pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Approach for Trajectory Control Design
Authors: Luigi Freda, Mario Gianni, Fiora Pirri
Date: 2018-10-08 00:00:00
Tasks: Gaussian Processes

This work presents a methodology to design trajectory tracking feedback control laws, which embed non-parametric statistical models, such as Gaussian Processes (GPs). The aim is to minimize unmodeled dynamics such as undesired slippages. The proposed approach has the benefit of avoiding complex terramechanics analysis to directly estimate from data the robot dynamics on a wide class of trajectories. Experiments in both real and simulated environments prove that the proposed methodology is promising.
Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations
Authors: Mahdi Rad, Markus Oberweger, Vincent Lepetit
Date: 2018-10-08 00:00:00
Tasks: 3D Hand Pose Estimation, 3D Pose Estimation, Hand Pose Estimation, Pose Estimation

We introduce a novel learning method for 3D pose estimation from color
images. While acquiring annotations for color images is a difficult task, our
approach circumvents this problem by learning a mapping from paired color and
depth images captured with an RGB-D camera. We jointly learn the pose from
synthetic depth images that are easy to generate, and learn to align these
synthetic depth images with the real depth images. We show our approach for the
task of 3D hand pose estimation and 3D object pose estimation, both from color
images only. Our method achieves performances comparable to state-of-the-art
methods on popular benchmark datasets, without requiring any annotations for
the color images.
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access
Authors: Chen Zhong, Ziyang Lu, M. Cenk Gursoy, Senem Velipasalar
Date: 2018-10-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the dynamic multichannel access problem, which can be formulated
as a partially observable Markov decision process (POMDP). We first propose a
model-free actor-critic deep reinforcement learning based framework to explore
the sensing policy. To evaluate the performance of the proposed sensing policy
and the framework's tolerance against uncertainty, we test the framework in
scenarios with different channel switching patterns and consider different
switching probabilities. Then, we consider a time-varying environment to
identify the adaptive ability of the proposed framework. Additionally, we
provide comparisons with the Deep-Q network (DQN) based framework proposed in
[1], in terms of both average reward and the time efficiency.
Multi-agent Deep Reinforcement Learning for Zero Energy Communities
Authors: Amit Prasad, Ivana Dusparic
Date: 2018-10-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a building whose net energy usage over a year is zero, i.e., its energy use is not larger than its overall renewables generation. A collection of ZEBs forms a Zero Energy Community (ZEC). This paper addresses the problem of energy sharing in such a community. This is different from previously addressed energy sharing between buildings as our focus is on the improvement of community energy status, while traditionally research focused on reducing losses due to transmission and storage, or achieving economic gains. We model this problem in a multi-agent environment and propose a Deep Reinforcement Learning (DRL) based solution. Each building is represented by an intelligent agent that learns over time the appropriate behaviour to share energy. We have evaluated the proposed solution in a multi-agent simulation built using osBrain. Results indicate that with time agents learn to collaborate and learn a policy comparable to the optimal policy, which in turn improves the ZEC's energy status. Buildings with no renewables preferred to request energy from their neighbours rather than from the supply grid.
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques
Authors: Oscar Araque, Lorenzo Gatti, Jacopo Staiano, Marco Guerini
Date: 2018-10-08 00:00:00
Tasks: Emotion Recognition, Sentiment Analysis, Specificity

Several lexica for sentiment analysis have been developed and made available
in the NLP community. While most of these come with word polarity annotations
(e.g. positive/negative), attempts at building lexica for finer-grained emotion
analysis (e.g. happiness, sadness) have recently attracted significant
attention. Such lexica are often exploited as a building block in the process
of developing learning models for which emotion recognition is needed, and/or
used as baselines to which compare the performance of the models. In this work,
we contribute two new resources to the community: a) an extension of an
existing and widely used emotion lexicon for English; and b) a novel version of
the lexicon targeting Italian. Furthermore, we show how simple techniques can
be used, both in supervised and unsupervised experimental settings, to boost
performances on datasets and tasks of varying degree of domain-specificity.
Recognizing Overlapped Speech in Meetings: A Multichannel Separation Approach Using Neural Networks
Authors: Takuya Yoshioka, Hakan Erdogan, Zhuo Chen, Xiong Xiao, Fil Alleva
Date: 2018-10-08 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

The goal of this work is to develop a meeting transcription system that can
recognize speech even when utterances of different speakers are overlapped.
While speech overlaps have been regarded as a major obstacle in accurately
transcribing meetings, a traditional beamformer with a single output has been
exclusively used because previously proposed speech separation techniques have
critical constraints for application to real meetings. This paper proposes a
new signal processing module, called an unmixing transducer, and describes its
implementation using a windowed BLSTM. The unmixing transducer has a fixed
number, say J, of output channels, where J may be different from the number of
meeting attendees, and transforms an input multi-channel acoustic signal into J
time-synchronous audio streams. Each utterance in the meeting is separated and
emitted from one of the output channels. Then, each output signal can be simply
fed to a speech recognition back-end for segmentation and transcription. Our
meeting transcription system using the unmixing transducer outperforms a system
based on a state-of-the-art neural mask-based beamformer by 10.8%. Significant
improvements are observed in overlapped segments. To the best of our knowledge,
this is the first report that applies overlapped speech recognition to
unconstrained real meeting audio.
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos
Authors: Yang Wang, Zhenheng Yang, Peng Wang, Yi Yang, Chenxu Luo, Wei Xu
Date: 2018-10-08 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

