pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Coordinate Descent for Generalized Linear Models with Regularization
Authors: Ilya Trofimov, Alexander Genkin
Date: 2016-11-07 00:00:00
Tasks: regression

Generalized linear model with $L_1$ and $L_2$ regularization is a widely used
technique for solving classification, class probability estimation and
regression problems. With the numbers of both features and examples growing
rapidly in the fields like text mining and clickstream data analysis
parallelization and the use of cluster architectures becomes important. We
present a novel algorithm for fitting regularized generalized linear models in
the distributed environment. The algorithm splits data between nodes by
features, uses coordinate descent on each node and line search to merge results
globally. Convergence proof is provided. A modifications of the algorithm
addresses slow node problem. For an important particular case of logistic
regression we empirically compare our program with several state-of-the art
approaches that rely on different algorithmic and data spitting methods.
Experiments demonstrate that our approach is scalable and superior when
training on large and sparse datasets.
GPU-acceleration for Large-scale Tree Boosting
Authors: Huan Zhang, Si Si, Cho-Jui Hsieh
Date: 2017-06-26 00:00:00
Tasks: CPU, GPU

In this paper, we present a novel massively parallel algorithm for
accelerating the decision tree building procedure on GPUs (Graphics Processing
Units), which is a crucial step in Gradient Boosted Decision Tree (GBDT) and
random forests training. Previous GPU based tree building algorithms are based
on parallel multi-scan or radix sort to find the exact tree split, and thus
suffer from scalability and performance issues. We show that using a histogram
based algorithm to approximately find the best split is more efficient and
scalable on GPU. By identifying the difference between classical GPU-based
image histogram construction and the feature histogram construction in decision
tree training, we develop a fast feature histogram building kernel on GPU with
carefully designed computational and memory access sequence to reduce atomic
update conflict and maximize GPU utilization. Our algorithm can be used as a
drop-in replacement for histogram construction in popular tree boosting systems
to improve their scalability. As an example, to train GBDT on epsilon dataset,
our method using a main-stream GPU is 7-8 times faster than histogram based
algorithm on CPU in LightGBM and 25 times faster than the exact-split finding
algorithm in XGBoost on a dual-socket 28-core Xeon server, while achieving
similar prediction accuracy.
Deep Semantic Classification for 3D LiDAR Data
Authors: Ayush Dewan, Gabriel L. Oliveira, Wolfram Burgard
Date: 2017-06-26 00:00:00
Tasks: Classification, General Classification

Robots are expected to operate autonomously in dynamic environments.
Understanding the underlying dynamic characteristics of objects is a key
enabler for achieving this goal. In this paper, we propose a method for
pointwise semantic classification of 3D LiDAR data into three classes:
non-movable, movable and dynamic. We concentrate on understanding these
specific semantics because they characterize important information required for
an autonomous system. Non-movable points in the scene belong to unchanging
segments of the environment, whereas the remaining classes corresponds to the
changing parts of the scene. The difference between the movable and dynamic
class is their motion state. The dynamic points can be perceived as moving,
whereas movable objects can move, but are perceived as static. To learn the
distinction between movable and non-movable points in the environment, we
introduce an approach based on deep neural network and for detecting the
dynamic points, we estimate pointwise motion. We propose a Bayes filter
framework for combining the learned semantic cues with the motion cues to infer
the required semantic classification. In extensive experiments, we compare our
approach with other methods on a standard benchmark dataset and report
competitive results in comparison to the existing state-of-the-art.
Furthermore, we show an improvement in the classification of points by
combining the semantic cues retrieved from the neural network with the motion
cues.
Sharper Bounds for Regularized Data Fitting
Authors: Haim Avron, Kenneth L. Clarkson, David P. Woodruff
Date: 2016-11-10 00:00:00

