pwc-archive/papers-with-abstracts huggingface dataset:
Morphological Error Detection in 3D Segmentations
Authors: David Rolnick, Yaron Meirovitch, Toufiq Parag, Hanspeter Pfister, Viren Jain, Jeff W. Lichtman, Edward S. Boyden, Nir Shavit
Date: 2017-05-30 00:00:00
Tasks: General Classification

Deep learning algorithms for connectomics rely upon localized classification,
rather than overall morphology. This leads to a high incidence of erroneously
merged objects. Humans, by contrast, can easily detect such errors by acquiring
intuition for the correct morphology of objects. Biological neurons have
complicated and variable shapes, which are challenging to learn, and merge
errors take a multitude of different forms. We present an algorithm, MergeNet,
that shows 3D ConvNets can, in fact, detect merge errors from high-level
neuronal morphology. MergeNet follows unsupervised training and operates across
datasets. We demonstrate the performance of MergeNet both on a variety of
connectomics data and on a dataset created from merged MNIST images.
Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy
Authors: Tathagata Chakraborti, Sarath Sreedharan, Yu Zhang, Subbarao Kambhampati
Date: 2017-01-28 00:00:00

When AI systems interact with humans in the loop, they are often called on to
provide explanations for their plans and behavior. Past work on plan
explanations primarily involved the AI system explaining the correctness of its
plan and the rationale for its decision in terms of its own model. Such
soliloquy is wholly inadequate in most realistic scenarios where the humans
have domain and task models that differ significantly from that used by the AI
system. We posit that the explanations are best studied in light of these
differing models. In particular, we show how explanation can be seen as a
"model reconciliation problem" (MRP), where the AI system in effect suggests
changes to the human's model, so as to make its plan be optimal with respect to
that changed human model. We will study the properties of such explanations,
present algorithms for automatically computing them, and evaluate the
performance of the algorithms.
Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks
Authors: Hang Ma, Jiaoyang Li, T. K. Satish Kumar, Sven Koenig
Date: 2017-05-30 00:00:00
Tasks: Multi-Agent Path Finding

The multi-agent path-finding (MAPF) problem has recently received a lot of
attention. However, it does not capture important characteristics of many
real-world domains, such as automated warehouses, where agents are constantly
engaged with new tasks. In this paper, we therefore study a lifelong version of
the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In
the MAPD problem, agents have to attend to a stream of delivery tasks in an
online setting. One agent has to be assigned to each delivery task. This agent
has to first move to a given pickup location and then to a given delivery
location while avoiding collisions with other agents. We present two decoupled
MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS).
Theoretically, we show that they solve all well-formed MAPD instances, a
realistic subclass of MAPD instances. Experimentally, we compare them against a
centralized strawman MAPD algorithm without this guarantee in a simulated
warehouse system. TP can easily be extended to a fully distributed MAPD
algorithm and is the best choice when real-time computation is of primary
concern since it remains efficient for MAPD instances with hundreds of agents
and tasks. TPTS requires limited communication among agents and balances well
between TP and the centralized MAPD algorithm.
Generic Tubelet Proposals for Action Localization
Authors: Jiawei He, Mostafa S. Ibrahim, Zhiwei Deng, Greg Mori
Date: 2017-05-30 00:00:00
Tasks: Action Classification, Action Localization, General Classification

We develop a novel framework for action localization in videos. We propose
the Tube Proposal Network (TPN), which can generate generic, class-independent,
video-level tubelet proposals in videos. The generated tubelet proposals can be
utilized in various video analysis tasks, including recognizing and localizing
actions in videos. In particular, we integrate these generic tubelet proposals
into a unified temporal deep network for action classification. Compared with
other methods, our generic tubelet proposal method is accurate, general, and is
fully differentiable under a smoothL1 loss function. We demonstrate the
performance of our algorithm on the standard UCF-Sports, J-HMDB21, and UCF-101
datasets. Our class-independent TPN outperforms other tubelet generation
methods, and our unified temporal deep network achieves state-of-the-art
localization results on all three datasets.
A Tale of Two Animats: What does it take to have goals?
Authors: Larissa Albantakis
Date: 2017-05-30 00:00:00

