pwc-archive/papers-with-abstracts huggingface dataset:
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
Authors: Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk
Date: 2017-07-11 00:00:00
Tasks: Compressive Sensing

In this paper we develop a novel computational sensing framework for sensing
and recovering structured signals. When trained on a set of representative
signals, our framework learns to take undersampled measurements and recover
signals from them using a deep convolutional neural network. In other words, it
learns a transformation from the original signals to a near-optimal number of
undersampled measurements and the inverse transformation from measurements to
signals. This is in contrast to traditional compressive sensing (CS) systems
that use random linear measurements and convex optimization or iterative
algorithms for signal recovery. We compare our new framework with
$\ell_1$-minimization from the phase transition point of view and demonstrate
that it outperforms $\ell_1$-minimization in the regions of phase transition
plot where $\ell_1$-minimization cannot recover the exact solution. In
addition, we experimentally demonstrate how learning measurements enhances the
overall recovery performance, speeds up training of recovery framework, and
leads to having fewer parameters to learn.
Deep Learning-Based Communication Over the Air
Authors: Sebastian Dörner, Sebastian Cammerer, Jakob Hoydis, Stephan ten Brink
Date: 2017-07-11 00:00:00
Tasks: Deep Learning, Transfer Learning

End-to-end learning of communications systems is a fascinating novel concept
that has so far only been validated by simulations for block-based
transmissions. It allows learning of transmitter and receiver implementations
as deep neural networks (NNs) that are optimized for an arbitrary
differentiable end-to-end performance metric, e.g., block error rate (BLER). In
this paper, we demonstrate that over-the-air transmissions are possible: We
build, train, and run a complete communications system solely composed of NNs
using unsynchronized off-the-shelf software-defined radios (SDRs) and
open-source deep learning (DL) software libraries. We extend the existing ideas
towards continuous data transmission which eases their current restriction to
short block lengths but also entails the issue of receiver synchronization. We
overcome this problem by introducing a frame synchronization module based on
another NN. A comparison of the BLER performance of the "learned" system with
that of a practical baseline shows competitive performance close to 1 dB, even
without extensive hyperparameter tuning. We identify several practical
challenges of training such a system over actual channels, in particular the
missing channel gradient, and propose a two-step learning procedure based on
the idea of transfer learning that circumvents this issue.
Optimizing Codes for Source Separation in Color Image Demosaicing and Compressive Video Recovery
Authors: Alankar Kotwal, Ajit Rajwade
Date: 2016-09-07 00:00:00
Tasks: compressed sensing, Demosaicking

There exist several applications in image processing (eg: video compressed
sensing [Hitomi, Y. et al, "Video from a single coded exposure photograph using
a learned overcomplete dictionary"] and color image demosaicing [Moghadam, A.
A. et al, "Compressive Framework for Demosaicing of Natural Images"]) which
require separation of constituent images given measurements in the form of a
coded superposition of those images. Physically practical code patterns in
these applications are non-negative, systematically structured, and do not
always obey the nice incoherence properties of other patterns such as Gaussian
codes, which can adversely affect reconstruction performance. The contribution
of this paper is to design code patterns for video compressed sensing and
demosaicing by minimizing the mutual coherence of the matrix $\boldsymbol{\Phi
\Psi}$ where $\boldsymbol{\Phi}$ represents the sensing matrix created from the
code, and $\boldsymbol{\Psi}$ is the signal representation matrix. Our main
contribution is that we explicitly take into account the special structure of
those code patterns as required by these applications: (1)~non-negativity,
(2)~block-diagonal nature, and (3)~circular shifting. In particular, the last
property enables for accurate and seamless patch-wise reconstruction for some
important compressed sensing architectures.
A step towards procedural terrain generation with GANs
Authors: Christopher Beckham, Christopher Pal
Date: 2017-07-11 00:00:00

Procedural terrain generation for video games has been traditionally been
done with smartly designed but handcrafted algorithms that generate heightmaps.
We propose a first step toward the learning and synthesis of these using recent
advances in deep generative modelling with openly available satellite imagery
from NASA.
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
Authors: YuXuan Liu, Abhishek Gupta, Pieter Abbeel, Sergey Levine
Date: 2017-07-11 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, Reinforcement Learning, Translation, Video Prediction

