pwc-archive/papers-with-abstracts huggingface dataset:
Developing a concept-level knowledge base for sentiment analysis in Singlish
Authors: Rajiv Bajpai, Soujanya Poria, Danyun Ho, Erik Cambria
Date: 2017-07-14 00:00:00
Tasks: Common Sense Reasoning, Graph Mining, Sentiment Analysis

In this paper, we present Singlish sentiment lexicon, a concept-level
knowledge base for sentiment analysis that associates multiword expressions to
a set of emotion labels and a polarity value. Unlike many other sentiment
analysis resources, this lexicon is not built by manually labeling pieces of
knowledge coming from general NLP resources such as WordNet or DBPedia.
Instead, it is automatically constructed by applying graph-mining and
multi-dimensional scaling techniques on the affective common-sense knowledge
collected from three different sources. This knowledge is represented
redundantly at three levels: semantic network, matrix, and vector space.
Subsequently, the concepts are labeled by emotions and polarity through the
ensemble application of spreading activation, neural networks and an emotion
categorization model.
Inner-Scene Similarities as a Contextual Cue for Object Detection
Authors: Noa Arbel, Tamar Avraham, Michael Lindenbaum
Date: 2017-07-14 00:00:00
Tasks: object-detection, Object Detection

Using image context is an effective approach for improving object detection.
Previously proposed methods used contextual cues that rely on semantic or
spatial information. In this work, we explore a different kind of contextual
information: inner-scene similarity. We present the CISS (Context by Inner
Scene Similarity) algorithm, which is based on the observation that two
visually similar sub-image patches are likely to share semantic identities,
especially when both appear in the same image. CISS uses base-scores provided
by a base detector and performs as a post-detection stage. For each candidate
sub-image (denoted anchor), the CISS algorithm finds a few similar sub-images
(denoted supporters), and, using them, calculates a new enhanced score for the
anchor. This is done by utilizing the base-scores of the supporters and a
pre-trained dependency model. The new scores are modeled as a linear function
of the base scores of the anchor and the supporters and is estimated using a
minimum mean square error optimization. This approach results in: (a) improved
detection of partly occluded objects (when there are similar non-occluded
objects in the scene), and (b) fewer false alarms (when the base detector
mistakenly classifies a background patch as an object). This work relates to
Duncan and Humphreys' "similarity theory," a psychophysical study. which
suggested that the human visual system perceptually groups similar image
regions and that the classification of one region is affected by the estimated
identity of the other. Experimental results demonstrate the enhancement of a
base detector's scores on the PASCAL VOC dataset.
TAPAS: Two-pass Approximate Adaptive Sampling for Softmax
Authors: Yu Bai, Sally Goldman, Li Zhang
Date: 2017-07-10 00:00:00
Tasks: General Classification, Multi-class Classification, Vocal Bursts Valence Prediction

TAPAS is a novel adaptive sampling method for the softmax model. It uses a
two pass sampling strategy where the examples used to approximate the gradient
of the partition function are first sampled according to a squashed population
distribution and then resampled adaptively using the context and current model.
We describe an efficient distributed implementation of TAPAS. We show, on both
synthetic data and a large real dataset, that TAPAS has low computational
overhead and works well for minimizing the rank loss for multi-class
classification problems with a very large label space.
A commentary on "The now-or-never bottleneck: a fundamental constraint on language", by Christiansen and Chater (2016)
Authors: Ramon Ferrer-i-Cancho
Date: 2015-09-05 00:00:00

In a recent article, Christiansen and Chater (2016) present a fundamental
constraint on language, i.e. a now-or-never bottleneck that arises from our
fleeting memory, and explore its implications, e.g., chunk-and-pass processing,
outlining a framework that promises to unify different areas of research. Here
we explore additional support for this constraint and suggest further
connections from quantitative linguistics and information theory.
f-GANs in an Information Geometric Nutshell
Authors: Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson
Date: 2017-07-14 00:00:00
Proceeding: NeurIPS 2017 12