We study matrix sketching methods for regularized variants of linear
regression, low rank approximation, and canonical correlation analysis. Our
main focus is on sketching techniques which preserve the objective function
value for regularized problems, which is an area that has remained largely
unexplored. We study regularization both in a fairly broad setting, and in the
specific context of the popular and widely used technique of ridge
regularization; for the latter, as applied to each of these problems, we show
algorithmic resource bounds in which the {\em statistical dimension} appears in
places where in previous bounds the rank would appear. The statistical
dimension is always smaller than the rank, and decreases as the amount of
regularization increases. In particular, for the ridge low-rank approximation
problem $\min_{Y,X} \lVert YX - A \rVert_F^2 + \lambda \lVert Y\rVert_F^2 +
\lambda\lVert X \rVert_F^2$, where $Y\in\mathbb{R}^{n\times k}$ and
$X\in\mathbb{R}^{k\times d}$, we give an approximation algorithm needing \[
O(\mathtt{nnz}(A)) + \tilde{O}((n+d)\varepsilon^{-1}k \min\{k,
\varepsilon^{-1}\mathtt{sd}_\lambda(Y^*)\})+
\mathtt{poly}(\mathtt{sd}_\lambda(Y^*) \varepsilon^{-1}) \] time, where
$s_{\lambda}(Y^*)\le k$ is the statistical dimension of $Y^*$, $Y^*$ is an
optimal $Y$, $\varepsilon$ is an error parameter, and $\mathtt{nnz}(A)$ is the
number of nonzero entries of $A$.This is faster than prior work, even when
$\lambda=0$.
  We also study regularization in a much more general setting. For example, we
obtain sketching-based algorithms for the low-rank approximation problem
$\min_{X,Y} \lVert YX - A \rVert_F^2 + f(Y,X)$ where $f(\cdot,\cdot)$ is a
regularizing function satisfying some very general conditions (chiefly,
invariance under orthogonal transformations).
Semantically Informed Multiview Surface Refinement
Authors: Maros Blaha, Mathias Rothermel, Martin R. Oswald, Torsten Sattler, Audrey Richard, Jan D. Wegner, Marc Pollefeys, Konrad Schindler
Date: 2017-06-26 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D geometry, 3D Reconstruction, Segmentation, Semantic Segmentation

We present a method to jointly refine the geometry and semantic segmentation
of 3D surface meshes. Our method alternates between updating the shape and the
semantic labels. In the geometry refinement step, the mesh is deformed with
variational energy minimization, such that it simultaneously maximizes
photo-consistency and the compatibility of the semantic segmentations across a
set of calibrated images. Label-specific shape priors account for interactions
between the geometry and the semantic labels in 3D. In the semantic
segmentation step, the labels on the mesh are updated with MRF inference, such
that they are compatible with the semantic segmentations in the input images.
Also, this step includes prior assumptions about the surface shape of different
semantic classes. The priors induce a tight coupling, where semantic
information influences the shape update and vice versa. Specifically, we
introduce priors that favor (i) adaptive smoothing, depending on the class
label; (ii) straightness of class boundaries; and (iii) semantic labels that
are consistent with the surface orientation. The novel mesh-based
reconstruction is evaluated in a series of experiments with real and synthetic
data. We compare both to state-of-the-art, voxel-based semantic 3D
reconstruction, and to purely geometric mesh refinement, and demonstrate that
the proposed scheme yields improved 3D geometry as well as an improved semantic
segmentation.
Effective Evaluation using Logged Bandit Feedback from Multiple Loggers
Authors: Aman Agarwal, Soumya Basu, Tobias Schnabel, Thorsten Joachims
Date: 2017-03-17 00:00:00
Tasks: counterfactual

