pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Qualia in Natural and Artificial Agents
Authors: Roman V. Yampolskiy
Date: 2017-12-11 00:00:00

The Hard Problem of consciousness has been dismissed as an illusion. By
showing that computers are capable of experiencing, we show that they are at
least rudimentarily conscious with potential to eventually reach
superconsciousness. The main contribution of the paper is a test for confirming
certain subjective experiences in a tested agent. We follow with analysis of
benefits and problems with conscious machines and implications of such
capability on future of computing, machine rights and artificial intelligence
safety.
Online Factorization and Partition of Complex Networks From Random Walks
Authors: Lin F. Yang, Vladimir Braverman, Tuo Zhao, Mengdi Wang
Date: 2017-05-22 00:00:00
Tasks: Clustering

Finding the reduced-dimensional structure is critical to understanding
complex networks. Existing approaches such as spectral clustering are
applicable only when the full network is explicitly observed. In this paper, we
focus on the online factorization and partition of implicit large-scale
networks based on observations from an associated random walk. We formulate
this into a nonconvex stochastic factorization problem and propose an efficient
and scalable stochastic generalized Hebbian algorithm. The algorithm is able to
process dependent state-transition data dynamically generated by the underlying
network and learn a low-dimensional representation for each vertex. By applying
a diffusion approximation analysis, we show that the continuous-time limiting
process of the stochastic algorithm converges globally to the "principal
components" of the Markov chain and achieves a nearly optimal sample
complexity. Once given the learned low-dimensional representations, we further
apply clustering techniques to recover the network partition. We show that when
the associated Markov process is lumpable, one can recover the partition
exactly with high probability. We apply the proposed approach to model the
traffic flow of Manhattan as city-wide random walks. By using our algorithm to
analyze the taxi trip data, we discover a latent partition of the Manhattan
city that closely matches the traffic dynamics.
Training Ensembles to Detect Adversarial Examples
Authors: Alexander Bagnall, Razvan Bunescu, Gordon Stewart
Date: 2017-12-11 00:00:00
Tasks: General Classification

We propose a new ensemble method for detecting and classifying adversarial
examples generated by state-of-the-art attacks, including DeepFool and C&W. Our
method works by training the members of an ensemble to have low classification
error on random benign examples while simultaneously minimizing agreement on
examples outside the training distribution. We evaluate on both MNIST and
CIFAR-10, against oblivious and both white- and black-box adversaries.
Online Dynamic Programming
Authors: Holakou Rahmanian, Manfred K. Warmuth
Date: 2017-06-02 00:00:00
Proceeding: NeurIPS 2017 12

We consider the problem of repeatedly solving a variant of the same dynamic
programming problem in successive trials. An instance of the type of problems
we consider is to find a good binary search tree in a changing environment.At
the beginning of each trial, the learner probabilistically chooses a tree with
the $n$ keys at the internal nodes and the $n+1$ gaps between keys at the
leaves. The learner is then told the frequencies of the keys and gaps and is
charged by the average search cost for the chosen tree. The problem is online
because the frequencies can change between trials. The goal is to develop
algorithms with the property that their total average search cost (loss) in all
trials is close to the total loss of the best tree chosen in hindsight for all
trials. The challenge, of course, is that the algorithm has to deal with
exponential number of trees. We develop a general methodology for tackling such
problems for a wide class of dynamic programming algorithms. Our framework
allows us to extend online learning algorithms like Hedge and Component Hedge
to a significantly wider class of combinatorial objects than was possible
before.
Eye In-Painting with Exemplar Generative Adversarial Networks
Authors: Brian Dolhansky, Cristian Canton Ferrer
Date: 2017-12-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Descriptive

This paper introduces a novel approach to in-painting where the identity of
the object to remove or change is preserved and accounted for at inference
time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize
exemplar information to produce high-quality, personalized in painting results.
We propose using exemplar information in the form of a reference image of the
region to in-paint, or a perceptual code describing that object. Unlike
previous conditional GAN formulations, this extra information can be inserted
at multiple points within the adversarial network, thus increasing its
descriptive power. We show that ExGANs can produce photo-realistic personalized
in-painting results that are both perceptually and semantically plausible by
applying them to the task of closed to-open eye in-painting in natural
pictures. A new benchmark dataset is also introduced for the task of eye
in-painting for future comparisons.
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data
Authors: Daniel George, E. A. Huerta
Date: 2017-11-21 00:00:00
Tasks: Gravitational Wave Detection, parameter estimation, Time Series Analysis

