phase-space reconstruction. In order to better represent the topological
properties of the phase-space, we incorporate the temporal adjacency
information when computing the homology groups. The persistence of these
homology groups encoded using persistence diagrams are used as features for the
actions. Our experiments with action recognition using these features
demonstrate that the proposed approach outperforms other baseline methods.
Semantic Folding Theory And its Application in Semantic Fingerprinting
Authors: Francisco De Sousa Webber
Date: 2015-11-28 00:00:00

Human language is recognized as a very complex domain since decades. No
computer system has been able to reach human levels of performance so far. The
only known computational system capable of proper language processing is the
human brain. While we gather more and more data about the brain, its
fundamental computational processes still remain obscure. The lack of a sound
computational brain theory also prevents the fundamental understanding of
Natural Language Processing. As always when science lacks a theoretical
foundation, statistical modeling is applied to accommodate as many sampled
real-world data as possible. An unsolved fundamental issue is the actual
representation of language (data) within the brain, denoted as the
Representational Problem. Starting with Jeff Hawkins' Hierarchical Temporal
Memory (HTM) theory, a consistent computational theory of the human cortex, we
have developed a corresponding theory of language data representation: The
Semantic Folding Theory. The process of encoding words, by using a topographic
semantic space as distributional reference frame into a sparse binary
representational vector is called Semantic Folding and is the central topic of
this document. Semantic Folding describes a method of converting language from
its symbolic representation (text) into an explicit, semantically grounded
representation that can be generically processed by Hawkins' HTM networks. As
it turned out, this change in representation, by itself, can solve many complex
NLP problems by applying Boolean operators and a generic similarity function
like the Euclidian Distance. Many practical problems of statistical NLP
systems, like the high cost of computation, the fundamental incongruity of
precision and recall , the complex tuning procedures etc., can be elegantly
overcome by applying Semantic Folding.
Image Labeling by Assignment
Authors: Freddie Åström, Stefania Petra, Bernhard Schmitzer, Christoph Schnörr
Date: 2016-03-16 00:00:00

We introduce a novel geometric approach to the image labeling problem.
Abstracting from specific labeling applications, a general objective function
is defined on a manifold of stochastic matrices, whose elements assign prior
data that are given in any metric space, to observed image measurements. The
corresponding Riemannian gradient flow entails a set of replicator equations,
one for each data point, that are spatially coupled by geometric averaging on
the manifold. Starting from uniform assignments at the barycenter as natural
initialization, the flow terminates at some global maximum, each of which
corresponds to an image labeling that uniquely assigns the prior data. Our
geometric variational approach constitutes a smooth non-convex inner
approximation of the general image labeling problem, implemented with sparse
interior-point numerics in terms of parallel multiplicative updates that
converge efficiently.
PERCH: Perception via Search for Multi-Object Recognition and Localization
Authors: Venkatraman Narayanan, Maxim Likhachev
Date: 2015-10-19 00:00:00
Tasks: Heuristic Search, Object Recognition, Scene Generation

In many robotic domains such as flexible automated manufacturing or personal
assistance, a fundamental perception task is that of identifying and localizing
objects whose 3D models are known. Canonical approaches to this problem include
discriminative methods that find correspondences between feature descriptors
computed over the model and observed data. While these methods have been
employed successfully, they can be unreliable when the feature descriptors fail
to capture variations in observed data; a classic cause being occlusion. As a
step towards deliberative reasoning, we present PERCH: PErception via SeaRCH,
an algorithm that seeks to find the best explanation of the observed sensor
data by hypothesizing possible scenes in a generative fashion. Our
contributions are: i) formulating the multi-object recognition and localization
task as an optimization problem over the space of hypothesized scenes, ii)
exploiting structure in the optimization to cast it as a combinatorial search
problem on what we call the Monotone Scene Generation Tree, and iii) leveraging
parallelization and recent advances in multi-heuristic search in making
combinatorial search tractable. We prove that our system can guaranteedly
produce the best explanation of the scene under the chosen cost function, and
validate our claims on real world RGB-D test data. Our experimental results
show that we can identify and localize objects under heavy occlusion--cases
where state-of-the-art methods struggle.
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing
Authors: Chenxin Ma, Martin Takáč
Date: 2016-03-16 00:00:00
Tasks: Distributed Optimization

In this paper we study inexact dumped Newton method implemented in a
distributed environment. We start with an original DiSCO algorithm
[Communication-Efficient Distributed Optimization of Self-Concordant Empirical
Loss, Yuchen Zhang and Lin Xiao, 2015]. We will show that this algorithm may
not scale well and propose an algorithmic modifications which will lead to less
communications, better load-balancing and more efficient computation. We
perform numerical experiments with an regularized empirical loss minimization
instance described by a 273GB dataset.
Applying Artifical Neural Networks To Predict Nominal Vehicle Performance
Authors: Adam J. Last
Date: 2016-03-16 00:00:00

This paper investigates the use of artificial neural networks (ANNs) to
replace traditional algorithms and manual review for identifying anomalies in
vehicle run data. The specific data used for this study is from undersea
vehicle qualification tests. Such data is highly non-linear, therefore
traditional algorithms are not adequate and manual review is time consuming. By
using ANNs to predict nominal vehicle performance based solely on information
available pre-run, vehicle deviation from expected performance can be
automatically identified in the post-run data. Such capability is only now
becoming available due to the rapid increase in understanding of ANN framework
and available computing power in the past decade. The ANN trained for the
purpose of this investigation is relatively simple, to keep the computing
requirements within the parameters of a modern desktop PC. This ANN showed
potential in predicting vehicle performance, particularly during transient
events within the run data. However, there were also several performance cases,
such as steady state operation and cases which did not have sufficient training
data, where the ANN showed deficiencies. It is expected that as computational
power becomes more readily available, ANN understanding matures, and more
training data is acquired from real world tests, the performance predictions of
the ANN will surpass traditional algorithms and manual human review.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Authors: Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng
Date: 2016-03-14 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Dimensionality Reduction, General Classification, GPU, Retrieval, speech-recognition

TensorFlow is an interface for expressing machine learning algorithms, and an
implementation for executing such algorithms. A computation expressed using
TensorFlow can be executed with little or no change on a wide variety of
heterogeneous systems, ranging from mobile devices such as phones and tablets
up to large-scale distributed systems of hundreds of machines and thousands of
computational devices such as GPU cards. The system is flexible and can be used
to express a wide variety of algorithms, including training and inference
algorithms for deep neural network models, and it has been used for conducting
research and for deploying machine learning systems into production across more
than a dozen areas of computer science and other fields, including speech
recognition, computer vision, robotics, information retrieval, natural language
processing, geographic information extraction, and computational drug
discovery. This paper describes the TensorFlow interface and an implementation
of that interface that we have built at Google. The TensorFlow API and a
reference implementation were released as an open-source package under the
Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.