Accurately evaluating new policies (e.g. ad-placement models, ranking
functions, recommendation functions) is one of the key prerequisites for
improving interactive systems. While the conventional approach to evaluation
relies on online A/B tests, recent work has shown that counterfactual
estimators can provide an inexpensive and fast alternative, since they can be
applied offline using log data that was collected from a different policy
fielded in the past. In this paper, we address the question of how to estimate
the performance of a new target policy when we have log data from multiple
historic policies. This question is of great relevance in practice, since
policies get updated frequently in most online systems. We show that naively
combining data from multiple logging policies can be highly suboptimal. In
particular, we find that the standard Inverse Propensity Score (IPS) estimator
suffers especially when logging and target policies diverge -- to a point where
throwing away data improves the variance of the estimator. We therefore propose
two alternative estimators which we characterize theoretically and compare
experimentally. We find that the new estimators can provide substantially
improved estimation accuracy.
An adaptive prefix-assignment technique for symmetry reduction
Authors: Tommi Junttila, Matti Karppa, Petteri Kaski, Jukka Kohonen
Date: 2017-06-26 00:00:00

This paper presents a technique for symmetry reduction that adaptively
assigns a prefix of variables in a system of constraints so that the generated
prefix-assignments are pairwise nonisomorphic under the action of the symmetry
group of the system. The technique is based on McKay's canonical extension
framework [J.~Algorithms 26 (1998), no.~2, 306--324]. Among key features of the
technique are (i) adaptability---the prefix sequence can be user-prescribed and
truncated for compatibility with the group of symmetries; (ii)
parallelizability---prefix-assignments can be processed in parallel
independently of each other; (iii) versatility---the method is applicable
whenever the group of symmetries can be concisely represented as the
automorphism group of a vertex-colored graph; and (iv) implementability---the
method can be implemented relying on a canonical labeling map for
vertex-colored graphs as the only nontrivial subroutine. To demonstrate the
practical applicability of our technique, we have prepared an experimental
open-source implementation of the technique and carry out a set of experiments
that demonstrate ability to reduce symmetry on hard instances. Furthermore, we
demonstrate that the implementation effectively parallelizes to compute
clusters with multiple nodes via a message-passing interface.
Handling PDDL3.0 State Trajectory Constraints with Temporal Landmarks
Authors: Eliseo Marzal, Mohannad Babli, Eva Onaindia, Laura Sebastia
Date: 2017-06-26 00:00:00

Temporal landmarks have been proved to be a helpful mechanism to deal with
temporal planning problems, specifically to improve planners performance and
handle problems with deadline constraints. In this paper, we show the strength
of using temporal landmarks to handle the state trajectory constraints of
PDDL3.0. We analyze the formalism of TempLM, a temporal planner particularly
aimed at solving planning problems with deadlines, and we present a detailed
study that exploits the underlying temporal landmark-based mechanism of TempLM
for representing and reasoning with trajectory constraints.
Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data
Authors: Jiuyong Li, Lin Liu, Jixue Liu, Ryan Green
Date: 2016-12-18 00:00:00
Tasks: General Classification

It is common that a trained classification model is applied to the operating
data that is deviated from the training data because of noise. This paper
demonstrates that an ensemble classifier, Diversified Multiple Tree (DMT), is
more robust in classifying noisy data than other widely used ensemble methods.
DMT is tested on three real world biomedical data sets from different
laboratories in comparison with four benchmark ensemble classifiers.
Experimental results show that DMT is significantly more accurate than other
benchmark ensemble classifiers on noisy test data. We also discuss a limitation
of DMT and its possible variations.
On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation
Authors: Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Luigi Di Stefano, Philip H. S. Torr
Date: 2017-02-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Camera Relocalization, regression

Camera relocalisation is an important problem in computer vision, with
applications in simultaneous localisation and mapping, virtual/augmented
reality and navigation. Common techniques either match the current image
against keyframes with known poses coming from a tracker, or establish 2D-to-3D
correspondences between keypoints in the current image and points in the scene
in order to estimate the camera pose. Recently, regression forests have become
a popular alternative to establish such correspondences. They achieve accurate
results, but must be trained offline on the target scene, preventing
relocalisation in new environments. In this paper, we show how to circumvent
this limitation by adapting a pre-trained forest to a new scene on the fly. Our
adapted forests achieve relocalisation performance that is on par with that of
offline forests, and our approach runs in under 150ms, making it desirable for
real-time systems that require online relocalisation.
Grounded Language Learning in a Simulated 3D World
Authors: Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, Phil Blunsom
Date: 2017-06-20 00:00:00
Tasks: Grounded language learning