The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent science, we
proposed the use of deep convolutional neural networks for the detection and
characterization of gravitational wave signals in real-time. This method, Deep
Filtering, was initially demonstrated using simulated LIGO noise. In this
article, we present the extension of Deep Filtering using real data from the
first observing run of LIGO, for both detection and parameter estimation of
gravitational waves from binary black hole mergers with continuous data streams
from multiple LIGO detectors. We show for the first time that machine learning
can detect and estimate the true parameters of a real GW event observed by
LIGO. Our comparisons show that Deep Filtering is far more computationally
efficient than matched-filtering, while retaining similar sensitivity and lower
errors, allowing real-time processing of weak time-series signals in
non-stationary non-Gaussian noise, with minimal resources, and also enables the
detection of new classes of gravitational wave sources that may go unnoticed
with existing detection algorithms. This approach is uniquely suited to enable
coincident detection campaigns of gravitational waves and their multimessenger
counterparts in real-time.
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently
Authors: Yaodong Yu, Difan Zou, Quanquan Gu
Date: 2017-12-11 00:00:00

We propose a family of nonconvex optimization algorithms that are able to
save gradient and negative curvature computations to a large extent, and are
guaranteed to find an approximate local minimum with improved runtime
complexity. At the core of our algorithms is the division of the entire domain
of the objective function into small and large gradient regions: our algorithms
only perform gradient descent based procedure in the large gradient region, and
only perform negative curvature descent in the small gradient region. Our novel
analysis shows that the proposed algorithms can escape the small gradient
region in only one negative curvature descent step whenever they enter it, and
thus they only need to perform at most $N_{\epsilon}$ negative curvature
direction computations, where $N_{\epsilon}$ is the number of times the
algorithms enter small gradient regions. For both deterministic and stochastic
settings, we show that the proposed algorithms can potentially beat the
state-of-the-art local minima finding algorithms. For the finite-sum setting,
our algorithm can also outperform the best algorithm in a certain regime.
Predicting Yelp Star Reviews Based on Network Structure with Deep Learning
Authors: Luis Perez
Date: 2017-12-11 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

In this paper, we tackle the real-world problem of predicting Yelp
star-review rating based on business features (such as images, descriptions),
user features (average previous ratings), and, of particular interest, network
properties (which businesses has a user rated before). We compare multiple
models on different sets of features -- from simple linear regression on
network features only to deep learning models on network and item features.
  In recent years, breakthroughs in deep learning have led to increased
accuracy in common supervised learning tasks, such as image classification,
captioning, and language understanding. However, the idea of combining deep
learning with network feature and structure appears to be novel. While the
problem of predicting future interactions in a network has been studied at
length, these approaches have often ignored either node-specific data or global
structure.
  We demonstrate that taking a mixed approach combining both node-level
features and network information can effectively be used to predict Yelp-review
star ratings. We evaluate on the Yelp dataset by splitting our data along the
time dimension (as would naturally occur in the real-world) and comparing our
model against others which do no take advantage of the network structure and/or
deep learning.
On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification
Authors: Gaurav Bhatt, Aman Sharma, Shivam Sharma, Ankush Nagpal, Balasubramanian Raman, Ankush Mittal
Date: 2017-12-11 00:00:00
Tasks: Fake News Detection, Feature Engineering, Open-Ended Question Answering, Stance Detection

Identifying the veracity of a news article is an interesting problem while
automating this process can be a challenging task. Detection of a news article
as fake is still an open question as it is contingent on many factors which the
current state-of-the-art models fail to incorporate. In this paper, we explore
a subtask to fake news identification, and that is stance detection. Given a
news article, the task is to determine the relevance of the body and its claim.
We present a novel idea that combines the neural, statistical and external
features to provide an efficient solution to this problem. We compute the
neural embedding from the deep recurrent model, statistical features from the
weighted n-gram bag-of-words model and handcrafted external features with the
help of feature engineering heuristics. Finally, using deep neural layer all
the features are combined, thereby classifying the headline-body news pair as
agree, disagree, discuss, or unrelated. We compare our proposed technique with
the current state-of-the-art models on the fake news challenge dataset. Through
extensive experiments, we find that the proposed model outperforms all the
state-of-the-art techniques including the submissions to the fake news
challenge.
MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments
Authors: Manolis Savva, Angel X. Chang, Alexey Dosovitskiy, Thomas Funkhouser, Vladlen Koltun
Date: 2017-12-11 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present MINOS, a simulator designed to support the development of
multisensory models for goal-directed navigation in complex indoor
environments. The simulator leverages large datasets of complex 3D environments
and supports flexible configuration of multimodal sensor suites. We use MINOS
to benchmark deep-learning-based navigation methods, to analyze the influence
of environmental complexity on navigation performance, and to carry out a
controlled study of multimodality in sensorimotor learning. The experiments
show that current deep reinforcement learning approaches fail in large
realistic environments. The experiments also indicate that multimodality is
beneficial in learning to navigate cluttered scenes. MINOS is released
open-source to the research community at http://minosworld.org . A video that
shows MINOS can be found at https://youtu.be/c0mL9K64q84
Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection
Authors: Anderson Y. Zhang, Harrison H. Zhou
Date: 2017-10-30 00:00:00
Tasks: Bayesian Inference, Community Detection, Stochastic Block Model, Variational Inference

