pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Robot Transfer Learning: A Dynamical System Perspective
Authors: Mohamed K. Helwa, Angela P. Schoellig
Date: 2017-07-27 00:00:00
Tasks: Transfer Learning

Multi-robot transfer learning allows a robot to use data generated by a
second, similar robot to improve its own behavior. The potential advantages are
reducing the time of training and the unavoidable risks that exist during the
training phase. Transfer learning algorithms aim to find an optimal transfer
map between different robots. In this paper, we investigate, through a
theoretical study of single-input single-output (SISO) systems, the properties
of such optimal transfer maps. We first show that the optimal transfer learning
map is, in general, a dynamic system. The main contribution of the paper is to
provide an algorithm for determining the properties of this optimal dynamic map
including its order and regressors (i.e., the variables it depends on). The
proposed algorithm does not require detailed knowledge of the robots' dynamics,
but relies on basic system properties easily obtainable through simple
experimental tests. We validate the proposed algorithm experimentally through
an example of transfer learning between two different quadrotor platforms.
Experimental results show that an optimal dynamic map, with correct properties
obtained from our proposed algorithm, achieves 60-70% reduction of transfer
learning error compared to the cases when the data is directly transferred or
transferred using an optimal static map.
Naturally Combined Shape-Color Moment Invariants under Affine Transformations
Authors: Ming Gong, You Hao, Hanlin Mo, Hua Li
Date: 2017-05-31 00:00:00

We proposed a kind of naturally combined shape-color affine moment invariants
(SCAMI), which consider both shape and color affine transformations
simultaneously in one single system. In the real scene, color and shape
deformations always exist in images simultaneously. Simple shape invariants or
color invariants can not be qualified for this situation. The conventional
method is just to make a simple linear combination of the two factors.
Meanwhile, the manual selection of weights is a complex issue. Our construction
method is based on the multiple integration framework. The integral kernel is
assigned as the continued product of the shape and color invariant cores. It is
the first time to directly derive an invariant to dual affine transformations
of shape and color. The manual selection of weights is no longer necessary, and
both the shape and color transformations are extended to affine transformation
group. With the various of invariant cores, a set of lower-order invariants are
constructed and the completeness and independence are discussed detailedly. A
set of SCAMIs, which called SCAMI24, are recommended, and the effectiveness and
robustness have been evaluated on both synthetic and real datasets.
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks
Authors: Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Prateek Vij
Date: 2016-10-27 00:00:00
Proceeding: COLING 2016 12
Tasks: Sarcasm Detection, Sentence, Sentiment Analysis, Text Categorization

Sarcasm detection is a key task for many natural language processing tasks.
In sentiment analysis, for example, sarcasm can flip the polarity of an
"apparently positive" sentence and, hence, negatively affect polarity detection
performance. To date, most approaches to sarcasm detection have treated the
task primarily as a text categorization problem. Sarcasm, however, can be
expressed in very subtle ways and requires a deeper understanding of natural
language that standard text categorization techniques cannot grasp. In this
work, we develop models based on a pre-trained convolutional neural network for
extracting sentiment, emotion and personality features for sarcasm detection.
Such features, along with the network's baseline features, allow the proposed
models to outperform the state of the art on benchmark datasets. We also
address the often ignored generalizability issue of classifying data that have
not been seen by the models at learning phase.
Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation
Authors: Margret Keuper
Date: 2017-04-06 00:00:00
Proceeding: ICCV 2017 10
Tasks: Motion Segmentation, Segmentation

Most state-of-the-art motion segmentation algorithms draw their potential
from modeling motion differences of local entities such as point trajectories
in terms of pairwise potentials in graphical models. Inference in instances of
minimum cost multicut problems defined on such graphs al- lows to optimize the
number of the resulting segments along with the segment assignment. However,
pairwise potentials limit the discriminative power of the employed motion
models to translational differences. More complex models such as Euclidean or
affine transformations call for higher-order potentials and a tractable
inference in the resulting higher-order graphical models. In this paper, we (1)
introduce a generalization of the minimum cost lifted multicut problem to
hypergraphs, and (2) propose a simple primal feasible heuristic that allows for
a reasonably efficient inference in instances of higher-order lifted multicut
problem instances defined on point trajectory hypergraphs for motion
segmentation. The resulting motion segmentations improve over the
state-of-the-art on the FBMS-59 dataset.
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions
Authors: Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
Date: 2017-07-26 00:00:00
Proceeding: WS 2017 8
Tasks: Natural Language Understanding

