pwc-archive/papers-with-abstracts huggingface dataset:
Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach
Authors: Jungsik Hwang, Jun Tani
Date: 2017-06-08 00:00:00
Tasks: Decision Making

This study investigates how adequate coordination among the different
cognitive processes of a humanoid robot can be developed through end-to-end
learning of direct perception of visuomotor stream. We propose a deep dynamic
neural network model built on a dynamic vision network, a motor generation
network, and a higher-level network. The proposed model was designed to process
and to integrate direct perception of dynamic visuomotor patterns in a
hierarchical model characterized by different spatial and temporal constraints
imposed on each level. We conducted synthetic robotic experiments in which a
robot learned to read human's intention through observing the gestures and then
to generate the corresponding goal-directed actions. Results verify that the
proposed model is able to learn the tutored skills and to generalize them to
novel situations. The model showed synergic coordination of perception, action
and decision making, and it integrated and coordinated a set of cognitive
skills including visual perception, intention reading, attention switching,
working memory, action preparation and execution in a seamless manner. Analysis
reveals that coherent internal representations emerged at each level of the
hierarchy. Higher-level representation reflecting actional intention developed
by means of continuous integration of the lower-level visuo-proprioceptive
stream.
Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
Authors: Chin-Cheng Hsu, Hsin-Te Hwang, Yi-Chiao Wu, Yu Tsao, Hsin-Min Wang
Date: 2017-04-04 00:00:00
Tasks: Generative Adversarial Network, Voice Conversion

Building a voice conversion (VC) system from non-parallel speech corpora is
challenging but highly valuable in real application scenarios. In most
situations, the source and the target speakers do not repeat the same texts or
they may even speak different languages. In this case, one possible, although
indirect, solution is to build a generative model for speech. Generative models
focus on explaining the observations with latent variables instead of learning
a pairwise transformation function, thereby bypassing the requirement of speech
frame alignment. In this paper, we propose a non-parallel VC framework with a
variational autoencoding Wasserstein generative adversarial network (VAW-GAN)
that explicitly considers a VC objective when building the speech model.
Experimental results corroborate the capability of our framework for building a
VC system from unaligned data, and demonstrate improved conversion quality.
Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library
Authors: Jaimie Murdock, Colin Allen, Katy Börner, Robert Light, Simon McAlister, Andrew Ravenscroft, Robert Rose, Doori Rose, Jun Otsuka, David Bourget, John Lawrence, Chris Reed
Date: 2017-02-03 00:00:00
Tasks: General Classification, Philosophy, Topic Models

We show how faceted search using a combination of traditional classification
systems and mixed-membership topic models can go beyond keyword search to
inform resource discovery, hypothesis formulation, and argument extraction for
interdisciplinary research. Our test domain is the history and philosophy of
scientific work on animal mind and cognition. The methods can be generalized to
other research areas and ultimately support a system for semi-automatic
identification of argument structures. We provide a case study for the
application of the methods to the problem of identifying and extracting
arguments about anthropomorphism during a critical period in the development of
comparative psychology. We show how a combination of classification systems and
mixed-membership models trained over large digital libraries can inform
resource discovery in this domain. Through a novel approach of "drill-down"
topic modeling---simultaneously reducing both the size of the corpus and the
unit of analysis---we are able to reduce a large collection of fulltext volumes
to a much smaller set of pages within six focal volumes containing arguments of
interest to historians and philosophers of comparative psychology. The volumes
identified in this way did not appear among the first ten results of the
keyword search in the HathiTrust digital library and the pages bear the kind of
"close reading" needed to generate original interpretations that is the heart
of scholarly work in the humanities. Zooming back out, we provide a way to
place the books onto a map of science originally constructed from very
different data and for different purposes. The multilevel approach advances
understanding of the intellectual and societal contexts in which writings are
interpreted.
Dense Transformer Networks
Authors: Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, Shuiwang Ji
Date: 2017-05-24 00:00:00
Tasks: Decoder, Image Segmentation, Semantic Segmentation

The key idea of current deep learning methods for dense prediction is to
apply a model on a regular patch centered on each pixel to make pixel-wise
predictions. These methods are limited in the sense that the patches are
determined by network architecture instead of learned from data. In this work,
we propose the dense transformer networks, which can learn the shapes and sizes
of patches from data. The dense transformer networks employ an encoder-decoder
architecture, and a pair of dense transformer modules are inserted into each of
the encoder and decoder paths. The novelty of this work is that we provide
technical solutions for learning the shapes and sizes of patches from data and
efficiently restoring the spatial correspondence required for dense prediction.
The proposed dense transformer modules are differentiable, thus the entire
network can be trained. We apply the proposed networks on natural and
biological image segmentation tasks and show superior performance is achieved
in comparison to baseline methods.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Authors: Charles R. Qi, Li Yi, Hao Su, Leonidas J. Guibas
Date: 2017-06-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, 3D Semantic Segmentation, Few-Shot 3D Point Cloud Classification, Person Re-Identification, Point Cloud Segmentation, Semantic Segmentation, Supervised Only 3D Point Cloud Classification

