pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Approach to Query Answering under Expressive Datalog+/-
Authors: Mostafa Milani, Andrea Cali, Leopoldo Bertossi
Date: 2016-04-22 00:00:00

Datalog+/- is a family of ontology languages that combine good computational
properties with high expressive power. Datalog+/- languages are provably able
to capture the most relevant Semantic Web languages. In this paper we consider
the class of weakly-sticky (WS) Datalog+/- programs, which allow for certain
useful forms of joins in rule bodies as well as extending the well-known class
of weakly-acyclic TGDs. So far, only non-deterministic algorithms were known
for answering queries on WS Datalog+/- programs. We present novel deterministic
query answering algorithms under WS Datalog+/-. In particular, we propose: (1)
a bottom-up grounding algorithm based on a query-driven chase, and (2) a hybrid
approach based on transforming a WS program into a so-called sticky one, for
which query rewriting techniques are known. We discuss how our algorithms can
be optimized and effectively applied for query answering in real-world
scenarios.
Symmetry-free SDP Relaxations for Affine Subspace Clustering
Authors: Francesco Silvestri, Gerhard Reinelt, Christoph Schnörr
Date: 2016-07-25 00:00:00
Tasks: Clustering

We consider clustering problems where the goal is to determine an optimal
partition of a given point set in Euclidean space in terms of a collection of
affine subspaces. While there is vast literature on heuristics for this kind of
problem, such approaches are known to be susceptible to poor initializations
and getting trapped in bad local optima. We alleviate these issues by
introducing a semidefinite relaxation based on Lasserre's method of moments.
While a similiar approach is known for classical Euclidean clustering problems,
a generalization to our more general subspace scenario is not straightforward,
due to the high symmetry of the objective function that weakens any convex
relaxation. We therefore introduce a new mechanism for symmetry breaking based
on covering the feasible region with polytopes. Additionally, we introduce and
analyze a deterministic rounding heuristic.
Identifying Depression on Twitter
Authors: Moin Nadeem
Date: 2016-07-25 00:00:00
Tasks: General Classification, text-classification, Text Classification

Social media has recently emerged as a premier method to disseminate
information online. Through these online networks, tens of millions of
individuals communicate their thoughts, personal experiences, and social
ideals. We therefore explore the potential of social media to predict, even
prior to onset, Major Depressive Disorder (MDD) in online personas. We employ a
crowdsourced method to compile a list of Twitter users who profess to being
diagnosed with depression. Using up to a year of prior social media postings,
we utilize a Bag of Words approach to quantify each tweet. Lastly, we leverage
several statistical classifiers to provide estimates to the risk of depression.
Our work posits a new methodology for constructing our classifier by treating
social as a text-classification problem, rather than a behavioral one on social
media platforms. By using a corpus of 2.5M tweets, we achieved an 81% accuracy
rate in classification, with a precision score of .86. We believe that this
method may be helpful in developing tools that estimate the risk of an
individual being depressed, can be employed by physicians, concerned
individuals, and healthcare agencies to aid in diagnosis, even possibly
enabling those suffering from depression to be more proactive about recovering
from their mental health.
Concrete Problems in AI Safety
Authors: Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané
Date: 2016-06-21 00:00:00
Tasks: BIG-bench Machine Learning, Safe Exploration

Rapid progress in machine learning and artificial intelligence (AI) has
brought increasing attention to the potential impacts of AI technologies on
society. In this paper we discuss one such potential impact: the problem of
accidents in machine learning systems, defined as unintended and harmful
behavior that may emerge from poor design of real-world AI systems. We present
a list of five practical research problems related to accident risk,
categorized according to whether the problem originates from having the wrong
objective function ("avoiding side effects" and "avoiding reward hacking"), an
objective function that is too expensive to evaluate frequently ("scalable
supervision"), or undesirable behavior during the learning process ("safe
exploration" and "distributional shift"). We review previous work in these
areas as well as suggesting research directions with a focus on relevance to
cutting-edge AI systems. Finally, we consider the high-level question of how to
think most productively about the safety of forward-looking applications of AI.
A Perspective on Sentiment Analysis
Authors: K Paramesha, K C Ravishankar
Date: 2016-07-21 00:00:00
Tasks: Sentiment Analysis