Nowozin \textit{et al} showed last year how to extend the GAN
\textit{principle} to all $f$-divergences. The approach is elegant but falls
short of a full description of the supervised game, and says little about the
key player, the generator: for example, what does the generator actually
converge to if solving the GAN game means convergence in some space of
parameters? How does that provide hints on the generator's design and compare
to the flourishing but almost exclusively experimental literature on the
subject?
  In this paper, we unveil a broad class of distributions for which such
convergence happens --- namely, deformed exponential families, a wide superset
of exponential families --- and show tight connections with the three other key
GAN parameters: loss, game and architecture. In particular, we show that
current deep architectures are able to factorize a very large number of such
densities using an especially compact design, hence displaying the power of
deep architectures and their concinnity in the $f$-GAN game. This result holds
given a sufficient condition on \textit{activation functions} --- which turns
out to be satisfied by popular choices. The key to our results is a variational
generalization of an old theorem that relates the KL divergence between regular
exponential families and divergences between their natural parameters. We
complete this picture with additional results and experimental insights on how
these results may be used to ground further improvements of GAN architectures,
via (i) a principled design of the activation functions in the generator and
(ii) an explicit integration of proper composite losses' link function in the
discriminator.
Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches
Authors: Ben Curran, Kyle Higham, Elisenda Ortiz, Demival Vasques Filho
Date: 2017-07-11 00:00:00
Tasks: Time Series Analysis

Quantitative methods to measure the participation to parliamentary debate and
discourse of elected Members of Parliament (MPs) and the parties they belong to
are lacking. This is an exploratory study in which we propose the development
of a new approach for a quantitative analysis of such participation. We utilize
the New Zealand government's digital Hansard database to construct a topic
model of parliamentary speeches consisting of nearly 40 million words in the
period 2003-2016. A Latent Dirichlet Allocation topic model is implemented in
order to reveal the thematic structure of our set of documents. This generative
statistical model enables the detection of major themes or topics that are
publicly discussed in the New Zealand parliament, as well as permitting their
classification by MP. Information on topic proportions is subsequently analyzed
using a combination of statistical methods. We observe patterns arising from
time-series analysis of topic frequencies which can be related to specific
social, economic and legislative events. We then construct a bipartite network
representation, linking MPs to topics, for each of four parliamentary terms in
this time frame. We build projected networks (onto the set of nodes represented
by MPs) and proceed to the study of the dynamical changes of their topology,
including community structure. By performing this longitudinal network
analysis, we can observe the evolution of the New Zealand parliamentary topic
network and its main parties in the period studied.
The Reversible Residual Network: Backpropagation Without Storing Activations
Authors: Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse
Date: 2017-07-14 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: General Classification, image-classification, Image Classification

Deep residual networks (ResNets) have significantly pushed forward the
state-of-the-art on image classification, increasing in performance as networks
grow both deeper and wider. However, memory consumption becomes a bottleneck,
as one needs to store the activations in order to calculate gradients using
backpropagation. We present the Reversible Residual Network (RevNet), a variant
of ResNets where each layer's activations can be reconstructed exactly from the
next layer's. Therefore, the activations for most layers need not be stored in
memory during backpropagation. We demonstrate the effectiveness of RevNets on
CIFAR-10, CIFAR-100, and ImageNet, establishing nearly identical classification
accuracy to equally-sized ResNets, even though the activation storage
requirements are independent of depth.
Joint Pose and Principal Curvature Refinement Using Quadrics
Authors: Andrew Spek, Tom Drummond
Date: 2017-07-03 00:00:00

In this paper we present a novel joint approach for optimising surface
curvature and pose alignment. We present two implementations of this joint
optimisation strategy, including a fast implementation that uses two frames and
an offline multi-frame approach. We demonstrate an order of magnitude
improvement in simulation over state of the art dense relative point-to-plane
Iterative Closest Point (ICP) pose alignment using our dense joint
frame-to-frame approach and show comparable pose drift to dense point-to-plane
ICP bundle adjustment using low-cost depth sensors. Additionally our improved
joint quadric based approach can be used to more accurately estimate surface
curvature on noisy point clouds than previous approaches.
A Fast Method For Computing Principal Curvatures From Range Images
Authors: Andrew Spek, Wai Ho Li, Tom Drummond
Date: 2017-07-03 00:00:00
Tasks: GPU, Object Recognition, Robotic Grasping, Semantic Segmentation