Imitation learning is an effective approach for autonomous systems to acquire
control policies when an explicit reward function is unavailable, using
supervision provided as demonstrations from an expert, typically a human
operator. However, standard imitation learning methods assume that the agent
receives examples of observation-action tuples that could be provided, for
instance, to a supervised learning algorithm. This stands in contrast to how
humans and animals imitate: we observe another person performing some behavior
and then figure out which actions will realize that behavior, compensating for
changes in viewpoint, surroundings, object positions and types, and other
factors. We term this kind of imitation learning "imitation-from-observation,"
and propose an imitation learning method based on video prediction with context
translation and deep reinforcement learning. This lifts the assumption in
imitation learning that the demonstration should consist of observations in the
same environment configuration, and enables a variety of interesting
applications, including learning robotic skills that involve tool use simply by
observing videos of human tool use. Our experimental results show the
effectiveness of our approach in learning a wide range of real-world robotic
tasks modeled after common household chores from videos of a human
demonstrator, including sweeping, ladling almonds, pushing objects as well as a
number of tasks in simulation.
Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search
Authors: Stephen Mussmann, Daniel Levy, Stefano Ermon
Date: 2017-07-11 00:00:00
Tasks: Word Embeddings

Inference in log-linear models scales linearly in the size of output space in
the worst-case. This is often a bottleneck in natural language processing and
computer vision tasks when the output space is feasibly enumerable but very
large. We propose a method to perform inference in log-linear models with
sublinear amortized cost. Our idea hinges on using Gumbel random variable
perturbations and a pre-computed Maximum Inner Product Search data structure to
access the most-likely elements in sublinear amortized time. Our method yields
provable runtime and accuracy guarantees. Further, we present empirical
experiments on ImageNet and Word Embeddings showing significant speedups for
sampling, inference, and learning in log-linear models.
Topic Identification for Speech without ASR
Authors: Chunxi Liu, Jan Trmal, Matthew Wiesner, Craig Harman, Sanjeev Khudanpur
Date: 2017-03-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, speech-recognition, Speech Recognition

Modern topic identification (topic ID) systems for speech use automatic
speech recognition (ASR) to produce speech transcripts, and perform supervised
classification on such ASR outputs. However, under resource-limited conditions,
the manually transcribed speech required to develop standard ASR systems can be
severely limited or unavailable. In this paper, we investigate alternative
unsupervised solutions to obtaining tokenizations of speech in terms of a
vocabulary of automatically discovered word-like or phoneme-like units, without
depending on the supervised training of ASR systems. Moreover, using automatic
phoneme-like tokenizations, we demonstrate that a convolutional neural network
based framework for learning spoken document representations provides
competitive performance compared to a standard bag-of-words representation, as
evidenced by comprehensive topic ID evaluations on both single-label and
multi-label classification tasks.
Stable Recovery Of Sparse Vectors From Random Sinusoidal Feature Maps
Authors: Mohammadreza Soltani, Chinmay Hegde
Date: 2017-01-23 00:00:00
Tasks: Dimensionality Reduction

Random sinusoidal features are a popular approach for speeding up
kernel-based inference in large datasets. Prior to the inference stage, the
approach suggests performing dimensionality reduction by first multiplying each
data vector by a random Gaussian matrix, and then computing an element-wise
sinusoid. Theoretical analysis shows that collecting a sufficient number of
such features can be reliably used for subsequent inference in kernel
classification and regression.
  In this work, we demonstrate that with a mild increase in the dimension of
the embedding, it is also possible to reconstruct the data vector from such
random sinusoidal features, provided that the underlying data is sparse enough.
In particular, we propose a numerically stable algorithm for reconstructing the
data vector given the nonlinear features, and analyze its sample complexity.
Our algorithm can be extended to other types of structured inverse problems,
such as demixing a pair of sparse (but incoherent) vectors. We support the
efficacy of our approach via numerical experiments.
PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI
Authors: Lei Tai, Haoyang Ye, Qiong Ye, Ming Liu
Date: 2016-10-06 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation of functional magnetic resonance imaging (fMRI) makes
great sense for pathology diagnosis and decision system of medical robots. The
multi-channel fMRI provides more information of the pathological features. But
the increased amount of data causes complexity in feature detections. This
paper proposes a principal component analysis (PCA)-aided fully convolutional
network to particularly deal with multi-channel fMRI. We transfer the learned
weights of contemporary classification networks to the segmentation task by
fine-tuning. The results of the convolutional network are compared with various
methods e.g. k-NN. A new labeling strategy is proposed to solve the semantic
segmentation problem with unclear boundaries. Even with a small-sized training
dataset, the test results demonstrate that our model outperforms other
pathological feature detection methods. Besides, its forward inference only
takes 90 milliseconds for a single set of fMRI data. To our knowledge, this is
the first time to realize pixel-wise labeling of multi-channel magnetic
resonance image using FCN.
CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
Authors: Adam Summerville, Joseph Osborn, Michael Mateas
Date: 2017-07-11 00:00:00
Tasks: Model Selection