Few prior works study deep learning on point sets. PointNet by Qi et al. is a
pioneer in this direction. However, by design PointNet does not capture local
structures induced by the metric space points live in, limiting its ability to
recognize fine-grained patterns and generalizability to complex scenes. In this
work, we introduce a hierarchical neural network that applies PointNet
recursively on a nested partitioning of the input point set. By exploiting
metric space distances, our network is able to learn local features with
increasing contextual scales. With further observation that point sets are
usually sampled with varying densities, which results in greatly decreased
performance for networks trained on uniform densities, we propose novel set
learning layers to adaptively combine features from multiple scales.
Experiments show that our network called PointNet++ is able to learn deep point
set features efficiently and robustly. In particular, results significantly
better than state-of-the-art have been obtained on challenging benchmarks of 3D
point clouds.
A Convex Framework for Fair Regression
Authors: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
Date: 2017-06-07 00:00:00
Tasks: Fairness, regression

We introduce a flexible family of fairness regularizers for (linear and
logistic) regression problems. These regularizers all enjoy convexity,
permitting fast optimization, and they span the rang from notions of group
fairness to strong individual fairness. By varying the weight on the fairness
regularizer, we can compute the efficient frontier of the accuracy-fairness
trade-off on any given dataset, and we measure the severity of this trade-off
via a numerical quantity we call the Price of Fairness (PoF). The centerpiece
of our results is an extensive comparative study of the PoF across six
different datasets in which fairness is a primary consideration.
ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks
Authors: Denis A. Gudovskiy, Luca Rigazio
Date: 2017-06-07 00:00:00
Tasks: Quantization

In this paper we introduce ShiftCNN, a generalized low-precision architecture
for inference of multiplierless convolutional neural networks (CNNs). ShiftCNN
is based on a power-of-two weight representation and, as a result, performs
only shift and addition operations. Furthermore, ShiftCNN substantially reduces
computational cost of convolutional layers by precomputing convolution terms.
Such an optimization can be applied to any CNN architecture with a relatively
small codebook of weights and allows to decrease the number of product
operations by at least two orders of magnitude. The proposed architecture
targets custom inference accelerators and can be realized on FPGAs or ASICs.
Extensive evaluation on ImageNet shows that the state-of-the-art CNNs can be
converted without retraining into ShiftCNN with less than 1% drop in accuracy
when the proposed quantization algorithm is employed. RTL simulations,
targeting modern FPGAs, show that power consumption of convolutional layers is
reduced by a factor of 4 compared to conventional 8-bit fixed-point
architectures.
CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks
Authors: Mustafa Mustafa, Deborah Bard, Wahid Bhimji, Zarija Lukić, Rami Al-Rfou, Jan M. Kratochvil
Date: 2017-06-07 00:00:00

Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic shift in the field of scientific simulations, but for that shift to happen we need to study the performance of such generators in the precision regime needed for science applications. To this end, in this work we apply Generative Adversarial Networks to the problem of generating weak lensing convergence maps. We show that our generator network produces maps that are described by, with high statistical confidence, the same summary statistics as the fully simulated maps.
Learning the structure of Bayesian Networks via the bootstrap
Authors: Giulio Caravagna, Daniele Ramazzotti
Date: 2017-06-07 00:00:00

Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. Within the context of Bayesian Networks, a practical and surprisingly successful solution to this learning problem is achieved by adopting score-functions optimisation schema, augmented with multiple restarts to avoid local optima. Yet, the conditions under which such strategies work well are poorly understood, and there are also some intrinsic limitations to learning the directionality of the interaction among the variables. Following an early intuition of Friedman and Koller, we propose to decouple the learning problem into two steps: first, we identify a partial ordering among input variables which constrains the structural learning problem, and then propose an effective bootstrap-based algorithm to simulate augmented data sets, and select the most important dependencies among the variables. By using several synthetic data sets, we show that our algorithm yields better recovery performance than the state of the art, increasing the chances of identifying a globally-optimal solution to the learning problem, and solving also well-known identifiability issues that affect the standard approach. We use our new algorithm to infer statistical dependencies between cancer driver somatic mutations detected by high-throughput genome sequencing data of multiple colorectal cancer patients. In this way, we also show how the proposed methods can shade new insights about cancer initiation, and progression. Code: https://github.com/caravagn/Bootstrap-based-Learning
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Authors: Hongteng Xu, Dixin Luo, Hongyuan Zha
Date: 2017-02-22 00:00:00
Proceeding: ICML 2017 8
Tasks: Point Processes

