pwc-archive/papers-with-abstracts huggingface dataset:
Towards Meaningful Maps of Polish Case Law
Authors: Michal Jungiewicz, Michał Łopuszyński
Date: 2015-10-12 00:00:00

In this work, we analyze the utility of two dimensional document maps for
exploratory analysis of Polish case law. We start by comparing two methods of
generating such visualizations. First is based on linear principal component
analysis (PCA). Second makes use of the modern nonlinear t-Distributed
Stochastic Neighbor Embedding method (t-SNE). We apply both PCA and t-SNE to a
corpus of judgments from different courts in Poland. It emerges that t-SNE
provides better, more interpretable results than PCA. As a next test, we apply
t-SNE to randomly selected sample of common court judgments corresponding to
different keywords. We show that t-SNE, in this case, reveals hidden topical
structure of the documents related to keyword,,pension". In conclusion, we find
that the t-SNE method could be a promising tool to facilitate the exploitative
analysis of legal texts, e.g., by complementing search or browse functionality
in legal databases.
Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation
Authors: Jordi Pont-Tuset, Pablo Arbelaez, Jonathan T. Barron, Ferran Marques, Jitendra Malik
Date: 2015-03-03 00:00:00
Tasks: Image Segmentation, Object, Object Proposal Generation, Semantic Segmentation

We propose a unified approach for bottom-up hierarchical image segmentation
and object proposal generation for recognition, called Multiscale Combinatorial
Grouping (MCG). For this purpose, we first develop a fast normalized cuts
algorithm. We then propose a high-performance hierarchical segmenter that makes
effective use of multiscale information. Finally, we propose a grouping
strategy that combines our multiscale regions into highly-accurate object
proposals by exploring efficiently their combinatorial space. We also present
Single-scale Combinatorial Grouping (SCG), a faster version of MCG that
produces competitive proposals in under five second per image. We conduct an
extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD,
and COCO datasets, showing that MCG produces state-of-the-art contours,
hierarchical regions, and object proposals.
Order-Embeddings of Images and Language
Authors: Ivan Vendrov, Ryan Kiros, Sanja Fidler, Raquel Urtasun
Date: 2015-11-19 00:00:00
Tasks: Cross-Modal Retrieval, Image Captioning, Natural Language Inference, Retrieval

Hypernymy, textual entailment, and image captioning can be seen as special
cases of a single visual-semantic hierarchy over words, sentences, and images.
In this paper we advocate for explicitly modeling the partial order structure
of this hierarchy. Towards this goal, we introduce a general method for
learning ordered representations, and show how it can be applied to a variety
of tasks involving images and language. We show that the resulting
representations improve performance over current approaches for hypernym
prediction and image-caption retrieval.
Auto-JacoBin: Auto-encoder Jacobian Binary Hashing
Authors: Xiping Fu, Brendan McCane, Steven Mills, Michael Albert, Lech Szymanski
Date: 2016-02-25 00:00:00
Tasks: Retrieval

Binary codes can be used to speed up nearest neighbor search tasks in large
scale data sets as they are efficient for both storage and retrieval. In this
paper, we propose a robust auto-encoder model that preserves the geometric
relationships of high-dimensional data sets in Hamming space. This is done by
considering a noise-removing function in a region surrounding the manifold
where the training data points lie. This function is defined with the property
that it projects the data points near the manifold into the manifold wisely,
and we approximate this function by its first order approximation. Experimental
results show that the proposed method achieves better than state-of-the-art
results on three large scale high dimensional data sets.
GOGMA: Globally-Optimal Gaussian Mixture Alignment
Authors: Dylan Campbell, Lars Petersson
Date: 2016-03-01 00:00:00
Proceeding: CVPR 2016 6

Gaussian mixture alignment is a family of approaches that are frequently used
for robustly solving the point-set registration problem. However, since they
use local optimisation, they are susceptible to local minima and can only
guarantee local optimality. Consequently, their accuracy is strongly dependent
on the quality of the initialisation. This paper presents the first
globally-optimal solution to the 3D rigid Gaussian mixture alignment problem
under the L2 distance between mixtures. The algorithm, named GOGMA, employs a
branch-and-bound approach to search the space of 3D rigid motions SE(3),
guaranteeing global optimality regardless of the initialisation. The geometry
of SE(3) was used to find novel upper and lower bounds for the objective
function and local optimisation was integrated into the scheme to accelerate
convergence without voiding the optimality guarantee. The evaluation
empirically supported the optimality proof and showed that the method performed
much more robustly on two challenging datasets than an existing
globally-optimal registration solution.
Storm Detection by Visual Learning Using Satellite Images
Authors: Yu Zhang, Stephen Wistar, Jia Li, Michael Steinberg, James Z. Wang
Date: 2016-03-01 00:00:00
Tasks: Weather Forecasting