What does it take for a system, biological or not, to have goals? Here, this
question is approached in the context of in silico artificial evolution. By
examining the informational and causal properties of artificial organisms
('animats') controlled by small, adaptive neural networks (Markov Brains), this
essay discusses necessary requirements for intrinsic information, autonomy, and
meaning. The focus lies on comparing two types of Markov Brains that evolved in
the same simple environment: one with purely feedforward connections between
its elements, the other with an integrated set of elements that causally
constrain each other. While both types of brains 'process' information about
their environment and are equally fit, only the integrated one forms a causally
autonomous entity above a background of external influences. This suggests that
to assess whether goals are meaningful for a system itself, it is important to
understand what the system is, rather than what it does.
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
Authors: Yu Xiang, Dieter Fox
Date: 2017-03-09 00:00:00
Tasks: Scene Understanding

3D scene understanding is important for robots to interact with the 3D world
in a meaningful way. Most previous works on 3D scene understanding focus on
recognizing geometrical or semantic properties of the scene independently. In
this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a
novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a
new recurrent neural network architecture for semantic labeling on RGB-D
videos. The output of the network is integrated with mapping techniques such as
KinectFusion in order to inject semantic information into the reconstructed 3D
scene. Experiments conducted on a real world dataset and a synthetic dataset
with RGB-D videos demonstrate the ability of our method in semantic 3D scene
mapping.
Experience Replay Using Transition Sequences
Authors: Thommen George Karimpanal, Roland Bouffanais
Date: 2017-05-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Experience replay is one of the most commonly used approaches to improve the sample efficiency of reinforcement learning algorithms. In this work, we propose an approach to select and replay sequences of transitions in order to accelerate the learning of a reinforcement learning agent in an off-policy setting. In addition to selecting appropriate sequences, we also artificially construct transition sequences using information gathered from previous agent-environment interactions. These sequences, when replayed, allow value function information to trickle down to larger sections of the state/state-action space, thereby making the most of the agent's experience. We demonstrate our approach on modified versions of standard reinforcement learning tasks such as the mountain car and puddle world problems and empirically show that it enables better learning of value functions as compared to other forms of experience replay. Further, we briefly discuss some of the possible extensions to this work, as well as applications and situations where this approach could be particularly useful.
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
Authors: Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Z. Steven Wu
Date: 2017-05-30 00:00:00