We propose and evaluate a new technique for learning hybrid automata
automatically by observing the runtime behavior of a dynamical system. Working
from a sequence of continuous state values and predicates about the
environment, CHARDA recovers the distinct dynamic modes, learns a model for
each mode from a given set of templates, and postulates causal guard conditions
which trigger transitions between modes. Our main contribution is the use of
information-theoretic measures (1)~as a cost function for data segmentation and
model selection to penalize over-fitting and (2)~to determine the likely causes
of each transition. CHARDA is easily extended with different classes of model
templates, fitting methods, or predicates. In our experiments on a complex
videogame character, CHARDA successfully discovers a reasonable
over-approximation of the character's true behaviors. Our results also compare
favorably against recent work in automatically learning probabilistic timed
automata in an aircraft domain: CHARDA exactly learns the modes of these
simpler automata.
Automated Game Design Learning
Authors: Joseph C. Osborn, Adam Summerville, Michael Mateas
Date: 2017-07-11 00:00:00
Tasks: Game Design

While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.
Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization
Authors: Guanghui Lan, Zhiqiang Zhou
Date: 2017-07-11 00:00:00
Tasks: Stochastic Optimization

In this paper, we consider multi-stage stochastic optimization problems with convex objectives and conic constraints at each stage. We present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. We show that DSA can achieve an optimal ${\cal O}(1/\epsilon^4)$ rate of convergence in terms of the total number of required scenarios when applied to a three-stage stochastic optimization problem. We further show that this rate of convergence can be improved to ${\cal O}(1/\epsilon^2)$ when the objective function is strongly convex. We also discuss variants of DSA for solving more general multi-stage stochastic optimization problems with the number of stages $T > 3$. The developed DSA algorithms only need to go through the scenario tree once in order to compute an $\epsilon$-solution of the multi-stage stochastic optimization problem. As a result, the memory required by DSA only grows linearly with respect to the number of stages. To the best of our knowledge, this is the first time that stochastic approximation type methods are generalized for multi-stage stochastic optimization with $T \ge 3$.
Fast Algorithms for Learning Latent Variables in Graphical Models
Authors: Mohammadreza Soltani, Chinmay Hegde
Date: 2017-06-27 00:00:00

We study the problem of learning latent variables in Gaussian graphical
models. Existing methods for this problem assume that the precision matrix of
the observed variables is the superposition of a sparse and a low-rank
component. In this paper, we focus on the estimation of the low-rank component,
which encodes the effect of marginalization over the latent variables. We
introduce fast, proper learning algorithms for this problem. In contrast with
existing approaches, our algorithms are manifestly non-convex. We support their
efficacy via a rigorous theoretical analysis, and show that our algorithms
match the best possible in terms of sample complexity, while achieving
computational speed-ups over existing methods. We complement our theory with
several numerical experiments.
Similarity Search Over Graphs Using Localized Spectral Analysis
Authors: Yariv Aizenbud, Amir Averbuch, Gil Shabat, Guy Ziv
Date: 2017-07-11 00:00:00

This paper provides a new similarity detection algorithm. Given an input set
of multi-dimensional data points, where each data point is assumed to be
multi-dimensional, and an additional reference data point for similarity
finding, the algorithm uses kernel method that embeds the data points into a
low dimensional manifold. Unlike other kernel methods, which consider the
entire data for the embedding, our method selects a specific set of kernel
eigenvectors. The eigenvectors are chosen to separate between the data points
and the reference data point so that similar data points can be easily
identified as being distinct from most of the members in the dataset.
Neural-based Natural Language Generation in Dialogue using RNN Encoder-Decoder with Semantic Aggregation
Authors: Van-Khanh Tran, Le-Minh Nguyen
Date: 2017-06-21 00:00:00
Proceeding: WS 2017 8
Tasks: Decoder, Sentence, Spoken Dialogue Systems, Text Generation

Natural language generation (NLG) is an important component in spoken
dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder
which is an extension of an Recurrent Neural Network based Encoder-Decoder
architecture. The proposed Semantic Aggregator consists of two components: an
Aligner and a Refiner. The Aligner is a conventional attention calculated over
the encoded input information, while the Refiner is another attention or gating
mechanism stacked over the attentive Aligner in order to further select and
aggregate the semantic elements. The proposed model can be jointly trained both
sentence planning and surface realization to produce natural language
utterances. The model was extensively assessed on four different NLG domains,
in which the experimental results showed that the proposed generator
consistently outperforms the previous methods on all the NLG domains.
Semantic Refinement GRU-based Neural Language Generation for Spoken Dialogue Systems
Authors: Van-Khanh Tran, Le-Minh Nguyen
Date: 2017-06-01 00:00:00
Tasks: Sentence, Spoken Dialogue Systems, Text Generation