Computers are widely utilized in today's weather forecasting as a powerful
tool to leverage an enormous amount of data. Yet, despite the availability of
such data, current techniques often fall short of producing reliable detailed
storm forecasts. Each year severe thunderstorms cause significant damage and
loss of life, some of which could be avoided if better forecasts were
available. We propose a computer algorithm that analyzes satellite images from
historical archives to locate visual signatures of severe thunderstorms for
short-term predictions. While computers are involved in weather forecasts to
solve numerical models based on sensory data, they are less competent in
forecasting based on visual patterns from satellite images. In our system, we
extract and summarize important visual storm evidence from satellite image
sequences in the way that meteorologists interpret the images. In particular,
the algorithm extracts and fits local cloud motion from image sequences to
model the storm-related cloud patches. Image data from the year 2008 have been
adopted to train the model, and historical thunderstorm reports in continental
US from 2000 through 2013 have been used as the ground-truth and priors in the
modeling process. Experiments demonstrate the usefulness and potential of the
algorithm for producing more accurate thunderstorm forecasts.
A Universal Update-pacing Framework For Visual Tracking
Authors: Zexi Hu, Yuefang Gao, Dong Wang, Xuhong Tian
Date: 2016-03-01 00:00:00
Tasks: Visual Tracking

This paper proposes a novel framework to alleviate the model drift problem in
visual tracking, which is based on paced updates and trajectory selection.
Given a base tracker, an ensemble of trackers is generated, in which each
tracker's update behavior will be paced and then traces the target object
forward and backward to generate a pair of trajectories in an interval. Then,
we implicitly perform self-examination based on trajectory pair of each tracker
and select the most robust tracker. The proposed framework can effectively
leverage temporal context of sequential frames and avoid to learn corrupted
information. Extensive experiments on the standard benchmark suggest that the
proposed framework achieves superior performance against state-of-the-art
trackers.
Cascaded Subpatch Networks for Effective CNNs
Authors: Xiaoheng Jiang, Yanwei Pang, Manli Sun, Xuelong. Li
Date: 2016-03-01 00:00:00

Conventional Convolutional Neural Networks (CNNs) use either a linear or
non-linear filter to extract features from an image patch (region) of spatial
size $ H\times W $ (Typically, $ H $ is small and is equal to $ W$, e.g., $ H $
is 5 or 7). Generally, the size of the filter is equal to the size $ H\times W
$ of the input patch. We argue that the representation ability of equal-size
strategy is not strong enough. To overcome the drawback, we propose to use
subpatch filter whose spatial size $ h\times w $ is smaller than $ H\times W $.
The proposed subpatch filter consists of two subsequent filters. The first one
is a linear filter of spatial size $ h\times w $ and is aimed at extracting
features from spatial domain. The second one is of spatial size $ 1\times 1 $
and is used for strengthening the connection between different input feature
channels and for reducing the number of parameters. The subpatch filter
convolves with the input patch and the resulting network is called a subpatch
network. Taking the output of one subpatch network as input, we further repeat
constructing subpatch networks until the output contains only one neuron in
spatial domain. These subpatch networks form a new network called Cascaded
Subpatch Network (CSNet). The feature layer generated by CSNet is called csconv
layer. For the whole input image, we construct a deep neural network by
stacking a sequence of csconv layers. Experimental results on four benchmark
datasets demonstrate the effectiveness and compactness of the proposed CSNet.
For example, our CSNet reaches a test error of $ 5.68\% $ on the CIFAR10
dataset without model averaging. To the best of our knowledge, this is the best
result ever obtained on the CIFAR10 dataset.
Learning Multilayer Channel Features for Pedestrian Detection
Authors: Jiale Cao, Yanwei Pang, Xuelong. Li
Date: 2016-03-01 00:00:00
Tasks: Pedestrian Detection