The mean field variational Bayes method is becoming increasingly popular in
statistics and machine learning. Its iterative Coordinate Ascent Variational
Inference algorithm has been widely applied to large scale Bayesian inference.
See Blei et al. (2017) for a recent comprehensive review. Despite the
popularity of the mean field method there exist remarkably little fundamental
theoretical justifications. To the best of our knowledge, the iterative
algorithm has never been investigated for any high dimensional and complex
model. In this paper, we study the mean field method for community detection
under the Stochastic Block Model. For an iterative Batch Coordinate Ascent
Variational Inference algorithm, we show that it has a linear convergence rate
and converges to the minimax rate within $\log n$ iterations. This complements
the results of Bickel et al. (2013) which studied the global minimum of the
mean field variational Bayes and obtained asymptotic normal estimation of
global model parameters. In addition, we obtain similar optimality results for
Gibbs sampling and an iterative procedure to calculate maximum likelihood
estimation, which can be of independent interest.
Glitch Classification and Clustering for LIGO with Deep Transfer Learning
Authors: Daniel George, Hongyu Shen, E. A. Huerta
Date: 2017-11-20 00:00:00
Tasks: Classification, Clustering, General Classification, Object Recognition, Transfer Learning

The detection of gravitational waves with LIGO and Virgo requires a detailed
understanding of the response of these instruments in the presence of
environmental and instrumental noise. Of particular interest is the study of
anomalous non-Gaussian noise transients known as glitches, since their high
occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational
wave signals. Therefore, successfully identifying and excising glitches is of
utmost importance to detect and characterize gravitational waves. In this
article, we present the first application of Deep Learning combined with
Transfer Learning for glitch classification, using real data from LIGO's first
discovery campaign labeled by Gravity Spy, showing that knowledge from
pre-trained models for real-world object recognition can be transferred for
classifying spectrograms of glitches. We demonstrate that this method enables
the optimal use of very deep convolutional neural networks for glitch
classification given small unbalanced training datasets, significantly reduces
the training time, and achieves state-of-the-art accuracy above 98.8%. Once
trained via transfer learning, we show that the networks can be truncated and
used as feature extractors for unsupervised clustering to automatically group
together new classes of glitches and anomalies. This novel capability is of
critical importance to identify and remove new types of glitches which will
occur as the LIGO/Virgo detectors gradually attain design sensitivity.
A Novel Way of Identifying Cyber Predators
Authors: Dan Liu, Ching Yee Suen, Olga Ormandjieva
Date: 2017-12-11 00:00:00
Tasks: General Classification, Language Modeling, Language Modelling, Sentence, Sentiment Analysis, text-classification, Text Classification

Recurrent Neural Networks with Long Short-Term Memory cell (LSTM-RNN) have
impressive ability in sequence data processing, particularly for language model
building and text classification. This research proposes the combination of
sentiment analysis, new approach of sentence vectors and LSTM-RNN as a novel
way for Sexual Predator Identification (SPI). LSTM-RNN language model is
applied to generate sentence vectors which are the last hidden states in the
language model. Sentence vectors are fed into another LSTM-RNN classifier, so
as to capture suspicious conversations. Hidden state enables to generate
vectors for sentences never seen before. Fasttext is used to filter the
contents of conversations and generate a sentiment score so as to identify
potential predators. The experiment achieves a record-breaking accuracy and
precision of 100% with recall of 81.10%, exceeding the top-ranked result in the
SPI competition.
Learning Modality-Invariant Representations for Speech and Images
Authors: Kenneth Leidal, David Harwath, James Glass
Date: 2017-12-11 00:00:00
Tasks: Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity, Transfer Learning