Natural language generation (NLG) plays a critical role in spoken dialogue
systems. This paper presents a new approach to NLG by using recurrent neural
networks (RNN), in which a gating mechanism is applied before RNN computation.
This allows the proposed model to generate appropriate sentences. The RNN-based
generator can be learned from unaligned data by jointly training sentence
planning and surface realization to produce natural language responses. The
model was extensively evaluated on four different NLG domains. The results show
that the proposed generator achieved better performance on all the NLG domains
compared to previous generators.
The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously
Authors: Serkan Cabi, Sergio Gómez Colmenarejo, Matthew W. Hoffman, Misha Denil, Ziyu Wang, Nando de Freitas
Date: 2017-07-11 00:00:00
Tasks: continuous-control, Continuous Control, MuJoCo

This paper introduces the Intentional Unintentional (IU) agent. This agent
endows the deep deterministic policy gradients (DDPG) agent for continuous
control with the ability to solve several tasks simultaneously. Learning to
solve many tasks simultaneously has been a long-standing, core goal of
artificial intelligence, inspired by infant development and motivated by the
desire to build flexible robot manipulators capable of many diverse behaviours.
We show that the IU agent not only learns to solve many tasks simultaneously
but it also learns faster than agents that target a single task at-a-time. In
some cases, where the single task DDPG method completely fails, the IU agent
successfully solves the task. To demonstrate this, we build a playroom
environment using the MuJoCo physics engine, and introduce a grounded formal
language to automatically generate tasks.
Hierarchical Deep Recurrent Architecture for Video Understanding
Authors: Luming Tang, Boyang Deng, Haiyu Zhao, Shuai Yi
Date: 2017-07-11 00:00:00
Tasks: Classification, General Classification, Mixture-of-Experts, Video Classification, Video Understanding

This paper introduces the system we developed for the Youtube-8M Video
Understanding Challenge, in which a large-scale benchmark dataset was used for
multi-label video classification. The proposed framework contains hierarchical
deep architecture, including the frame-level sequence modeling part and the
video-level classification part. In the frame-level sequence modelling part, we
explore a set of methods including Pooling-LSTM (PLSTM), Hierarchical-LSTM
(HLSTM), Random-LSTM (RLSTM) in order to address the problem of large amount of
frames in a video. We also introduce two attention pooling methods, single
attention pooling (ATT) and multiply attention pooling (Multi-ATT) so that we
can pay more attention to the informative frames in a video and ignore the
useless frames. In the video-level classification part, two methods are
proposed to increase the classification performance, i.e.
Hierarchical-Mixture-of-Experts (HMoE) and Classifier Chains (CC). Our final
submission is an ensemble consisting of 18 sub-models. In terms of the official
evaluation metric Global Average Precision (GAP) at 20, our best submission
achieves 0.84346 on the public 50% of test dataset and 0.84333 on the private
50% of test data.
Modeling the dynamics of domain specific terminology in diachronic corpora
Authors: Gerhard Heyer, Cathleen Kantner, Andreas Niekler, Max Overbeck, Gregor Wiedemann
Date: 2017-07-11 00:00:00
Tasks: Term Extraction

In terminology work, natural language processing, and digital humanities,
several studies address the analysis of variations in context and meaning of
terms in order to detect semantic change and the evolution of terms. We
distinguish three different approaches to describe contextual variations:
methods based on the analysis of patterns and linguistic clues, methods
exploring the latent semantic space of single words, and methods for the
analysis of topic membership. The paper presents the notion of context
volatility as a new measure for detecting semantic change and applies it to key
term extraction in a political science case study. The measure quantifies the
dynamics of a term's contextual variation within a diachronic corpus to
identify periods of time that are characterised by intense controversial
debates or substantial semantic transformations.
Leipzig Corpus Miner - A Text Mining Infrastructure for Qualitative Data Analysis
Authors: Andreas Niekler, Gregor Wiedemann, Gerhard Heyer
Date: 2017-07-11 00:00:00
Tasks: Information Retrieval, Retrieval

This paper presents the "Leipzig Corpus Miner", a technical infrastructure
for supporting qualitative and quantitative content analysis. The
infrastructure aims at the integration of 'close reading' procedures on
individual documents with procedures of 'distant reading', e.g. lexical
characteristics of large document collections. Therefore information retrieval
systems, lexicometric statistics and machine learning procedures are combined
in a coherent framework which enables qualitative data analysts to make use of
state-of-the-art Natural Language Processing techniques on very large document
collections. Applicability of the framework ranges from social sciences to
media studies and market research. As an example we introduce the usage of the
framework in a political science study on post-democracy and neoliberalism.