We are increasingly surrounded by artificially intelligent technology that
takes decisions and executes actions on our behalf. This creates a pressing
need for general means to communicate with, instruct and guide artificial
agents, with human language the most compelling means for such communication.
To achieve this in a scalable fashion, agents must be able to relate language
to the world and to actions; that is, their understanding of language must be
grounded and embodied. However, learning grounded language is a notoriously
challenging problem in artificial intelligence research. Here we present an
agent that learns to interpret language in a simulated 3D environment where it
is rewarded for the successful execution of written instructions. Trained via a
combination of reinforcement and unsupervised learning, and beginning with
minimal prior knowledge, the agent learns to relate linguistic symbols to
emergent perceptual representations of its physical surroundings and to
pertinent sequences of actions. The agent's comprehension of language extends
beyond its prior experience, enabling it to apply familiar language to
unfamiliar situations and to interpret entirely novel instructions. Moreover,
the speed with which this agent learns new words increases as its semantic
knowledge grows. This facility for generalising and bootstrapping semantic
knowledge indicates the potential of the present approach for reconciling
ambiguous natural language with the complexity of the physical world.
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates
Authors: Jun Liu, Amir Shahroudy, Dong Xu, Alex C. Kot, Gang Wang
Date: 2017-06-26 00:00:00
Tasks: Action Recognition, One-Shot 3D Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Skeleton-based human action recognition has attracted a lot of research
attention during the past few years. Recent works attempted to utilize
recurrent neural networks to model the temporal dependencies between the 3D
positional configurations of human body joints for better analysis of human
activities in the skeletal data. The proposed work extends this idea to spatial
domain as well as temporal domain to better analyze the hidden sources of
action-related information within the human skeleton sequences in both of these
domains simultaneously. Based on the pictorial structure of Kinect's skeletal
data, an effective tree-structure based traversal framework is also proposed.
In order to deal with the noise in the skeletal data, a new gating mechanism
within LSTM module is introduced, with which the network can learn the
reliability of the sequential data and accordingly adjust the effect of the
input data on the updating procedure of the long-term context representation
stored in the unit's memory cell. Moreover, we introduce a novel multi-modal
feature fusion strategy within the LSTM unit in this paper. The comprehensive
experimental results on seven challenging benchmark datasets for human action
recognition demonstrate the effectiveness of the proposed method.
Efficient Manifold and Subspace Approximations with Spherelets
Authors: Didong Li, Minerva Mukhopadhyay, David B. Dunson
Date: 2017-06-26 00:00:00
Tasks: Clustering, Data Compression, Denoising, Dimensionality Reduction

In statistical dimensionality reduction, it is common to rely on the assumption that high dimensional data tend to concentrate near a lower dimensional manifold. There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction. Most of the literature relies on linear or locally linear approximations. In this article, we propose a simple and general alternative, which instead uses spheres, an approach we refer to as spherelets. We develop spherical principal components analysis (SPCA), and provide theory on the convergence rate for global and local SPCA, while showing that spherelets can provide lower covering numbers and MSEs for many manifolds. Results relative to state-of-the-art competitors show gains in ability to accurately approximate manifolds with fewer components. Unlike most competitors, which simply output lower-dimensional features, our approach projects data onto the estimated manifold to produce fitted values that can be used for model assessment and cross validation. The methods are illustrated with applications to multiple data sets.
Deep Semantics-Aware Photo Adjustment
Authors: Seonghyeon Nam, Seon Joo Kim
Date: 2017-06-26 00:00:00
Tasks: Photo Retouching, Scene Parsing