Estimation of surface curvature from range data is important for a range of
tasks in computer vision and robotics, object segmentation, object recognition
and robotic grasping estimation. This work presents a fast method of robustly
computing accurate metric principal curvature values from noisy point clouds
which was implemented on GPU. In contrast to existing readily available
solutions which first differentiate the surface to estimate surface normals and
then differentiate these to obtain curvature, amplifying noise, our method
iteratively fits parabolic quadric surface patches to the data. Additionally
previous methods with a similar formulation use less robust techniques less
applicable to a high noise sensor. We demonstrate that our method is fast and
provides better curvature estimates than existing techniques. In particular we
compare our method to several alternatives to demonstrate the improvement.
Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
Authors: Md. Ashad Alam, Hui-Yi Lin, Vince Calhoun, Yu-Ping Wang
Date: 2017-07-14 00:00:00
Tasks: Triplet

In this study, we tested the interaction effect of multimodal datasets using
a novel method called the kernel method for detecting higher order interactions
among biologically relevant mulit-view data. Using a semiparametric method on a
reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects
linear model and derived a score-based variance component statistic that tests
for higher order interactions between multi-view data. The proposed method
offers an intangible framework for the identification of higher order
interaction effects (e.g., three way interaction) between genetics, brain
imaging, and epigenetic data. Extensive numerical simulation studies were first
conducted to evaluate the performance of this method. Finally, this method was
evaluated using data from the Mind Clinical Imaging Consortium (MCIC) including
single nucleotide polymorphism (SNP) data, functional magnetic resonance
imaging (fMRI) scans, and deoxyribonucleic acid (DNA) methylation data,
respectfully, in schizophrenia patients and healthy controls. We treated each
gene-derived SNPs, region of interest (ROI) and gene-derived DNA methylation as
a single testing unit, which are combined into triplets for evaluation. In
addition, cardiovascular disease risk factors such as age, gender, and body
mass index were assessed as covariates on hippocampal volume and compared
between triplets. Our method identified $13$-triplets ($p$-values $\leq 0.001$)
that included $6$ gene-derived SNPs, $10$ ROIs, and $6$ gene-derived DNA
methylations that correlated with changes in hippocampal volume, suggesting
that these triplets may be important in explaining schizophrenia-related
neurodegeneration. With strong evidence ($p$-values $\leq 0.000001$), the
triplet ({\bf MAGI2, CRBLCrus1.L, FBXO28}) has the potential to distinguish
schizophrenia patients from the healthy control variations.
Deep API Learning
Authors: Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, Sunghun Kim
Date: 2016-05-27 00:00:00
Tasks: Decoder, Information Retrieval, Language Modeling, Language Modelling, Retrieval, Word Alignment

Developers often wonder how to implement a certain functionality (e.g., how
to parse XML files) using APIs. Obtaining an API usage sequence based on an
API-related natural language query is very helpful in this regard. Given a
query, existing approaches utilize information retrieval models to search for
matching API sequences. These approaches treat queries and APIs as bag-of-words
(i.e., keyword matching or word-to-word alignment) and lack a deep
understanding of the semantics of the query.
  We propose DeepAPI, a deep learning based approach to generate API usage
sequences for a given natural language query. Instead of a bags-of-words
assumption, it learns the sequence of words in a query and the sequence of
associated APIs. DeepAPI adapts a neural language model named RNN
Encoder-Decoder. It encodes a word sequence (user query) into a fixed-length
context vector, and generates an API sequence based on the context vector. We
also augment the RNN Encoder-Decoder by considering the importance of
individual APIs. We empirically evaluate our approach with more than 7 million
annotated code snippets collected from GitHub. The results show that our
approach generates largely accurate API sequences and outperforms the related
approaches.
Key-Value Retrieval Networks for Task-Oriented Dialogue
Authors: Mihail Eric, Christopher D. Manning
Date: 2017-05-15 00:00:00
Proceeding: WS 2017 8
Tasks: Information Retrieval, Retrieval, Scheduling, Task-Oriented Dialogue Systems