Sentiment Analysis (SA) is indeed a fascinating area of research which has
stolen the attention of researchers as it has many facets and more importantly
it promises economic stakes in the corporate and governance sector. SA has been
stemmed out of text analytics and established itself as a separate identity and
a domain of research. The wide ranging results of SA have proved to influence
the way some critical decisions are taken. Hence, it has become relevant in
thorough understanding of the different dimensions of the input, output and the
processes and approaches of SA.
Evaluating Link Prediction Accuracy on Dynamic Networks with Added and Removed Edges
Authors: Ruthwik R. Junuthula, Kevin S. Xu, Vijay K. Devabhaktuni
Date: 2016-07-25 00:00:00
Tasks: Dynamic Link Prediction, Link Prediction, Prediction

The task of predicting future relationships in a social network, known as
link prediction, has been studied extensively in the literature. Many link
prediction methods have been proposed, ranging from common neighbors to
probabilistic models. Recent work by Yang et al. has highlighted several
challenges in evaluating link prediction accuracy. In dynamic networks where
edges are both added and removed over time, the link prediction problem is more
complex and involves predicting both newly added and newly removed edges. This
results in new challenges in the evaluation of dynamic link prediction methods,
and the recommendations provided by Yang et al. are no longer applicable,
because they do not address edge removal. In this paper, we investigate several
metrics currently used for evaluating accuracies of dynamic link prediction
methods and demonstrate why they can be misleading in many cases. We provide
several recommendations on evaluating dynamic link prediction accuracy,
including separation into two categories of evaluation. Finally we propose a
unified metric to characterize link prediction accuracy effectively using a
single number.
Accelerating Stochastic Composition Optimization
Authors: Mengdi Wang, Ji Liu, Ethan X. Fang
Date: 2016-07-25 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Consider the stochastic composition optimization problem where the objective
is a composition of two expected-value functions. We propose a new stochastic
first-order method, namely the accelerated stochastic compositional proximal
gradient (ASC-PG) method, which updates based on queries to the sampling oracle
using two different timescales. The ASC-PG is the first proximal gradient
method for the stochastic composition problem that can deal with nonsmooth
regularization penalty. We show that the ASC-PG exhibits faster convergence
than the best known algorithms, and that it achieves the optimal sample-error
complexity in several important special cases. We further demonstrate the
application of ASC-PG to reinforcement learning and conduct numerical
experiments.
Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
Authors: Flavian Vasile, Elena Smirnova, Alexis Conneau
Date: 2016-07-25 00:00:00

We propose Meta-Prod2vec, a novel method to compute item similarities for
recommendation that leverages existing item metadata. Such scenarios are
frequently encountered in applications such as content recommendation, ad
targeting and web search. Our method leverages past user interactions with
items and their attributes to compute low-dimensional embeddings of items.
Specifically, the item metadata is in- jected into the model as side
information to regularize the item embeddings. We show that the new item
representa- tions lead to better performance on recommendation tasks on an open
music dataset.
Identity Mappings in Deep Residual Networks
Authors: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Date: 2016-03-16 00:00:00
Tasks: Image Classification

Deep residual networks have emerged as a family of extremely deep
architectures showing compelling accuracy and nice convergence behaviors. In
this paper, we analyze the propagation formulations behind the residual
building blocks, which suggest that the forward and backward signals can be
directly propagated from one block to any other block, when using identity
mappings as the skip connections and after-addition activation. A series of
ablation experiments support the importance of these identity mappings. This
motivates us to propose a new residual unit, which makes training easier and
improves generalization. We report improved results using a 1001-layer ResNet
on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet.
Code is available at: https://github.com/KaimingHe/resnet-1k-layers
Estimating Activity at Multiple Scales using Spatial Abstractions
Authors: Majd Hawasly, Florian T. Pokorny, Subramanian Ramamoorthy
Date: 2016-07-25 00:00:00
Tasks: Clustering, Trajectory Clustering