Automatic photo adjustment is to mimic the photo retouching style of
professional photographers and automatically adjust photos to the learned
style. There have been many attempts to model the tone and the color adjustment
globally with low-level color statistics. Also, spatially varying photo
adjustment methods have been studied by exploiting high-level features and
semantic label maps. Those methods are semantics-aware since the color mapping
is dependent on the high-level semantic context. However, their performance is
limited to the pre-computed hand-crafted features and it is hard to reflect
user's preference to the adjustment. In this paper, we propose a deep neural
network that models the semantics-aware photo adjustment. The proposed network
exploits bilinear models that are the multiplicative interaction of the color
and the contexual features. As the contextual features we propose the semantic
adjustment map, which discovers the inherent photo retouching presets that are
applied according to the scene context. The proposed method is trained using a
robust loss with a scene parsing task. The experimental results show that the
proposed method outperforms the existing method both quantitatively and
qualitatively. The proposed method also provides users a way to retouch the
photo by their own likings by giving customized adjustment maps.
Can Computers overcome Humans? Consciousness interaction and its implications
Authors: Camilo Miguel Signorelli
Date: 2017-06-07 00:00:00

Can computers overcome human capabilities? This is a paradoxical and
controversial question, particularly because there are many hidden assumptions.
This article focuses on that issue putting on evidence some misconception
related with future generations of machines and the understanding of the brain.
It will be discussed to what extent computers might reach human capabilities,
and how it could be possible only if the computer is a conscious machine.
However, it will be shown that if the computer is conscious, an interference
process due to consciousness would affect the information processing of the
system. Therefore, it might be possible to make conscious machines to overcome
human capabilities, which will have limitations as well as humans. In other
words, trying to overcome human capabilities with computers implies the
paradoxical conclusion that a computer will never overcome human capabilities
at all, or if the computer does, it should not be considered as a computer
anymore.
Lensless computational imaging through deep learning
Authors: Ayan Sinha, Justin Lee, Shuai Li, George Barbastathis
Date: 2017-02-22 00:00:00
Tasks: Deep Learning, General Classification

Deep learning has been proven to yield reliably generalizable answers to
numerous classification and decision tasks. Here, we demonstrate for the first
time, to our knowledge, that deep neural networks (DNNs) can be trained to
solve inverse problems in computational imaging. We experimentally demonstrate
a lens-less imaging system where a DNN was trained to recover a phase object
given a raw intensity image recorded some distance away.
The Fog of War: A Machine Learning Approach to Forecasting Weather on Mars
Authors: Daniele Bellutta
Date: 2017-06-26 00:00:00
Tasks: BIG-bench Machine Learning

For over a decade, scientists at NASA's Jet Propulsion Laboratory (JPL) have
been recording measurements from the Martian surface as a part of the Mars
Exploration Rovers mission. One quantity of interest has been the opacity of
Mars's atmosphere for its importance in day-to-day estimations of the amount of
power available to the rover from its solar arrays. This paper proposes the use
of neural networks as a method for forecasting Martian atmospheric opacity that
is more effective than the current empirical model. The more accurate
prediction provided by these networks would allow operators at JPL to make more
accurate predictions of the amount of energy available to the rover when they
plan activities for coming sols.
Between Homomorphic Signal Processing and Deep Neural Networks: Constructing Deep Algorithms for Polyphonic Music Transcription
Authors: Li Su
Date: 2017-06-26 00:00:00
Tasks: Music Transcription, Relation

This paper presents a new approach in understanding how deep neural networks
(DNNs) work by applying homomorphic signal processing techniques. Focusing on
the task of multi-pitch estimation (MPE), this paper demonstrates the
equivalence relation between a generalized cepstrum and a DNN in terms of their
structures and functionality. Such an equivalence relation, together with pitch
perception theories and the recently established
rectified-correlations-on-a-sphere (RECOS) filter analysis, provide an
alternative way in explaining the role of the nonlinear activation function and
the multi-layer structure, both of which exist in a cepstrum and a DNN. To
validate the efficacy of this new approach, a new feature designed in the same
fashion is proposed for pitch salience function. The new feature outperforms
the one-layer spectrum in the MPE task and, as predicted, it addresses the
issue of the missing fundamental effect and also achieves better robustness to
noise.