Traditional approaches to differential privacy assume a fixed privacy
requirement $\epsilon$ for a computation, and attempt to maximize the accuracy
of the computation subject to the privacy constraint. As differential privacy
is increasingly deployed in practical settings, it may often be that there is
instead a fixed accuracy requirement for a given computation and the data
analyst would like to maximize the privacy of the computation subject to the
accuracy constraint. This raises the question of how to find and run a
maximally private empirical risk minimizer subject to a given accuracy
requirement. We propose a general "noise reduction" framework that can apply to
a variety of private empirical risk minimization (ERM) algorithms, using them
to "search" the space of privacy levels to find the empirically strongest one
that meets the accuracy constraint, incurring only logarithmic overhead in the
number of privacy levels searched. The privacy analysis of our algorithm leads
naturally to a version of differential privacy where the privacy parameters are
dependent on the data, which we term ex-post privacy, and which is related to
the recently introduced notion of privacy odometers. We also give an ex-post
privacy analysis of the classical AboveThreshold privacy tool, modifying it to
allow for queries chosen depending on the database. Finally, we apply our
approach to two common objectives, regularized linear and logistic regression,
and empirically compare our noise reduction methods to (i) inverting the
theoretical utility guarantees of standard private ERM algorithms and (ii) a
stronger, empirical baseline based on binary search.
Stacking machine learning classifiers to identify Higgs bosons at the LHC
Authors: Alexandre Alves
Date: 2016-12-21 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) algorithms have been employed in the problem of
classifying signal and background events with high accuracy in particle
physics. In this paper, we compare the performance of a widespread ML
technique, namely, \emph{stacked generalization}, against the results of two
state-of-art algorithms: (1) a deep neural network (DNN) in the task of
discovering a new neutral Higgs boson and (2) a scalable machine learning
system for tree boosting, in the Standard Model Higgs to tau leptons channel,
both at the 8 TeV LHC. In a cut-and-count analysis, \emph{stacking} three
algorithms performed around 16\% worse than DNN but demanding far less
computation efforts, however, the same \emph{stacking} outperforms boosted
decision trees. Using the stacked classifiers in a multivariate statistical
analysis (MVA), on the other hand, significantly enhances the statistical
significance compared to cut-and-count in both Higgs processes, suggesting that
combining an ensemble of simpler and faster ML algorithms with MVA tools is a
better approach than building a complex state-of-art algorithm for
cut-and-count.
Dynamics Based Features For Graph Classification
Authors: Leonardo Gutierrez Gomez, Benjamin Chiem, Jean-Charles Delvenne
Date: 2017-05-30 00:00:00
Tasks: Classification, General Classification, Graph Classification

Numerous social, medical, engineering and biological challenges can be framed
as graph-based learning tasks. Here, we propose a new feature based approach to
network classification. We show how dynamics on a network can be useful to
reveal patterns about the organization of the components of the underlying
graph where the process takes place. We define generalized assortativities on
networks and use them as generalized features across multiple time scales.
These features turn out to be suitable signatures for discriminating between
different classes of networks. Our method is evaluated empirically on
established network benchmarks. We also introduce a new dataset of human brain
networks (connectomes) and use it to evaluate our method. Results reveal that
our dynamics based features are competitive and often outperform state of the
art accuracies.
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
Authors: Xiang Yu, Ngoc Thang Vu
Date: 2017-05-30 00:00:00
Proceeding: ACL 2017 7
Tasks: Dependency Parsing, Word Embeddings

We present a transition-based dependency parser that uses a convolutional
neural network to compose word representations from characters. The character
composition model shows great improvement over the word-lookup model,
especially for parsing agglutinative languages. These improvements are even
better than using pre-trained word embeddings from extra data. On the SPMRL
data sets, our system outperforms the previous best greedy parser (Ballesteros
et al., 2015) by a margin of 3% on average.
High Dimensional Inference with Random Maximum A-Posteriori Perturbations
Authors: Tamir Hazan, Francesco Orabona, Anand D. Sarwate, Subhransu Maji, Tommi Jaakkola
Date: 2016-02-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This paper presents a new approach, called perturb-max, for high-dimensional
statistical inference that is based on applying random perturbations followed
by optimization. This framework injects randomness to maximum a-posteriori
(MAP) predictors by randomly perturbing the potential function for the input. A
classic result from extreme value statistics asserts that perturb-max
operations generate unbiased samples from the Gibbs distribution using
high-dimensional perturbations. Unfortunately, the computational cost of
generating so many high-dimensional random variables can be prohibitive.
However, when the perturbations are of low dimension, sampling the perturb-max
prediction is as efficient as MAP optimization. This paper shows that the
expected value of perturb-max inference with low dimensional perturbations can
be used sequentially to generate unbiased samples from the Gibbs distribution.
Furthermore the expected value of the maximal perturbations is a natural bound
on the entropy of such perturb-max models. A measure concentration result for
perturb-max values shows that the deviation of their sampled average from its
expectation decays exponentially in the number of samples, allowing effective
approximation of the expectation.
Forward-Backward Selection with Early Dropping
Authors: Giorgos Borboudakis, Ioannis Tsamardinos
Date: 2017-05-30 00:00:00
Tasks: Computational Efficiency, feature selection

