pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Comprehensive Approach for Estimating Concept Semantic Similarity in WordNet
Authors: Xiao-gang Zhang, Shou-qian Sun, Ke-jun Zhang
Date: 2017-03-06 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Computation of semantic similarity between concepts is an important
foundation for many research works. This paper focuses on IC computing methods
and IC measures, which estimate the semantic similarities between concepts by
exploiting the topological parameters of the taxonomy. Based on analyzing
representative IC computing methods and typical semantic similarity measures,
we propose a new hybrid IC computing method. Through adopting the parameter
dhyp and lch, we utilize the new IC computing method and propose a novel
comprehensive measure of semantic similarity between concepts. An experiment
based on WordNet "is a" taxonomy has been designed to test representative
measures and our measure on benchmark dataset R&G, and the results show that
our measure can obviously improve the similarity accuracy. We evaluate the
proposed approach by comparing the correlation coefficients between five
measures and the artificial data. The results show that our proposal
outperforms the previous measures.
Cats and Captions vs. Creators and the Clock: Comparing Multimodal Content to Context in Predicting Relative Popularity
Authors: Jack Hessel, Lillian Lee, David Mimno
Date: 2017-03-06 00:00:00

The content of today's social media is becoming more and more rich,
increasingly mixing text, images, videos, and audio. It is an intriguing
research question to model the interplay between these different modes in
attracting user attention and engagement. But in order to pursue this study of
multimodal content, we must also account for context: timing effects, community
preferences, and social factors (e.g., which authors are already popular) also
affect the amount of feedback and reaction that social-media posts receive. In
this work, we separate out the influence of these non-content factors in
several ways. First, we focus on ranking pairs of submissions posted to the
same community in quick succession, e.g., within 30 seconds, this framing
encourages models to focus on time-agnostic and community-specific content
features. Within that setting, we determine the relative performance of author
vs. content features. We find that victory usually belongs to "cats and
captions," as visual and textual features together tend to outperform
identity-based features. Moreover, our experiments show that when considered in
isolation, simple unigram text features and deep neural network visual features
yield the highest accuracy individually, and that the combination of the two
modalities generally leads to the best accuracies overall.
Third-Person Imitation Learning
Authors: Bradly C. Stadie, Pieter Abbeel, Ilya Sutskever
Date: 2017-03-06 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) makes it possible to train agents capable of achieving sophisticated goals in complex and uncertain environments. A key difficulty in reinforcement learning is specifying a reward function for the agent to optimize. Traditionally, imitation learning in RL has been used to overcome this problem. Unfortunately, hitherto imitation learning methods tend to require that demonstrations are supplied in the first-person: the agent is provided with a sequence of states and a specification of the actions that it should have taken. While powerful, this kind of imitation learning is limited by the relatively hard problem of collecting first-person demonstrations. Humans address this problem by learning from third-person demonstrations: they observe other humans perform tasks, infer the task, and accomplish the same task themselves. In this paper, we present a method for unsupervised third-person imitation learning. Here third-person refers to training an agent to correctly achieve a simple goal in a simple environment when it is provided a demonstration of a teacher achieving the same goal but from a different viewpoint; and unsupervised refers to the fact that the agent receives only these third-person demonstrations, and is not provided a correspondence between teacher states and student states. Our methods primary insight is that recent advances from domain confusion can be utilized to yield domain agnostic features which are crucial during the training process. To validate our approach, we report successful experiments on learning from third-person demonstrations in a pointmass domain, a reacher domain, and inverted pendulum.
Viewpoint Selection for Photographing Architectures
Authors: Jingwu He, Linbo Wang, Wenzhe Zhou, Hongjie Zhang, Xiufen Cui, Yanwen Guo
Date: 2017-03-06 00:00:00
Tasks: 2k, Clustering

This paper studies the problem of how to choose good viewpoints for taking
photographs of architectures. We achieve this by learning from professional
photographs of world famous landmarks that are available on the Internet.
Unlike previous efforts devoted to photo quality assessment which mainly rely
on 2D image features, we show in this paper combining 2D image features
extracted from images with 3D geometric features computed on the 3D models can
result in more reliable evaluation of viewpoint quality. Specifically, we
collect a set of photographs for each of 15 world famous architectures as well
as their 3D models from the Internet. Viewpoint recovery for images is carried
out through an image-model registration process, after which a newly proposed
viewpoint clustering strategy is exploited to validate users' viewpoint
preferences when photographing landmarks. Finally, we extract a number of 2D
and 3D features for each image based on multiple visual and geometric cues and
perform viewpoint recommendation by learning from both 2D and 3D features using
a specifically designed SVM-2K multi-view learner, achieving superior
performance over using solely 2D or 3D features. We show the effectiveness of
the proposed approach through extensive experiments. The experiments also
demonstrate that our system can be used to recommend viewpoints for rendering
textured 3D models of buildings for the use of architectural design, in
addition to viewpoint evaluation of photographs and recommendation of
viewpoints for photographing architectures in practice.
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models
Authors: Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone
Date: 2016-10-18 00:00:00
Tasks: General Classification