Learning depth and optical flow via deep neural networks by watching videos
has made significant progress recently. In this paper, we jointly solve the two
tasks by exploiting the underlying geometric rules within stereo videos.
Specifically, given two consecutive stereo image pairs from a video, we first
estimate depth, camera ego-motion and optical flow from three neural networks.
Then the whole scene is decomposed into moving foreground and static background
by compar- ing the estimated optical flow and rigid flow derived from the depth
and ego-motion. We propose a novel consistency loss to let the optical flow
learn from the more accurate rigid flow in static regions. We also design a
rigid alignment module which helps refine ego-motion estimation by using the
estimated depth and optical flow. Experiments on the KITTI dataset show that
our results significantly outperform other state-of- the-art algorithms. Source
codes can be found at https: //github.com/baidu-research/UnDepthflow
An ensemble based on a bi-objective evolutionary spectral algorithm for graph clustering
Authors: Camila P. S. Tautenhain, Mariá C. V. Nascimento
Date: 2018-10-08 00:00:00
Tasks: Clustering, Graph Clustering

Graph clustering is a challenging pattern recognition problem whose goal is to identify vertex partitions with high intra-group connectivity. This paper investigates a bi-objective problem that maximizes the number of intra-cluster edges of a graph and minimizes the expected number of inter-cluster edges in a random graph with the same degree sequence as the original one. The difference between the two investigated objectives is the definition of the well-known measure of graph clustering quality: the modularity. We introduce a spectral decomposition hybridized with an evolutionary heuristic, called MOSpecG, to approach this bi-objective problem and an ensemble strategy to consolidate the solutions found by MOSpecG into a final robust partition. The results of computational experiments with real and artificial LFR networks demonstrated a significant improvement in the results and performance of the introduced method in regard to another bi-objective algorithm found in the literature. The crossover operator based on the geometric interpretation of the modularity maximization problem to match the communities of a pair of individuals was of utmost importance for the good performance of MOSpecG. Hybridizing spectral graph theory and intelligent systems allowed us to define significantly high-quality community structures.
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization
Authors: Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee
Date: 2018-10-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Question Answering, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Modern Visual Question Answering (VQA) models have been shown to rely heavily
on superficial correlations between question and answer words learned during
training such as overwhelmingly reporting the type of room as kitchen or the
sport being played as tennis, irrespective of the image. Most alarmingly, this
shortcoming is often not well reflected during evaluation because the same
strong priors exist in test distributions; however, a VQA system that fails to
ground questions in image content would likely perform poorly in real-world
settings. In this work, we present a novel regularization scheme for VQA that
reduces this effect. We introduce a question-only model that takes as input the
question encoding from the VQA model and must leverage language biases in order
to succeed. We then pose training as an adversarial game between the VQA model
and this question-only adversary -- discouraging the VQA model from capturing
language biases in its question encoding. Further,we leverage this
question-only model to estimate the increase in model confidence after
considering the image, which we maximize explicitly to encourage visual
grounding. Our approach is a model agnostic training procedure and simple to
implement. We show empirically that it can improve performance significantly on
a bias-sensitive split of the VQA dataset for multiple base models -- achieving
state-of-the-art on this task. Further, on standard VQA tasks, our approach
shows significantly less drop in accuracy compared to existing bias-reducing
VQA models.
Fast Context Adaptation via Meta-Learning
Authors: Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson
Date: 2018-10-08 00:00:00
Tasks: General Classification, Meta-Learning, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.
Understanding the Origins of Bias in Word Embeddings
Authors: Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, Richard Zemel
Date: 2018-10-08 00:00:00
Tasks: BIG-bench Machine Learning, Translation, Word Embeddings

The power of machine learning systems not only promises great technical progress, but risks societal harm. As a recent example, researchers have shown that popular word embedding algorithms exhibit stereotypical biases, such as gender bias. The widespread use of these algorithms in machine learning systems, from automated translation services to curriculum vitae scanners, can amplify stereotypes in important contexts. Although methods have been developed to measure these biases and alter word embeddings to mitigate their biased representations, there is a lack of understanding in how word embedding bias depends on the training data. In this work, we develop a technique for understanding the origins of bias in word embeddings. Given a word embedding trained on a corpus, our method identifies how perturbing the corpus will affect the bias of the resulting embedding. This can be used to trace the origins of word embedding bias back to the original training documents. Using our method, one can investigate trends in the bias of the underlying corpus and identify subsets of documents whose removal would most reduce bias. We demonstrate our techniques on both a New York Times and Wikipedia corpus and find that our influence function-based approximations are very accurate.
The 30-Year Cycle In The AI Debate
Authors: Jean-Marie Chauvet
Date: 2018-10-08 00:00:00
Tasks: BIG-bench Machine Learning