Autonomous robots operating in dynamic environments must maintain beliefs
over a hypothesis space that is rich enough to represent the activities of
interest at different scales. This is important both in order to accommodate
the availability of evidence at varying degrees of coarseness, such as when
interpreting and assimilating natural instructions, but also in order to make
subsequent reactive planning more efficient. We present an algorithm that
combines a topology-based trajectory clustering procedure that generates
hierarchically-structured spatial abstractions with a bank of particle filters
at each of these abstraction levels so as to produce probability estimates over
an agent's navigation activity that is kept consistent across the hierarchy. We
study the performance of the proposed method using a synthetic trajectory
dataset in 2D, as well as a dataset taken from AIS-based tracking of ships in
an extended harbour area. We show that, in comparison to a baseline which is a
particle filter that estimates activity without exploiting such structure, our
method achieves a better normalised error in predicting the trajectory as well
as better time to convergence to a true class when compared against ground
truth.
Tracking with multi-level features
Authors: Roberto Henschel, Laura Leal-Taixé, Bodo Rosenhahn, Konrad Schindler
Date: 2016-07-25 00:00:00
Tasks: Clustering, Multiple Object Tracking, Object Tracking

We present a novel formulation of the multiple object tracking problem which
integrates low and mid-level features. In particular, we formulate the tracking
problem as a quadratic program coupling detections and dense point
trajectories. Due to the computational complexity of the initial QP, we propose
an approximation by two auxiliary problems, a temporal and spatial association,
where the temporal subproblem can be efficiently solved by a linear program and
the spatial association by a clustering algorithm. The objective function of
the QP is used in order to find the optimal number of clusters, where each
cluster ideally represents one person. Evaluation is provided for multiple
scenarios, showing the superiority of our method with respect to classic
tracking-by-detection methods and also other methods that greedily integrate
low-level features.
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
Authors: Lluis Castrejon, Yusuf Aytar, Carl Vondrick, Hamed Pirsiavash, Antonio Torralba
Date: 2016-07-25 00:00:00
Proceeding: CVPR 2016 6
Tasks: Retrieval

People can recognize scenes across many different modalities beyond natural
images. In this paper, we investigate how to learn cross-modal scene
representations that transfer across modalities. To study this problem, we
introduce a new cross-modal scene dataset. While convolutional neural networks
can categorize cross-modal scenes well, they also learn an intermediate
representation not aligned across modalities, which is undesirable for
cross-modal transfer applications. We present methods to regularize cross-modal
convolutional neural networks so that they have a shared representation that is
agnostic of the modality. Our experiments suggest that our scene representation
can help transfer representations across modalities for retrieval. Moreover,
our visualizations suggest that units emerge in the shared representation that
tend to activate on consistent concepts independently of the modality.
A Statistical Test for Joint Distributions Equivalence
Authors: Francesco Solera, Andrea Palazzi
Date: 2016-07-25 00:00:00

We provide a distribution-free test that can be used to determine whether any
two joint distributions $p$ and $q$ are statistically different by inspection
of a large enough set of samples. Following recent efforts from Long et al.
[1], we rely on joint kernel distribution embedding to extend the kernel
two-sample test of Gretton et al. [2] to the case of joint probability
distributions. Our main result can be directly applied to verify if a
dataset-shift has occurred between training and test distributions in a
learning framework, without further assuming the shift has occurred only in the
input, in the target or in the conditional distribution.
Automatic Attribute Discovery with Neural Activations
Authors: Sirion Vittayakorn, Takayuki Umeda, Kazuhiko Murasaki, Kyoko Sudo, Takayuki Okatani, Kota Yamaguchi
Date: 2016-07-25 00:00:00
Tasks: Attribute