Neural task-oriented dialogue systems often struggle to smoothly interface
with a knowledge base. In this work, we seek to address this problem by
proposing a new neural dialogue agent that is able to effectively sustain
grounded, multi-domain discourse through a novel key-value retrieval mechanism.
The model is end-to-end differentiable and does not need to explicitly model
dialogue state or belief trackers. We also release a new dataset of 3,031
dialogues that are grounded through underlying knowledge bases and span three
distinct tasks in the in-car personal assistant space: calendar scheduling,
weather information retrieval, and point-of-interest navigation. Our
architecture is simultaneously trained on data from all domains and
significantly outperforms a competitive rule-based system and other existing
neural dialogue architectures on the provided domains according to both
automatic and human evaluation metrics.
Differential Angular Imaging for Material Recognition
Authors: Jia Xue, Hang Zhang, Kristin Dana, Ko Nishino
Date: 2016-12-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Material Recognition

Material recognition for real-world outdoor surfaces has become increasingly
important for computer vision to support its operation "in the wild."
Computational surface modeling that underlies material recognition has
transitioned from reflectance modeling using in-lab controlled radiometric
measurements to image-based representations based on internet-mined images of
materials captured in the scene. We propose to take a middle-ground approach
for material recognition that takes advantage of both rich radiometric cues and
flexible image capture. We realize this by developing a framework for
differential angular imaging, where small angular variations in image capture
provide an enhanced appearance representation and significant recognition
improvement. We build a large-scale material database, Ground Terrain in
Outdoor Scenes (GTOS) database, geared towards real use for autonomous agents.
The database consists of over 30,000 images covering 40 classes of outdoor
ground terrain under varying weather and lighting conditions. We develop a
novel approach for material recognition called a Differential Angular Imaging
Network (DAIN) to fully leverage this large dataset. With this novel network
architecture, we extract characteristics of materials encoded in the angular
and spatial gradients of their appearance. Our results show that DAIN achieves
recognition performance that surpasses single view or coarsely quantized
multiview images. These results demonstrate the effectiveness of differential
angular imaging as a means for flexible, in-place material recognition.
Advances in Artificial Intelligence Require Progress Across all of Computer Science
Authors: Gregory D. Hager, Randal Bryant, Eric Horvitz, Maja Mataric, Vasant Honavar
Date: 2017-07-13 00:00:00
Tasks: All

Advances in Artificial Intelligence require progress across all of computer
science.
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Authors: Lin Chen, Moran Feldman, Amin Karbasi
Date: 2017-07-13 00:00:00
Proceeding: ICML 2018 7

Submodular functions are a broad class of set functions, which naturally
arise in diverse areas. Many algorithms have been suggested for the
maximization of these functions. Unfortunately, once the function deviates from
submodularity, the known algorithms may perform arbitrarily poorly. Amending
this issue, by obtaining approximation results for set functions generalizing
submodular functions, has been the focus of recent works.
  One such class, known as weakly submodular functions, has received a lot of
attention. A key result proved by Das and Kempe (2011) showed that the
approximation ratio of the greedy algorithm for weakly submodular maximization
subject to a cardinality constraint degrades smoothly with the distance from
submodularity. However, no results have been obtained for maximization subject
to constraints beyond cardinality. In particular, it is not known whether the
greedy algorithm achieves any non-trivial approximation ratio for such
constraints.
  In this paper, we prove that a randomized version of the greedy algorithm
(previously used by Buchbinder et al. (2014) for a different problem) achieves
an approximation ratio of $(1 + 1/\gamma)^{-2}$ for the maximization of a
weakly submodular function subject to a general matroid constraint, where
$\gamma$ is a parameter measuring the distance of the function from
submodularity. Moreover, we also experimentally compare the performance of this
version of the greedy algorithm on real world problems against natural
benchmarks, and show that the algorithm we study performs well also in
practice. To the best of our knowledge, this is the first algorithm with a
non-trivial approximation guarantee for maximizing a weakly submodular function
subject to a constraint other than the simple cardinality constraint. In
particular, it is the first algorithm with such a guarantee for the important
and broad class of matroid constraints.
Human-Level Intelligence or Animal-Like Abilities?
Authors: Adnan Darwiche
Date: 2017-07-13 00:00:00