Robots operating alongside humans in diverse, stochastic environments must be
able to accurately interpret natural language commands. These instructions
often fall into one of two categories: those that specify a goal condition or
target state, and those that specify explicit actions, or how to perform a
given task. Recent approaches have used reward functions as a semantic
representation of goal-based commands, which allows for the use of a
state-of-the-art planner to find a policy for the given task. However, these
reward functions cannot be directly used to represent action-oriented commands.
We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding
Network (DRAGGN), for task grounding and execution that handles natural
language from either category as input, and generalizes to unseen environments.
Our robot-simulation results demonstrate that a system successfully
interpreting both goal-oriented and action-oriented task specifications brings
us closer to robust natural language understanding for human-robot interaction.
Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants
Authors: Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
Date: 2017-07-26 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Word Embeddings

This paper deals with using word embedding models to trace the temporal
dynamics of semantic relations between pairs of words. The set-up is similar to
the well-known analogies task, but expanded with a time dimension. To this end,
we apply incremental updating of the models with new training texts, including
incremental vocabulary expansion, coupled with learned transformation matrices
that let us map between members of the relation. The proposed approach is
evaluated on the task of predicting insurgent armed groups based on
geographical locations. The gold standard data for the time span 1994--2010 is
extracted from the UCDP Armed Conflicts dataset. The results show that the
method is feasible and outperforms the baselines, but also that important work
still remains to be done.
Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition
Authors: Yuan Zong, Xiaohua Huang, Wenming Zheng, Zhen Cui, Guoying Zhao
Date: 2017-07-26 00:00:00
Tasks: Emotion Recognition, Micro Expression Recognition, Micro-Expression Recognition

In this paper, we investigate the cross-database micro-expression recognition
problem, where the training and testing samples are from two different
micro-expression databases. Under this setting, the training and testing
samples would have different feature distributions and hence the performance of
most existing micro-expression recognition methods may decrease greatly. To
solve this problem, we propose a simple yet effective method called Target
Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to
re-generate the samples from target micro-expression database and the
re-generated target samples would share same or similar feature distributions
with the original source samples. For this reason, we can then use the
classifier learned based on the labeled source samples to accurately predict
the micro-expression categories of the unlabeled target samples. To evaluate
the performance of the proposed TSRG method, extensive cross-database
micro-expression recognition experiments designed based on SMIC and CASME II
databases are conducted. Compared with recent state-of-the-art cross-database
emotion recognition methods, the proposed TSRG achieves more promising results.
Learning Generic Sentence Representations Using Convolutional Neural Networks
Authors: Zhe Gan, Yunchen Pu, Ricardo Henao, Chunyuan Li, Xiaodong He, Lawrence Carin
Date: 2016-11-23 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Sentence

We propose a new encoder-decoder approach to learn distributed sentence
representations that are applicable to multiple purposes. The model is learned
by using a convolutional neural network as an encoder to map an input sentence
into a continuous vector, and using a long short-term memory recurrent neural
network as a decoder. Several tasks are considered, including sentence
reconstruction and future sentence prediction. Further, a hierarchical
encoder-decoder model is proposed to encode a sentence to predict multiple
future sentences. By training our models on a large collection of novels, we
obtain a highly generic convolutional sentence encoder that performs well in
practice. Experimental results on several benchmark datasets, and across a
broad range of applications, demonstrate the superiority of the proposed model
over competing methods.
A unified method for super-resolution recovery and real exponential-sum separation
Authors: Charles K. Chui, Hrushikesh N. Mhaskar
Date: 2017-07-26 00:00:00
Tasks: Astronomy, blind source separation, Clustering, Super-Resolution

In this paper, motivated by diffraction of traveling light waves, a simple
mathematical model is proposed, both for the multivariate super-resolution
problem and the problem of blind-source separation of real-valued exponential
sums. This model facilitates the development of a unified theory and a unified
solution of both problems in this paper. Our consideration of the
super-resolution problem is aimed at applications to fluorescence microscopy
and observational astronomy, and the motivation for our consideration of the
second problem is the current need of extracting multivariate exponential
features in magnetic resonance spectroscopy (MRS) for the neurologist and
radiologist as well as for providing a mathematical tool for isotope separation
in Nuclear Chemistry. The unified method introduced in this paper can be easily
realized by processing only finitely many data, sampled at locations that are
not necessarily prescribed in advance, with computational scheme consisting
only of matrix - vector multiplication, peak finding, and clustering.
A Fourier-invariant method for locating point-masses and computing their attributes
Authors: Charles K. Chui, Hrushikesh N. Mhaskar
Date: 2017-07-26 00:00:00