Many real-world applications require robust algorithms to learn point
processes based on a type of incomplete data --- the so-called short
doubly-censored (SDC) event sequences. We study this critical problem of
quantitative asynchronous event sequence analysis under the framework of Hawkes
processes by leveraging the idea of data synthesis. Given SDC event sequences
observed in a variety of time intervals, we propose a sampling-stitching data
synthesis method --- sampling predecessors and successors for each SDC event
sequence from potential candidates and stitching them together to synthesize
long training sequences. The rationality and the feasibility of our method are
discussed in terms of arguments based on likelihood. Experiments on both
synthetic and real-world data demonstrate that the proposed data synthesis
method improves learning results indeed for both time-invariant and
time-varying Hawkes processes.
L2-constrained Softmax Loss for Discriminative Face Verification
Authors: Rajeev Ranjan, Carlos D. Castillo, Rama Chellappa
Date: 2017-03-28 00:00:00
Tasks: Face Verification

In recent years, the performance of face verification systems has
significantly improved using deep convolutional neural networks (DCNNs). A
typical pipeline for face verification includes training a deep network for
subject classification with softmax loss, using the penultimate layer output as
the feature descriptor, and generating a cosine similarity score given a pair
of face images. The softmax loss function does not optimize the features to
have higher similarity score for positive pairs and lower similarity score for
negative pairs, which leads to a performance gap. In this paper, we add an
L2-constraint to the feature descriptors which restricts them to lie on a
hypersphere of a fixed radius. This module can be easily implemented using
existing deep learning frameworks. We show that integrating this simple step in
the training pipeline significantly boosts the performance of face
verification. Specifically, we achieve state-of-the-art results on the
challenging IJB-A dataset, achieving True Accept Rate of 0.909 at False Accept
Rate 0.0001 on the face verification protocol. Additionally, we achieve
state-of-the-art performance on LFW dataset with an accuracy of 99.78%, and
competing performance on YTF dataset with accuracy of 96.08%.
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
Authors: Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles
Date: 2017-06-07 00:00:00
Proceeding: CVPR 2017
Tasks: Segmentation

We present an end-to-end, multimodal, fully convolutional network for
extracting semantic structures from document images. We consider document
semantic structure extraction as a pixel-wise segmentation task, and propose a
unified model that classifies pixels based not only on their visual appearance,
as in the traditional page segmentation task, but also on the content of
underlying text. Moreover, we propose an efficient synthetic document
generation process that we use to generate pretraining data for our network.
Once the network is trained on a large set of synthetic documents, we fine-tune
the network on unlabeled real documents using a semi-supervised approach. We
systematically study the optimum network architecture and show that both our
multimodal approach and the synthetic data pretraining significantly boost the
performance.
Context Attentive Bandits: Contextual Bandit with Restricted Context
Authors: Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi, Raphael Feraud
Date: 2017-05-10 00:00:00
Tasks: Recommendation Systems, Thompson Sampling

We consider a novel formulation of the multi-armed bandit model, which we
call the contextual bandit with restricted context, where only a limited number
of features can be accessed by the learner at every iteration. This novel
formulation is motivated by different online problems arising in clinical
trials, recommender systems and attention modeling. Herein, we adapt the
standard multi-armed bandit algorithm known as Thompson Sampling to take
advantage of our restricted context setting, and propose two novel algorithms,
called the Thompson Sampling with Restricted Context(TSRC) and the Windows
Thompson Sampling with Restricted Context(WTSRC), for handling stationary and
nonstationary environments, respectively. Our empirical results demonstrate
advantages of the proposed approaches on several real-life datasets
CoMaL Tracking: Tracking Points at the Object Boundaries
Authors: Santhosh K. Ramakrishnan, Swarna Kamlam Ravindran, Anurag Mittal
Date: 2017-06-07 00:00:00
Tasks: Object, Point Tracking