How can a machine learn to recognize visual attributes emerging out of online
community without a definitive supervised dataset? This paper proposes an
automatic approach to discover and analyze visual attributes from a noisy
collection of image-text data on the Web. Our approach is based on the
relationship between attributes and neural activations in the deep network. We
characterize the visual property of the attribute word as a divergence within
weakly-annotated set of images. We show that the neural activations are useful
for discovering and learning a classifier that well agrees with human
perception from the noisy real-world Web data. The empirical study suggests the
layered structure of the deep neural networks also gives us insights into the
perceptual depth of the given word. Finally, we demonstrate that we can utilize
highly-activating neurons for finding semantically relevant regions.
A Model of Pathways to Artificial Superintelligence Catastrophe for Risk and Decision Analysis
Authors: Anthony M. Barrett, Seth D. Baum
Date: 2016-07-25 00:00:00
Tasks: Decision Making, Management

An artificial superintelligence (ASI) is artificial intelligence that is
significantly more intelligent than humans in all respects. While ASI does not
currently exist, some scholars propose that it could be created sometime in the
future, and furthermore that its creation could cause a severe global
catastrophe, possibly even resulting in human extinction. Given the high
stakes, it is important to analyze ASI risk and factor the risk into decisions
related to ASI research and development. This paper presents a graphical model
of major pathways to ASI catastrophe, focusing on ASI created via recursive
self-improvement. The model uses the established risk and decision analysis
modeling paradigms of fault trees and influence diagrams in order to depict
combinations of events and conditions that could lead to AI catastrophe, as
well as intervention options that could decrease risks. The events and
conditions include select aspects of the ASI itself as well as the human
process of ASI research, development, and management. Model structure is
derived from published literature on ASI risk. The model offers a foundation
for rigorous quantitative evaluation and decision making on the long-term risk
of ASI catastrophe.
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Authors: Yukai Shi, Keze Wang, Li Xu, Liang Lin
Date: 2016-07-25 00:00:00
Tasks: Image Super-Resolution, Representation Learning, Super-Resolution

Recently, machine learning based single image super resolution (SR)
approaches focus on jointly learning representations for high-resolution (HR)
and low-resolution (LR) image patch pairs to improve the quality of the
super-resolved images. However, due to treat all image pixels equally without
considering the salient structures, these approaches usually fail to produce
visual pleasant images with sharp edges and fine details. To address this
issue, in this work we present a new novel SR approach, which replaces the main
building blocks of the classical interpolation pipeline by a flexible,
content-adaptive deep neural networks. In particular, two well-designed
structure-aware components, respectively capturing local- and holistic- image
contents, are naturally incorporated into the fully-convolutional
representation learning to enhance the image sharpness and naturalness.
Extensively evaluations on several standard benchmarks (e.g., Set5, Set14 and
BSD200) demonstrate that our approach can achieve superior results, especially
on the image with salient structures, over many existing state-of-the-art SR
methods under both quantitative and qualitative measures.
Temporal Model Adaptation for Person Re-Identification
Authors: Niki Martinel, Abir Das, Christian Micheloni, Amit K. Roy-Chowdhury
Date: 2016-07-25 00:00:00
Tasks: model, Person Re-Identification

Person re-identification is an open and challenging problem in computer
vision. Majority of the efforts have been spent either to design the best
feature representation or to learn the optimal matching metric. Most approaches
have neglected the problem of adapting the selected features or the learned
model over time. To address such a problem, we propose a temporal model
adaptation scheme with human in the loop. We first introduce a
similarity-dissimilarity learning method which can be trained in an incremental
fashion by means of a stochastic alternating directions methods of multipliers
optimization procedure. Then, to achieve temporal adaptation with limited human
effort, we exploit a graph-based approach to present the user only the most
informative probe-gallery matches that should be used to update the model.
Results on three datasets have shown that our approach performs on par or even
better than state-of-the-art approaches while reducing the manual pairwise
labeling effort by about 80%.
Product Classification in E-Commerce using Distributional Semantics
Authors: Vivek Gupta, Harish Karnick, Ashendra Bansal, Pradhuman Jhala
Date: 2016-06-20 00:00:00
Proceeding: COLING 2016 12
Tasks: Classification, General Classification