Motivated by the interest of observing the growth of cancer cells among
normal living cells and exploring how galaxies and stars are truly formed, the
objective of this paper is to introduce a rigorous and effective method for
counting point-masses, determining their spatial locations, and computing their
attributes. Based on computation of Hermite moments that are Fourier-invariant,
our approach facilitates the processing of both spatial and Fourier data in any
dimension.
Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models
Authors: Can Pu, Nanbo Li, Robert B. Fisher
Date: 2017-07-26 00:00:00

Matching 3D rigid point clouds in complex environments robustly and
accurately is still a core technique used in many applications. This paper
proposes a new architecture combining error estimation from sample covariances
and dual global probability alignment based on the convolution of adaptive
Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM
is defined using probability distributions from the corresponding points. Then
rigid point cloud alignment is performed by maximizing the global probability
from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which
can be efficiently optimized and has a large zone of accurate convergence.
Thousands of trials have been conducted on 200 models from public 2D and 3D
datasets to demonstrate superior robustness and accuracy in complex
environments with unpredictable noise, outliers, occlusion, initial rotation,
shape and missing points.
Convolutional Experts Constrained Local Model for Facial Landmark Detection
Authors: Amir Zadeh, Tadas Baltrušaitis, Louis-Philippe Morency
Date: 2016-11-26 00:00:00
Tasks: Facial Landmark Detection

Constrained Local Models (CLMs) are a well-established family of methods for
facial landmark detection. However, they have recently fallen out of favor to
cascaded regression-based approaches. This is in part due to the inability of
existing CLM local detectors to model the very complex individual landmark
appearance that is affected by expression, illumination, facial hair, makeup,
and accessories. In our work, we present a novel local detector --
Convolutional Experts Network (CEN) -- that brings together the advantages of
neural architectures and mixtures of experts in an end-to-end framework. We
further propose a Convolutional Experts Constrained Local Model (CE-CLM)
algorithm that uses CEN as local detectors. We demonstrate that our proposed
CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial
landmark detection by a large margin on four publicly-available datasets. Our
approach is especially accurate and robust on challenging profile images.
Gradient-based Inference for Networks with Output Constraints
Authors: Jay Yoon Lee, Sanket Vaibhav Mehta, Michael Wick, Jean-Baptiste Tristan, Jaime Carbonell
Date: 2017-07-26 00:00:00
Tasks: Constituency Parsing, Semantic Role Labeling, Structured Prediction, valid

Practitioners apply neural networks to increasingly complex problems in
natural language processing, such as syntactic parsing and semantic role
labeling that have rich output structures. Many such structured-prediction
problems require deterministic constraints on the output values; for example,
in sequence-to-sequence syntactic parsing, we require that the sequential
outputs encode valid trees. While hidden units might capture such properties,
the network is not always able to learn such constraints from the training data
alone, and practitioners must then resort to post-processing. In this paper, we
present an inference method for neural networks that enforces deterministic
constraints on outputs without performing rule-based post-processing or
expensive discrete search. Instead, in the spirit of gradient-based training,
we enforce constraints with gradient-based inference (GBI): for each input at
test-time, we nudge continuous model weights until the network's unconstrained
inference procedure generates an output that satisfies the constraints. We
study the efficacy of GBI on three tasks with hard constraints: semantic role
labeling, syntactic parsing, and sequence transduction. In each case, the
algorithm not only satisfies constraints but improves accuracy, even when the
underlying network is state-of-the-art.
Self-Correcting Models for Model-Based Reinforcement Learning
Authors: Erik Talvitie
Date: 2016-12-19 00:00:00
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When an agent cannot represent a perfectly accurate model of its
environment's dynamics, model-based reinforcement learning (MBRL) can fail
catastrophically. Planning involves composing the predictions of the model;
when flawed predictions are composed, even minor errors can compound and render
the model useless for planning. Hallucinated Replay (Talvitie 2014) trains the
model to "correct" itself when it produces errors, substantially improving MBRL
with flawed models. This paper theoretically analyzes this approach,
illuminates settings in which it is likely to be effective or ineffective, and
presents a novel error bound, showing that a model's ability to self-correct is
more tightly related to MBRL performance than one-step prediction error. These
results inspire an MBRL algorithm for deterministic MDPs with performance
guarantees that are robust to model class limitations.
Discretization-free Knowledge Gradient Methods for Bayesian Optimization
Authors: Jian Wu, Peter I. Frazier
Date: 2017-07-20 00:00:00
Tasks: Bayesian Optimization