The vision systems of the eagle and the snake outperform everything that we
can make in the laboratory, but snakes and eagles cannot build an eyeglass or a
telescope or a microscope. (Judea Pearl)
Tensor-Based Backpropagation in Neural Networks with Non-Sequential Input
Authors: Hirsh R. Agarwal, Andrew Huang
Date: 2017-07-13 00:00:00
Tasks: Computational Efficiency

Neural networks have been able to achieve groundbreaking accuracy at tasks
conventionally considered only doable by humans. Using stochastic gradient
descent, optimization in many dimensions is made possible, albeit at a
relatively high computational cost. By splitting training data into batches,
networks can be distributed and trained vastly more efficiently and with
minimal accuracy loss. We have explored the mathematics behind efficiently
implementing tensor-based batch backpropagation algorithms. A common approach
to batch training is iterating over batch items individually. Explicitly using
tensor operations to backpropagate allows training to be performed
non-linearly, increasing computational efficiency.
Model compression as constrained optimization, with application to neural nets. Part II: quantization
Authors: Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev
Date: 2017-07-13 00:00:00
Tasks: Binarization, Model Compression, Quantization

We consider the problem of deep neural net compression by quantization: given
a large, reference net, we want to quantize its real-valued weights using a
codebook with $K$ entries so that the training loss of the quantized net is
minimal. The codebook can be optimally learned jointly with the net, or fixed,
as for binarization or ternarization approaches. Previous work has quantized
the weights of the reference net, or incorporated rounding operations in the
backpropagation algorithm, but this has no guarantee of converging to a
loss-optimal, quantized net. We describe a new approach based on the recently
proposed framework of model compression as constrained optimization
\citep{Carreir17a}. This results in a simple iterative "learning-compression"
algorithm, which alternates a step that learns a net of continuous weights with
a step that quantizes (or binarizes/ternarizes) the weights, and is guaranteed
to converge to local optimum of the loss for quantized nets. We develop
algorithms for an adaptive codebook or a (partially) fixed codebook. The latter
includes binarization, ternarization, powers-of-two and other important
particular cases. We show experimentally that we can achieve much higher
compression rates than previous quantization work (even using just 1 bit per
weight) with negligible loss degradation.
Discriminative Optimization: Theory and Applications to Computer Vision Problems
Authors: Jayakorn Vongkulbhisal, Fernando de la Torre, João P. Costeira
Date: 2017-07-13 00:00:00
Tasks: Camera Pose Estimation, Computational Efficiency, Denoising, Image Denoising, Point Cloud Registration, Pose Estimation

Many computer vision problems are formulated as the optimization of a cost
function. This approach faces two main challenges: (i) designing a cost
function with a local optimum at an acceptable solution, and (ii) developing an
efficient numerical method to search for one (or multiple) of these local
optima. While designing such functions is feasible in the noiseless case, the
stability and location of local optima are mostly unknown under noise,
occlusion, or missing data. In practice, this can result in undesirable local
optima or not having a local optimum in the expected place. On the other hand,
numerical optimization algorithms in high-dimensional spaces are typically
local and often rely on expensive first or second order information to guide
the search. To overcome these limitations, this paper proposes Discriminative
Optimization (DO), a method that learns search directions from data without the
need of a cost function. Specifically, DO explicitly learns a sequence of
updates in the search space that leads to stationary points that correspond to
desired solutions. We provide a formal analysis of DO and illustrate its
benefits in the problem of 3D point cloud registration, camera pose estimation,
and image denoising. We show that DO performed comparably or outperformed
state-of-the-art algorithms in terms of accuracy, robustness to perturbations,
and computational efficiency.