Pedestrian detection based on the combination of Convolutional Neural Network
(i.e., CNN) and traditional handcrafted features (i.e., HOG+LUV) has achieved
great success. Generally, HOG+LUV are used to generate the candidate proposals
and then CNN classifies these proposals. Despite its success, there is still
room for improvement. For example, CNN classifies these proposals by the
full-connected layer features while proposal scores and the features in the
inner-layers of CNN are ignored. In this paper, we propose a unifying framework
called Multilayer Channel Features (MCF) to overcome the drawback. It firstly
integrates HOG+LUV with each layer of CNN into a multi-layer image channels.
Based on the multi-layer image channels, a multi-stage cascade AdaBoost is then
learned. The weak classifiers in each stage of the multi-stage cascade is
learned from the image channels of corresponding layer. With more abundant
features, MCF achieves the state-of-the-art on Caltech pedestrian dataset
(i.e., 10.40% miss rate). Using new and accurate annotations, MCF achieves
7.98% miss rate. As many non-pedestrian detection windows can be quickly
rejected by the first few stages, it accelerates detection speed by 1.43 times.
By eliminating the highly overlapped detection windows with lower scores after
the first stage, it's 4.07 times faster with negligible performance loss.
Sparse Multivariate Factor Regression
Authors: Milad Kharratzadeh, Mark Coates
Date: 2015-02-25 00:00:00
Tasks: Dimensionality Reduction, regression

We consider the problem of multivariate regression in a setting where the
relevant predictors could be shared among different responses. We propose an
algorithm which decomposes the coefficient matrix into the product of a long
matrix and a wide matrix, with an elastic net penalty on the former and an
$\ell_1$ penalty on the latter. The first matrix linearly transforms the
predictors to a set of latent factors, and the second one regresses the
responses on these factors. Our algorithm simultaneously performs dimension
reduction and coefficient estimation and automatically estimates the number of
latent factors from the data. Our formulation results in a non-convex
optimization problem, which despite its flexibility to impose effective
low-dimensional structure, is difficult, or even impossible, to solve exactly
in a reasonable time. We specify an optimization algorithm based on alternating
minimization with three different sets of updates to solve this non-convex
problem and provide theoretical results on its convergence and optimality.
Finally, we demonstrate the effectiveness of our algorithm via experiments on
simulated and real data.
Variational Auto-encoded Deep Gaussian Processes
Authors: Zhenwen Dai, Andreas Damianou, Javier González, Neil Lawrence
Date: 2015-11-19 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

We develop a scalable deep non-parametric generative model by augmenting deep
Gaussian processes with a recognition model. Inference is performed in a novel
scalable variational framework where the variational posterior distributions
are reparametrized through a multilayer perceptron. The key aspect of this
reformulation is that it prevents the proliferation of variational parameters
which otherwise grow linearly in proportion to the sample size. We derive a new
formulation of the variational lower bound that allows us to distribute most of
the computation in a way that enables to handle datasets of the size of
mainstream deep learning tasks. We show the efficacy of the method on a variety
of challenges including deep unsupervised learning and deep Bayesian
optimization.
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
Authors: Pouya Bashivan, Irina Rish, Mohammed Yeasin, Noel Codella
Date: 2015-11-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Time Series, Time Series Analysis, Video Classification

One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.
Density Modeling of Images using a Generalized Normalization Transformation
Authors: Johannes Ballé, Valero Laparra, Eero P. Simoncelli
Date: 2015-11-19 00:00:00

We introduce a parametric nonlinear transformation that is well-suited for
Gaussianizing data from natural images. The data are linearly transformed, and
each component is then normalized by a pooled activity measure, computed by
exponentiating a weighted sum of rectified and exponentiated components and a
constant. We optimize the parameters of the full transformation (linear
transform, exponents, weights, constant) over a database of natural images,
directly minimizing the negentropy of the responses. The optimized
transformation substantially Gaussianizes the data, achieving a significantly
smaller mutual information between transformed components than alternative
methods including ICA and radial Gaussianization. The transformation is
differentiable and can be efficiently inverted, and thus induces a density
model on images. We show that samples of this model are visually similar to
samples of natural image patches. We demonstrate the use of the model as a
prior probability density that can be used to remove additive noise. Finally,
we show that the transformation can be cascaded, with each layer optimized
using the same Gaussianization objective, thus offering an unsupervised method
of optimizing a deep network architecture.
Easy Monotonic Policy Iteration
Authors: Joshua Achiam
Date: 2016-02-29 00:00:00
Tasks: Reinforcement Learning

A key problem in reinforcement learning for control with general function
approximators (such as deep neural networks and other nonlinear functions) is
that, for many algorithms employed in practice, updates to the policy or
$Q$-function may fail to improve performance---or worse, actually cause the
policy performance to degrade. Prior work has addressed this for policy
iteration by deriving tight policy improvement bounds; by optimizing the lower
bound on policy improvement, a better policy is guaranteed. However, existing
approaches suffer from bounds that are hard to optimize in practice because
they include sup norm terms which cannot be efficiently estimated or
differentiated. In this work, we derive a better policy improvement bound where
the sup norm of the policy divergence has been replaced with an average
divergence; this leads to an algorithm, Easy Monotonic Policy Iteration, that
generates sequences of policies with guaranteed non-decreasing returns and is
easy to implement in a sample-based framework.
Continuous control with deep reinforcement learning
Authors: Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra
Date: 2015-09-09 00:00:00
Tasks: Action Detection, continuous-control, Continuous Control, Deep Reinforcement Learning, OpenAI Gym, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL), Speech Emotion Recognition