This paper studies Bayesian ranking and selection (R&S) problems with
correlated prior beliefs and continuous domains, i.e. Bayesian optimization
(BO). Knowledge gradient methods [Frazier et al., 2008, 2009] have been widely
studied for discrete R&S problems, which sample the one-step Bayes-optimal
point. When used over continuous domains, previous work on the knowledge
gradient [Scott et al., 2011, Wu and Frazier, 2016, Wu et al., 2017] often rely
on a discretized finite approximation. However, the discretization introduces
error and scales poorly as the dimension of domain grows. In this paper, we
develop a fast discretization-free knowledge gradient method for Bayesian
optimization. Our method is not restricted to the fully sequential setting, but
useful in all settings where knowledge gradient can be used over continuous
domains. We show how our method can be generalized to handle (i) batch of
points suggestion (parallel knowledge gradient); (ii) the setting where
derivative information is available in the optimization process
(derivative-enabled knowledge gradient). In numerical experiments, we
demonstrate that the discretization-free knowledge gradient method finds global
optima significantly faster than previous Bayesian optimization algorithms on
both synthetic test functions and real-world applications, especially when
function evaluations are noisy; and derivative-enabled knowledge gradient can
further improve the performances, even outperforming the gradient-based
optimizer such as BFGS when derivative information is available.
Self-organized Hierarchical Softmax
Authors: Yikang Shen, Shawn Tan, Chrisopher Pal, Aaron Courville
Date: 2017-07-26 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, Sentence Compression

We propose a new self-organizing hierarchical softmax formulation for
neural-network-based language models over large vocabularies. Instead of using
a predefined hierarchical structure, our approach is capable of learning word
clusters with clear syntactical and semantic meaning during the language model
training process. We provide experiments on standard benchmarks for language
modeling and sentence compression tasks. We find that this approach is as fast
as other efficient softmax approximations, while achieving comparable or even
better performance relative to similar full softmax models.
NormFace: L2 Hypersphere Embedding for Face Verification
Authors: Feng Wang, Xiang Xiang, Jian Cheng, Alan L. Yuille
Date: 2017-04-21 00:00:00
Tasks: Face Verification, Metric Learning

Thanks to the recent developments of Convolutional Neural Networks, the
performance of face verification methods has increased rapidly. In a typical
face verification method, feature normalization is a critical step for boosting
performance. This motivates us to introduce and study the effect of
normalization during training. But we find this is non-trivial, despite
normalization being differentiable. We identify and study four issues related
to normalization through mathematical analysis, which yields understanding and
helps with parameter settings. Based on this analysis we propose two strategies
for training using normalized features. The first is a modification of softmax
loss, which optimizes cosine similarity instead of inner-product. The second is
a reformulation of metric learning by introducing an agent vector for each
class. We show that both strategies, and small variants, consistently improve
performance by between 0.2% to 0.4% on the LFW dataset based on two models.
This is significant because the performance of the two models on LFW dataset is
close to saturation at over 98%. Codes and models are released on
https://github.com/happynear/NormFace
Video Highlight Prediction Using Audience Chat Reactions
Authors: Cheng-Yang Fu, Joon Lee, Mohit Bansal, Alexander C. Berg
Date: 2017-07-26 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Prediction

Sports channel video portals offer an exciting domain for research on
multimodal, multilingual analysis. We present methods addressing the problem of
automatic video highlight prediction based on joint visual features and textual
analysis of the real-world audience discourse with complex slang, in both
English and traditional Chinese. We present a novel dataset based on League of
Legends championships recorded from North American and Taiwanese Twitch.tv
channels (will be released for further research), and demonstrate strong
results on these using multimodal, character-level CNN-RNN model architectures.
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning
Authors: Frederik Ruelens, Bert J. Claessens, Peter Vrancx, Fred Spiessens, Geert Deconinck
Date: 2017-07-26 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Time Series, Time Series Analysis

This paper considers a demand response agent that must find a near-optimal
sequence of decisions based on sparse observations of its environment.
Extracting a relevant set of features from these observations is a challenging
task and may require substantial domain knowledge. One way to tackle this
problem is to store sequences of past observations and actions in the state
vector, making it high dimensional, and apply techniques from deep learning.
This paper investigates the capabilities of different deep learning techniques,
such as convolutional neural networks and recurrent neural networks, to extract
relevant features for finding near-optimal policies for a residential heating
system and electric water heater that are hindered by sparse observations. Our
simulation results indicate that in this specific scenario, feeding sequences
of time-series to an LSTM network, which is a specific type of recurrent neural
network, achieved a higher performance than stacking these time-series in the
input of a convolutional neural network or deep neural network.