We investigate the capabilities and limitations of Gaussian process models by
jointly exploring three complementary directions: (i) scalable and
statistically efficient inference; (ii) flexible kernels; and (iii) objective
functions for hyperparameter learning alternative to the marginal likelihood.
Our approach outperforms all previously reported GP methods on the standard
MNIST dataset; performs comparatively to previous kernel-based methods using
the RECTANGLES-IMAGE dataset; and breaks the 1% error-rate barrier in GP models
using the MNIST8M dataset, showing along the way the scalability of our method
at unprecedented scale for GP models (8 million observations) in classification
problems. Overall, our approach represents a significant breakthrough in kernel
methods and GP models, bridging the gap between deep learning approaches and
kernel machines.
Multi-Agent Cooperation and the Emergence of (Natural) Language
Authors: Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni
Date: 2016-12-21 00:00:00

The current mainstream approach to train natural language systems is to
expose them to large amounts of text. This passive learning is problematic if
we are interested in developing interactive machines, such as conversational
agents. We propose a framework for language learning that relies on multi-agent
communication. We study this learning in the context of referential games. In
these games, a sender and a receiver see a pair of images. The sender is told
one of them is the target and is allowed to send a message from a fixed,
arbitrary vocabulary to the receiver. The receiver must rely on this message to
identify the target. Thus, the agents develop their own language interactively
out of the need to communicate. We show that two networks with simple
configurations are able to learn to coordinate in the referential game. We
further explore how to make changes to the game environment to cause the "word
meanings" induced in the game to better reflect intuitive semantic properties
of the images. In addition, we present a simple strategy for grounding the
agents' code into natural language. Both of these are necessary steps towards
developing machines that are able to communicate with humans productively.
Diversified Texture Synthesis with Feed-forward Networks
Authors: Yijun Li, Chen Fang, Jimei Yang, Zhaowen Wang, Xin Lu, Ming-Hsuan Yang
Date: 2017-03-05 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Texture Synthesis

Recent progresses on deep discriminative and generative modeling have shown
promising results on texture synthesis. However, existing feed-forward based
methods trade off generality for efficiency, which suffer from many issues,
such as shortage of generality (i.e., build one network per texture), lack of
diversity (i.e., always produce visually identical output) and suboptimality
(i.e., generate less satisfying visual effects). In this work, we focus on
solving these issues for improved texture synthesis. We propose a deep
generative feed-forward network which enables efficient synthesis of multiple
textures within one single network and meaningful interpolation between them.
Meanwhile, a suite of important techniques are introduced to achieve better
convergence and diversity. With extensive experiments, we demonstrate the
effectiveness of the proposed model and techniques for synthesizing a large
number of textures and show its applications with the stylization.
Lie-Access Neural Turing Machines
Authors: Greg Yang, Alexander M. Rush
Date: 2016-11-09 00:00:00

External neural memory structures have recently become a popular tool for
algorithmic deep learning (Graves et al. 2014, Weston et al. 2014). These
models generally utilize differentiable versions of traditional discrete
memory-access structures (random access, stacks, tapes) to provide the storage
necessary for computational tasks. In this work, we argue that these neural
memory systems lack specific structure important for relative indexing, and
propose an alternative model, Lie-access memory, that is explicitly designed
for the neural setting. In this paradigm, memory is accessed using a continuous
head in a key-space manifold. The head is moved via Lie group actions, such as
shifts or rotations, generated by a controller, and memory access is performed
by linear smoothing in key space. We argue that Lie groups provide a natural
generalization of discrete memory structures, such as Turing machines, as they
provide inverse and identity operators while maintaining differentiability. To
experiment with this approach, we implement a simplified Lie-access neural
Turing machine (LANTM) with different Lie groups. We find that this approach is
able to perform well on a range of algorithmic tasks.
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Authors: Chris J. Maddison, andriy mnih, Yee Whye Teh
Date: 2016-11-02 00:00:00
Tasks: Density Estimation, Structured Prediction