Forward-backward selection is one of the most basic and commonly-used feature
selection algorithms available. It is also general and conceptually applicable
to many different types of data. In this paper, we propose a heuristic that
significantly improves its running time, while preserving predictive accuracy.
The idea is to temporarily discard the variables that are conditionally
independent with the outcome given the selected variable set. Depending on how
those variables are reconsidered and reintroduced, this heuristic gives rise to
a family of algorithms with increasingly stronger theoretical guarantees. In
distributions that can be faithfully represented by Bayesian networks or
maximal ancestral graphs, members of this algorithmic family are able to
correctly identify the Markov blanket in the sample limit. In experiments we
show that the proposed heuristic increases computational efficiency by about
two orders of magnitude in high-dimensional problems, while selecting fewer
variables and retaining predictive performance. Furthermore, we show that the
proposed algorithm and feature selection with LASSO perform similarly when
restricted to select the same number of variables, making the proposed
algorithm an attractive alternative for problems where no (efficient) algorithm
for LASSO exists.
A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization
Authors: Samuel Silva, Rengan Suresh, Feng Tao, Johnathan Votion, Yongcan Cao
Date: 2017-05-30 00:00:00
Tasks: Clustering, Management

Data-target association is an important step in multi-target localization for
the intelligent operation of un- manned systems in numerous applications such
as search and rescue, traffic management and surveillance. The objective of
this paper is to present an innovative data association learning approach named
multi-layer K-means (MLKM) based on leveraging the advantages of some existing
machine learning approaches, including K-means, K-means++, and deep neural
networks. To enable the accurate data association from different sensors for
efficient target localization, MLKM relies on the clustering capabilities of
K-means++ structured in a multi-layer framework with the error correction
feature that is motivated by the backpropogation that is well-known in deep
learning research. To show the effectiveness of the MLKM method, numerous
simulation examples are conducted to compare its performance with K-means,
K-means++, and deep neural networks.
Ternary Neural Networks with Fine-Grained Quantization
Authors: Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, Pradeep Dubey
Date: 2017-05-02 00:00:00
Tasks: Quantization

We propose a novel fine-grained quantization (FGQ) method to ternarize
pre-trained full precision models, while also constraining activations to 8 and
4-bits. Using this method, we demonstrate a minimal loss in classification
accuracy on state-of-the-art topologies without additional training. We provide
an improved theoretical formulation that forms the basis for a higher quality
solution using FGQ. Our method involves ternarizing the original weight tensor
in groups of $N$ weights. Using $N=4$, we achieve Top-1 accuracy within $3.7\%$
and $4.2\%$ of the baseline full precision result for Resnet-101 and Resnet-50
respectively, while eliminating $75\%$ of all multiplications. These results
enable a full 8/4-bit inference pipeline, with best-reported accuracy using
ternary weights on ImageNet dataset, with a potential of $9\times$ improvement
in performance. Also, for smaller networks like AlexNet, FGQ achieves
state-of-the-art results. We further study the impact of group size on both
performance and accuracy. With a group size of $N=64$, we eliminate
$\approx99\%$ of the multiplications; however, this introduces a noticeable
drop in accuracy, which necessitates fine tuning the parameters at lower
precision. We address this by fine-tuning Resnet-50 with 8-bit activations and
ternary weights at $N=64$, improving the Top-1 accuracy to within $4\%$ of the
full precision result with $<30\%$ additional training overhead. Our final
quantized model can run on a full 8-bit compute pipeline using 2-bit weights
and has the potential of up to $15\times$ improvement in performance compared
to baseline full-precision models.
A Low Dimensionality Representation for Language Variety Identification
Authors: Francisco Rangel, Marc Franco-Salvador, Paolo Rosso
Date: 2017-05-30 00:00:00