Product classification is the task of automatically predicting a taxonomy
path for a product in a predefined taxonomy hierarchy given a textual product
description or title. For efficient product classification we require a
suitable representation for a document (the textual description of a product)
feature vector and efficient and fast algorithms for prediction. To address the
above challenges, we propose a new distributional semantics representation for
document vector formation. We also develop a new two-level ensemble approach
utilizing (with respect to the taxonomy tree) a path-wise, node-wise and
depth-wise classifiers for error reduction in the final product classification.
Our experiments show the effectiveness of the distributional representation and
the ensemble approach on data sets from a leading e-commerce platform and
achieve better results on various evaluation metrics compared to earlier
approaches.
Large-Scale Video Search with Efficient Temporal Voting Structure
Authors: Ersin Esen, Savas Ozkan, Ilkay Atil
Date: 2016-07-25 00:00:00

In this work, we propose a fast content-based video querying system for
large-scale video search. The proposed system is distinguished from similar
works with two major contributions. First contribution is superiority of joint
usage of repeated content representation and efficient hashing mechanisms.
Repeated content representation is utilized with a simple yet robust feature,
which is based on edge energy of frames. Each of the representation is
converted into hash code with Hamming Embedding method for further queries.
Second contribution is novel queue-based voting scheme that leads to modest
memory requirements with gradual memory allocation capability, contrary to
complete brute-force temporal voting schemes. This aspect enables us to make
queries on large video databases conveniently, even on commodity computers with
limited memory capacity. Our results show that the system can respond to video
queries on a large video database with fast query times, high recall rate and
very low memory and disk requirements.
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Authors: Zhaowei Cai, Quanfu Fan, Rogerio S. Feris, Nuno Vasconcelos
Date: 2016-07-25 00:00:00
Tasks: Face Detection, Feature Upsampling, Object, object-detection, Object Detection, Pedestrian Detection, Real-Time Object Detection

A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is
proposed for fast multi-scale object detection. The MS-CNN consists of a
proposal sub-network and a detection sub-network. In the proposal sub-network,
detection is performed at multiple output layers, so that receptive fields
match objects of different scales. These complementary scale-specific detectors
are combined to produce a strong multi-scale object detector. The unified
network is learned end-to-end, by optimizing a multi-task loss. Feature
upsampling by deconvolution is also explored, as an alternative to input
upsampling, to reduce the memory and computation costs. State-of-the-art object
detection performance, at up to 15 fps, is reported on datasets, such as KITTI
and Caltech, containing a substantial number of small objects.
Deep nets for local manifold learning
Authors: Charles K. Chui, H. N. Mhaskar
Date: 2016-07-24 00:00:00

The problem of extending a function $f$ defined on a training data
$\mathcal{C}$ on an unknown manifold $\mathbb{X}$ to the entire manifold and a
tubular neighborhood of this manifold is considered in this paper. For
$\mathbb{X}$ embedded in a high dimensional ambient Euclidean space
$\mathbb{R}^D$, a deep learning algorithm is developed for finding a local
coordinate system for the manifold {\bf without eigen--decomposition}, which
reduces the problem to the classical problem of function approximation on a low
dimensional cube. Deep nets (or multilayered neural networks) are proposed to
accomplish this approximation scheme by using the training data. Our methods do
not involve such optimization techniques as back--propagation, while assuring
optimal (a priori) error bounds on the output in terms of the number of
derivatives of the target function. In addition, these methods are universal,
in that they do not require a prior knowledge of the smoothness of the target
function, but adjust the accuracy of approximation locally and automatically,
depending only upon the local smoothness of the target function. Our ideas are
easily extended to solve both the pre--image problem and the out--of--sample
extension problem, with a priori bounds on the growth of the function thus
extended.