In this paper, we explore the unsupervised learning of a semantic embedding
space for co-occurring sensory inputs. Specifically, we focus on the task of
learning a semantic vector space for both spoken and handwritten digits using
the TIDIGITs and MNIST datasets. Current techniques encode image and
audio/textual inputs directly to semantic embeddings. In contrast, our
technique maps an input to the mean and log variance vectors of a diagonal
Gaussian from which sample semantic embeddings are drawn. In addition to
encouraging semantic similarity between co-occurring inputs,our loss function
includes a regularization term borrowed from variational autoencoders (VAEs)
which drives the posterior distributions over embeddings to be unit Gaussian.
We can use this regularization term to filter out modality information while
preserving semantic information. We speculate this technique may be more
broadly applicable to other areas of cross-modality/domain information
retrieval and transfer learning.
Towards reduction of autocorrelation in HMC by machine learning
Authors: Akinori Tanaka, Akio Tomiya
Date: 2017-12-11 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we propose new algorithm to reduce autocorrelation in Markov
chain Monte-Carlo algorithms for euclidean field theories on the lattice. Our
proposing algorithm is the Hybrid Monte-Carlo algorithm (HMC) with restricted
Boltzmann machine. We examine the validity of the algorithm by employing the
phi-fourth theory in three dimension. We observe reduction of the
autocorrelation both in symmetric and broken phase as well. Our proposing
algorithm provides consistent central values of expectation values of the
action density and one-point Green's function with ones from the original HMC
in both the symmetric phase and broken phase within the statistical error. On
the other hand, two-point Green's functions have slight difference between one
calculated by the HMC and one by our proposing algorithm in the symmetric
phase. Furthermore, near the criticality, the distribution of the one-point
Green's function differs from the one from HMC. We discuss the origin of
discrepancies and its improvement.
DeepConfig: Automating Data Center Network Topologies Management with Machine Learning
Authors: Christopher Streiffer, Huan Chen, Theophilus Benson, Asim Kadav
Date: 2017-12-11 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Management

In recent years, many techniques have been developed to improve the
performance and efficiency of data center networks. While these techniques
provide high accuracy, they are often designed using heuristics that leverage
domain-specific properties of the workload or hardware.
  In this vision paper, we argue that many data center networking techniques,
e.g., routing, topology augmentation, energy savings, with diverse goals
actually share design and architectural similarity. We present a design for
developing general intermediate representations of network topologies using
deep learning that is amenable to solving classes of data center problems. We
develop a framework, DeepConfig, that simplifies the processing of configuring
and training deep learning agents that use the intermediate representation to
learns different tasks. To illustrate the strength of our approach, we
configured, implemented, and evaluated a DeepConfig-Agent that tackles the data
center topology augmentation problem. Our initial results are promising ---
DeepConfig performs comparably to the optimal.
Using a single RGB frame for real time 3D hand pose estimation in the wild
Authors: Paschalis Panteleris, Iason Oikonomidis, Antonis Argyros
Date: 2017-12-11 00:00:00
Tasks: 3D Hand Pose Estimation, Camera Calibration, Hand Pose Estimation, Pose Estimation

We present a method for the real-time estimation of the full 3D pose of one
or more human hands using a single commodity RGB camera. Recent work in the
area has displayed impressive progress using RGBD input. However, since the
introduction of RGBD sensors, there has been little progress for the case of
monocular color input. We capitalize on the latest advancements of deep
learning, combining them with the power of generative hand pose estimation
techniques to achieve real-time monocular 3D hand pose estimation in
unrestricted scenarios. More specifically, given an RGB image and the relevant
camera calibration information, we employ a state-of-the-art detector to
localize hands. Given a crop of a hand in the image, we run the pretrained
network of OpenPose for hands to estimate the 2D location of hand joints.
Finally, non-linear least-squares minimization fits a 3D model of the hand to
the estimated 2D joint positions, recovering the 3D hand pose. Extensive
experimental results provide comparison to the state of the art as well as
qualitative assessment of the method in the wild.
Generating Visual Representations for Zero-Shot Classification
Authors: Maxime Bucher, Stéphane Herbin, Frédéric Jurie
Date: 2017-08-23 00:00:00
Tasks: Classification, General Classification, zero-shot-classification, Zero-Shot Learning

This paper addresses the task of learning an image clas-sifier when some
categories are defined by semantic descriptions only (e.g. visual attributes)
while the others are defined by exemplar images as well. This task is often
referred to as the Zero-Shot classification task (ZSC). Most of the previous
methods rely on learning a common embedding space allowing to compare visual
features of unknown categories with semantic descriptions. This paper argues
that these approaches are limited as i) efficient discrimi-native classifiers
can't be used ii) classification tasks with seen and unseen categories
(Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently.
In contrast , this paper suggests to address ZSC and GZSC by i) learning a
conditional generator using seen classes ii) generate artificial training
examples for the categories without exemplars. ZSC is then turned into a
standard supervised learning problem. Experiments with 4 generative models and
5 datasets experimentally validate the approach, giving state-of-the-art
results on both ZSC and GZSC.