Language variety identification aims at labelling texts in a native language
(e.g. Spanish, Portuguese, English) with its specific variation (e.g.
Argentina, Chile, Mexico, Peru, Spain; Brazil, Portugal; UK, US). In this work
we propose a low dimensionality representation (LDR) to address this task with
five different varieties of Spanish: Argentina, Chile, Mexico, Peru and Spain.
We compare our LDR method with common state-of-the-art representations and show
an increase in accuracy of ~35%. Furthermore, we compare LDR with two reference
distributed representation models. Experimental results show competitive
performance while dramatically reducing the dimensionality --and increasing the
big data suitability-- to only 6 features per variety. Additionally, we analyse
the behaviour of the employed machine learning algorithms and the most
discriminating features. Finally, we employ an alternative dataset to test the
robustness of our low dimensionality representation with another set of similar
languages.
Recurrent Estimation of Distributions
Authors: Junier B. Oliva, Kumar Avinava Dubey, Barnabas Poczos, Eric Xing, Jeff Schneider
Date: 2017-05-30 00:00:00
Tasks: Anomaly Detection, Density Estimation

This paper presents the recurrent estimation of distributions (RED) for
modeling real-valued data in a semiparametric fashion. RED models make two
novel uses of recurrent neural networks (RNNs) for density estimation of
general real-valued data. First, RNNs are used to transform input covariates
into a latent space to better capture conditional dependencies in inputs.
After, an RNN is used to compute the conditional distributions of the latent
covariates. The resulting model is efficient to train, compute, and sample
from, whilst producing normalized pdfs. The effectiveness of RED is shown via
several real-world data experiments. Our results show that RED models achieve a
lower held-out negative log-likelihood than other neural network approaches
across multiple dataset sizes and dimensionalities. Further context of the
efficacy of RED is provided by considering anomaly detection tasks, where we
also observe better performance over alternative models.
Knowledge Base Completion: Baselines Strike Back
Authors: Rudolf Kadlec, Ondrej Bajgar, Jan Kleindienst
Date: 2017-05-30 00:00:00
Proceeding: WS 2017 8
Tasks: Knowledge Base Completion

Many papers have been published on the knowledge base completion task in the
past few years. Most of these introduce novel architectures for relation
learning that are evaluated on standard datasets such as FB15k and WN18. This
paper shows that the accuracy of almost all models published on the FB15k can
be outperformed by an appropriately tuned baseline - our reimplementation of
the DistMult model. Our findings cast doubt on the claim that the performance
improvements of recent models are due to architectural changes as opposed to
hyper-parameter tuning or different training objectives. This should prompt
future research to re-consider how the performance of models is evaluated and
reported.
The Cramer Distance as a Solution to Biased Wasserstein Gradients
Authors: Marc G. Bellemare, Ivo Danihelka, Will Dabney, Shakir Mohamed, Balaji Lakshminarayanan, Stephan Hoyer, Rémi Munos
Date: 2017-05-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning, Generative Adversarial Network

The Wasserstein probability metric has received much attention from the
machine learning community. Unlike the Kullback-Leibler divergence, which
strictly measures change in probability, the Wasserstein metric reflects the
underlying geometry between outcomes. The value of being sensitive to this
geometry has been demonstrated, among others, in ordinal regression and
generative modelling. In this paper we describe three natural properties of
probability divergences that reflect requirements from machine learning: sum
invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein
metric possesses the first two properties but, unlike the Kullback-Leibler
divergence, does not possess the third. We provide empirical evidence
suggesting that this is a serious issue in practice. Leveraging insights from
probabilistic forecasting we propose an alternative to the Wasserstein metric,
the Cram\'er distance. We show that the Cram\'er distance possesses all three
desired properties, combining the best of the Wasserstein and Kullback-Leibler
divergences. To illustrate the relevance of the Cram\'er distance in practice
we design a new algorithm, the Cram\'er Generative Adversarial Network (GAN),
and show that it performs significantly better than the related Wasserstein
GAN.