The reparameterization trick enables optimizing large scale stochastic
computation graphs via gradient descent. The essence of the trick is to
refactor each stochastic node into a differentiable function of its parameters
and a random variable with fixed distribution. After refactoring, the gradients
of the loss propagated by the chain rule through the graph are low variance
unbiased estimators of the gradients of the expected loss. While many
continuous random variables have such reparameterizations, discrete random
variables lack useful reparameterizations due to the discontinuous nature of
discrete states. In this work we introduce Concrete random
variables---continuous relaxations of discrete random variables. The Concrete
distribution is a new family of distributions with closed form densities and a
simple reparameterization. Whenever a discrete stochastic node of a computation
graph can be refactored into a one-hot bit representation that is treated
continuously, Concrete stochastic nodes can be used with automatic
differentiation to produce low-variance biased gradients of objectives
(including objectives that depend on the log-probability of latent stochastic
nodes) on the corresponding discrete graph. We demonstrate the effectiveness of
Concrete relaxations on density estimation and structured prediction tasks
using neural networks.
Neural Machine Translation and Sequence-to-sequence Models: A Tutorial
Authors: Graham Neubig
Date: 2017-03-05 00:00:00
Tasks: Machine Translation, Math, Translation

This tutorial introduces a new and powerful set of techniques variously
called "neural machine translation" or "neural sequence-to-sequence models".
These techniques have been used in a number of tasks regarding the handling of
human language, and can be a powerful tool in the toolbox of anyone who wants
to model sequential data of some sort. The tutorial assumes that the reader
knows the basics of math and programming, but does not assume any particular
experience with neural networks or natural language processing. It attempts to
explain the intuition behind the various methods covered, then delves into them
with enough mathematical detail to understand them concretely, and culiminates
with a suggestion for an implementation exercise, where readers can test that
they understood the content in practice.
Learning Power Spectrum Maps from Quantized Power Measurements
Authors: Daniel Romero, Seung-Jun Kim, Georgios B. Giannakis, Roberto Lopez-Valcarce
Date: 2016-06-07 00:00:00
Tasks: Quantization

Power spectral density (PSD) maps providing the distribution of RF power
across space and frequency are constructed using power measurements collected
by a network of low-cost sensors. By introducing linear compression and
quantization to a small number of bits, sensor measurements can be communicated
to the fusion center with minimal bandwidth requirements. Strengths of data-
and model-driven approaches are combined to develop estimators capable of
incorporating multiple forms of spectral and propagation prior information
while fitting the rapid variations of shadow fading across space. To this end,
novel nonparametric and semiparametric formulations are investigated. It is
shown that PSD maps can be obtained using support vector machine-type solvers.
In addition to batch approaches, an online algorithm attuned to real-time
operation is developed. Numerical tests assess the performance of the novel
algorithms.
Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks
Authors: Martin Engelcke, Dushyant Rao, Dominic Zeng Wang, Chi Hay Tong, Ingmar Posner
Date: 2016-09-21 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection, Real-Time Object Detection

This paper proposes a computationally efficient approach to detecting objects
natively in 3D point clouds using convolutional neural networks (CNNs). In
particular, this is achieved by leveraging a feature-centric voting scheme to
implement novel convolutional layers which explicitly exploit the sparsity
encountered in the input. To this end, we examine the trade-off between
accuracy and speed for different architectures and additionally propose to use
an L1 penalty on the filter activations to further encourage sparsity in the
intermediate representations. To the best of our knowledge, this is the first
work to propose sparse convolutional layers and L1 regularisation for efficient
large-scale processing of 3D data. We demonstrate the efficacy of our approach
on the KITTI object detection benchmark and show that Vote3Deep models with as
few as three layers outperform the previous state of the art in both laser and
laser-vision based approaches by margins of up to 40% while remaining highly
competitive in terms of processing time.
A Theory of Output-Side Unsupervised Domain Adaptation
Authors: Tomer Galanti, Lior Wolf
Date: 2017-03-05 00:00:00
Tasks: Domain Adaptation, Generalization Bounds, Unsupervised Domain Adaptation

When learning a mapping from an input space to an output space, the
assumption that the sample distribution of the training data is the same as
that of the test data is often violated. Unsupervised domain shift methods
adapt the learned function in order to correct for this shift. Previous work
has focused on utilizing unlabeled samples from the target distribution. We
consider the complementary problem in which the unlabeled samples are given
post mapping, i.e., we are given the outputs of the mapping of unknown samples
from the shifted domain. Two other variants are also studied: the two sided
version, in which unlabeled samples are give from both the input and the output
spaces, and the Domain Transfer problem, which was recently formalized. In all
cases, we derive generalization bounds that employ discrepancy terms.
Random vector generation of a semantic space
Authors: Jean-François Delpech, Sabine Ploux
Date: 2017-03-05 00:00:00
Tasks: Text Generation