Traditional point tracking algorithms such as the KLT use local 2D
information aggregation for feature detection and tracking, due to which their
performance degrades at the object boundaries that separate multiple objects.
Recently, CoMaL Features have been proposed that handle such a case. However,
they proposed a simple tracking framework where the points are re-detected in
each frame and matched. This is inefficient and may also lose many points that
are not re-detected in the next frame. We propose a novel tracking algorithm to
accurately and efficiently track CoMaL points. For this, the level line segment
associated with the CoMaL points is matched to MSER segments in the next frame
using shape-based matching and the matches are further filtered using
texture-based matching. Experiments show improvements over a simple
re-detect-and-match framework as well as KLT in terms of speed/accuracy on
different real-world applications, especially at the object boundaries.
Bandit Models of Human Behavior: Reward Processing in Mental Disorders
Authors: Djallel Bouneffouf, Irina Rish, Guillermo A. Cecchi
Date: 2017-06-07 00:00:00
Tasks: Decision Making, Thompson Sampling

Drawing an inspiration from behavioral studies of human decision making, we
propose here a general parametric framework for multi-armed bandit problem,
which extends the standard Thompson Sampling approach to incorporate reward
processing biases associated with several neurological and psychiatric
conditions, including Parkinson's and Alzheimer's diseases,
attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
We demonstrate empirically that the proposed parametric approach can often
outperform the baseline Thompson Sampling on a variety of datasets. Moreover,
from the behavioral modeling perspective, our parametric framework can be
viewed as a first step towards a unifying computational model capturing reward
processing abnormalities across multiple mental conditions.
Multi-Level and Multi-Scale Feature Aggregation Using Pre-trained Convolutional Neural Networks for Music Auto-tagging
Authors: Jongpil Lee, Juhan Nam
Date: 2017-03-06 00:00:00
Tasks: General Classification, image-classification, Image Classification, Music Auto-Tagging, Transfer Learning

Music auto-tagging is often handled in a similar manner to image
classification by regarding the 2D audio spectrogram as image data. However,
music auto-tagging is distinguished from image classification in that the tags
are highly diverse and have different levels of abstractions. Considering this
issue, we propose a convolutional neural networks (CNN)-based architecture that
embraces multi-level and multi-scaled features. The architecture is trained in
three steps. First, we conduct supervised feature learning to capture local
audio features using a set of CNNs with different input sizes. Second, we
extract audio features from each layer of the pre-trained convolutional
networks separately and aggregate them altogether given a long audio clip.
Finally, we put them into fully-connected networks and make final predictions
of the tags. Our experiments show that using the combination of multi-level and
multi-scale features is highly effective in music auto-tagging and the proposed
method outperforms previous state-of-the-arts on the MagnaTagATune dataset and
the Million Song Dataset. We further show that the proposed architecture is
useful in transfer learning.
An Unsupervised Algorithm For Learning Lie Group Transformations
Authors: Jascha Sohl-Dickstein, Ching Ming Wang, Bruno A. Olshausen
Date: 2010-01-07 00:00:00
Tasks: parameter estimation, Translation

We present several theoretical contributions which allow Lie groups to be fit
to high dimensional datasets. Transformation operators are represented in their
eigen-basis, reducing the computational complexity of parameter estimation to
that of training a linear transformation model. A transformation specific
"blurring" operator is introduced that allows inference to escape local minima
via a smoothing of the transformation space. A penalty on traversed manifold
distance is added which encourages the discovery of sparse, minimal distance,
transformations between states. Both learning and inference are demonstrated
using these methods for the full set of affine transformations on natural image
patches. Transformation operators are then trained on natural video sequences.
It is shown that the learned video transformations provide a better description
of inter-frame differences than the standard motion model based on rigid
translation.
Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network
Authors: Oluwatobi Olabiyi, Eric Martinson, Vijay Chintalapudi, Rui Guo
Date: 2017-06-07 00:00:00
Tasks: Prediction

Advanced driver assistance systems (ADAS) can be significantly improved with
effective driver action prediction (DAP). Predicting driver actions early and
accurately can help mitigate the effects of potentially unsafe driving
behaviors and avoid possible accidents. In this paper, we formulate driver
action prediction as a timeseries anomaly prediction problem. While the anomaly
(driver actions of interest) detection might be trivial in this context,
finding patterns that consistently precede an anomaly requires searching for or
extracting features across multi-modal sensory inputs. We present such a driver
action prediction system, including a real-time data acquisition, processing
and learning framework for predicting future or impending driver action. The
proposed system incorporates camera-based knowledge of the driving environment
and the driver themselves, in addition to traditional vehicle dynamics. It then
uses a deep bidirectional recurrent neural network (DBRNN) to learn the
correlation between sensory inputs and impending driver behavior achieving
accurate and high horizon action prediction. The proposed system performs
better than other existing systems on driver action prediction tasks and can
accurately predict key driver actions including acceleration, braking, lane
change and turning at durations of 5sec before the action is executed by the
driver.