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.
Visual Representations: Defining Properties and Deep Approximations
Authors: Stefano Soatto, Alessandro Chiuso
Date: 2014-11-27 00:00:00

Visual representations are defined in terms of minimal sufficient statistics
of visual data, for a class of tasks, that are also invariant to nuisance
variability. Minimal sufficiency guarantees that we can store a representation
in lieu of raw data with smallest complexity and no performance loss on the
task at hand. Invariance guarantees that the statistic is constant with respect
to uninformative transformations of the data. We derive analytical expressions
for such representations and show they are related to feature descriptors
commonly used in computer vision, as well as to convolutional neural networks.
This link highlights the assumptions and approximations tacitly assumed by
these methods and explains empirical practices such as clamping, pooling and
joint normalization.
Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm
Authors: Paolo Campigotto, Christian Rudloff, Maximilian Leodolter, Dietmar Bauer
Date: 2016-02-29 00:00:00

Route choice in multimodal networks shows a considerable variation between
different individuals as well as the current situational context.
Personalization of recommendation algorithms are already common in many areas,
e.g., online retail. However, most online routing applications still provide
shortest distance or shortest travel-time routes only, neglecting individual
preferences as well as the current situation. Both aspects are of particular
importance in a multimodal setting as attractivity of some transportation modes
such as biking crucially depends on personal characteristics and exogenous
factors like the weather. This paper introduces the FAVourite rOUte
Recommendation (FAVOUR) approach to provide personalized, situation-aware route
proposals based on three steps: first, at the initialization stage, the user
provides limited information (home location, work place, mobility options,
sociodemographics) used to select one out of a small number of initial
profiles. Second, based on this information, a stated preference survey is
designed in order to sharpen the profile. In this step a mass preference prior
is used to encode the prior knowledge on preferences from the class identified
in step one. And third, subsequently the profile is continuously updated during
usage of the routing services. The last two steps use Bayesian learning
techniques in order to incorporate information from all contributing
individuals. The FAVOUR approach is presented in detail and tested on a small
number of survey participants. The experimental results on this real-world
dataset show that FAVOUR generates better-quality recommendations w.r.t.
alternative learning algorithms from the literature. In particular the
definition of the mass preference prior for initialization of step two is shown
to provide better predictions than a number of alternatives from the
literature.
Generating Images from Captions with Attention
Authors: Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
Date: 2015-11-09 00:00:00
Tasks: Image Generation, Retrieval, Text-to-Image Generation

Motivated by the recent progress in generative models, we introduce a model
that generates images from natural language descriptions. The proposed model
iteratively draws patches on a canvas, while attending to the relevant words in
the description. After training on Microsoft COCO, we compare our model with
several baseline generative models on image generation and retrieval tasks. We
demonstrate that our model produces higher quality samples than other
approaches and generates images with novel scene compositions corresponding to
previously unseen captions in the dataset.
On Complex Valued Convolutional Neural Networks
Authors: Nitzan Guberman
Date: 2016-02-29 00:00:00
Tasks: Cell Detection, Face Recognition, image-classification, Image Classification, object-detection, Object Detection

Convolutional neural networks (CNNs) are the cutting edge model for
supervised machine learning in computer vision. In recent years CNNs have
outperformed traditional approaches in many computer vision tasks such as
object detection, image classification and face recognition. CNNs are
vulnerable to overfitting, and a lot of research focuses on finding
regularization methods to overcome it. One approach is designing task specific
models based on prior knowledge.
  Several works have shown that properties of natural images can be easily
captured using complex numbers. Motivated by these works, we present a
variation of the CNN model with complex valued input and weights. We construct
the complex model as a generalization of the real model. Lack of order over the
complex field raises several difficulties both in the definition and in the
training of the network. We address these issues and suggest possible
solutions.
  The resulting model is shown to be a restricted form of a real valued CNN
with twice the parameters. It is sensitive to phase structure, and we suggest
it serves as a regularized model for problems where such structure is
important. This suggestion is verified empirically by comparing the performance
of a complex and a real network in the problem of cell detection. The two
networks achieve comparable results, and although the complex model is hard to
train, it is significantly less vulnerable to overfitting. We also demonstrate
that the complex network detects meaningful phase structure in the data.