We show how random vectors and random projection can be implemented in the
usual vector space model to construct a Euclidean semantic space from a French
synonym dictionary. We evaluate theoretically the resulting noise and show the
experimental distribution of the similarities of terms in a neighborhood
according to the choice of parameters. We also show that the Schmidt
orthogonalization process is applicable and can be used to separate homonyms
with distinct semantic meanings. Neighboring terms are easily arranged into
semantically significant clusters which are well suited to the generation of
realistic lists of synonyms and to such applications as word selection for
automatic text generation. This process, applicable to any language, can easily
be extended to collocations, is extremely fast and can be updated in real time,
whenever new synonyms are proposed.
L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction
Authors: Yongwei Nie, Xu Cao, Chengjiang Long, Ping Li, Guiqing Li
Date: 2017-03-05 00:00:00
Tasks: Face Alignment

Current face alignment algorithms can robustly find a set of landmarks along
face contour. However, the landmarks are sparse and lack curve details,
especially in chin and cheek areas where a lot of concave-convex bending
information exists. In this paper, we propose a local to global seam cutting
and integrating algorithm (L2GSCI) to extract continuous and accurate face
contour. Our method works in three steps with the help of a rough initial
curve. First, we sample small and overlapped squares along the initial curve.
Second, the seam cutting part of L2GSCI extracts a local seam in each square
region. Finally, the seam integrating part of L2GSCI connects all the redundant
seams together to form a continuous and complete face curve. Overall, the
proposed method is much more straightforward than existing face alignment
algorithms, but can achieve pixel-level continuous face curves rather than
discrete and sparse landmarks. Moreover, experiments on two face benchmark
datasets (i.e., LFPW and HELEN) show that our method can precisely reveal
concave-convex bending details of face contours, which has significantly
improved the performance when compared with the state-ofthe- art face alignment
approaches.
Face Alignment with Cascaded Semi-Parametric Deep Greedy Neural Forests
Authors: Arnaud Dapogny, Kévin Bailly, Séverine Dubuisson
Date: 2017-03-05 00:00:00
Tasks: Face Alignment

Face alignment is an active topic in computer vision, consisting in aligning
a shape model on the face. To this end, most modern approaches refine the shape
in a cascaded manner, starting from an initial guess. Those shape updates can
either be applied in the feature point space (\textit{i.e.} explicit updates)
or in a low-dimensional, parametric space. In this paper, we propose a
semi-parametric cascade that first aligns a parametric shape, then captures
more fine-grained deformations of an explicit shape. For the purpose of
learning shape updates at each cascade stage, we introduce a deep greedy neural
forest (GNF) model, which is an improved version of deep neural forest (NF).
GNF appears as an ideal regressor for face alignment, as it combines
differentiability, high expressivity and fast evaluation runtime. The proposed
framework is very fast and achieves high accuracies on multiple challenging
benchmarks, including small, medium and large pose experiments.
Graph sampling with determinantal processes
Authors: Nicolas Tremblay, Pierre-Olivier Amblard, Simon Barthelmé
Date: 2017-03-05 00:00:00
Tasks: Graph Sampling, Point Processes

We present a new random sampling strategy for k-bandlimited signals defined
on graphs, based on determinantal point processes (DPP). For small graphs, ie,
in cases where the spectrum of the graph is accessible, we exhibit a DPP
sampling scheme that enables perfect recovery of bandlimited signals. For large
graphs, ie, in cases where the graph's spectrum is not accessible, we
investigate, both theoretically and empirically, a sub-optimal but much faster
DPP based on loop-erased random walks on the graph. Preliminary experiments
show promising results especially in cases where the number of measurements
should stay as small as possible and for graphs that have a strong community
structure. Our sampling scheme is efficient and can be applied to graphs with
up to $10^6$ nodes.
Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis
Authors: Andrew Stevens, Yunchen Pu, Yannan Sun, Greg Spell, Lawrence Carin
Date: 2016-12-08 00:00:00
Tasks: Denoising, Dictionary Learning, General Classification, image-classification, Image Classification

A multi-way factor analysis model is introduced for tensor-variate data of
any order. Each data item is represented as a (sparse) sum of Kruskal
decompositions, a Kruskal-factor analysis (KFA). KFA is nonparametric and can
infer both the tensor-rank of each dictionary atom and the number of dictionary
atoms. The model is adapted for online learning, which allows dictionary
learning on large data sets. After KFA is introduced, the model is extended to
a deep convolutional tensor-factor analysis, supervised by a Bayesian SVM. The
experiments section demonstrates the improvement of KFA over vectorized
approaches (e.g., BPFA), tensor decompositions, and convolutional neural
networks (CNN) in multi-way denoising, blind inpainting, and image
classification. The improvement in PSNR for the inpainting results over other
methods exceeds 1dB in several cases and we achieve state of the art results on
Caltech101 image classification.