In the last couple of years, the rise of Artificial Intelligence and the
successes of academic breakthroughs in the field have been inescapable. Vast
sums of money have been thrown at AI start-ups. Many existing tech companies --
including the giants like Google, Amazon, Facebook, and Microsoft -- have
opened new research labs. The rapid changes in these everyday work and
entertainment tools have fueled a rising interest in the underlying technology
itself; journalists write about AI tirelessly, and companies -- of tech nature
or not -- brand themselves with AI, Machine Learning or Deep Learning whenever
they get a chance. Confronting squarely this media coverage, several analysts
are starting to voice concerns about over-interpretation of AI's blazing
successes and the sometimes poor public reporting on the topic. This paper
reviews briefly the track-record in AI and Machine Learning and finds this
pattern of early dramatic successes, followed by philosophical critique and
unexpected difficulties, if not downright stagnation, returning almost to the
clock in 30-year cycles since 1958.
The infinite Viterbi alignment and decay-convexity
Authors: Nick Whiteley, Matt W. Jones, Aleks P. F. Domanski
Date: 2018-10-08 00:00:00

The infinite Viterbi alignment is the limiting maximum a-posteriori estimate of the unobserved path in a hidden Markov model as the length of the time horizon grows. For models on state-space $\mathbb{R}^{d}$ satisfying a new ``decay-convexity'' condition, we develop an approach to existence of the infinite Viterbi alignment in an infinite dimensional Hilbert space. Quantitative bounds on the distance to the infinite Viterbi alignment, which are the first of their kind, are derived and used to illustrate how approximate estimation via parallelization can be accurate and scaleable to high-dimensional problems because the rate of convergence to the infinite Viterbi alignment does not necessarily depend on $d$. The results are applied to approximate estimation via parallelization and a model of neural population activity.
Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective
Authors: Filip Tronarp, Hans Kersting, Simo Särkkä, Philipp Hennig
Date: 2018-10-08 00:00:00

We formulate probabilistic numerical approximations to solutions of ordinary
differential equations (ODEs) as problems in Gaussian process (GP) regression
with non-linear measurement functions. This is achieved by defining the
measurement sequence to consist of the observations of the difference between
the derivative of the GP and the vector field evaluated at the GP---which are
all identically zero at the solution of the ODE. When the GP has a state-space
representation, the problem can be reduced to a non-linear Bayesian filtering
problem and all widely-used approximations to the Bayesian filtering and
smoothing problems become applicable. Furthermore, all previous GP-based ODE
solvers that are formulated in terms of generating synthetic measurements of
the gradient field come out as specific approximations. Based on the non-linear
Bayesian filtering problem posed in this paper, we develop novel Gaussian
solvers for which we establish favourable stability properties. Additionally,
non-Gaussian approximations to the filtering problem are derived by the
particle filter approach. The resulting solvers are compared with other
probabilistic solvers in illustrative experiments.
A Unified Dynamic Approach to Sparse Model Selection
Authors: Chendi Huang, Yuan YAO
Date: 2018-10-08 00:00:00
Tasks: model, Model Selection, regression

Sparse model selection is ubiquitous from linear regression to graphical
models where regularization paths, as a family of estimators upon the
regularization parameter varying, are computed when the regularization
parameter is unknown or decided data-adaptively. Traditional computational
methods rely on solving a set of optimization problems where the regularization
parameters are fixed on a grid that might be inefficient. In this paper, we
introduce a simple iterative regularization path, which follows the dynamics of
a sparse Mirror Descent algorithm or a generalization of Linearized Bregman
Iterations with nonlinear loss. Its performance is competitive to
\texttt{glmnet} with a further bias reduction. A path consistency theory is
presented that under the Restricted Strong Convexity (RSC) and the
Irrepresentable Condition (IRR), the path will first evolve in a subspace with
no false positives and reach an estimator that is sign-consistent or of minimax
optimal $\ell_2$ error rate. Early stopping regularization is required to
prevent overfitting. Application examples are given in sparse logistic
regression and Ising models for NIPS coauthorship.
An easy-to-use empirical likelihood ABC method
Authors: Sanjay Chaudhuri, Subhro Ghosh, David J. Nott, Kim Cuc Pham
Date: 2018-10-03 00:00:00
Tasks: Bayesian Inference

Many scientifically well-motivated statistical models in natural, engineering
and environmental sciences are specified through a generative process, but in
some cases it may not be possible to write down a likelihood for these models
analytically. Approximate Bayesian computation (ABC) methods, which allow
Bayesian inference in these situations, are typically computationally
intensive. Recently, computationally attractive empirical likelihood based ABC
methods have been suggested in the literature. These methods heavily rely on
the availability of a set of suitable analytically tractable estimating
equations. We propose an easy-to-use empirical likelihood ABC method, where the
only inputs required are a choice of summary statistic, it's observed value,
and the ability to simulate summary statistics for any parameter value under
the model. It is shown that the posterior obtained using the proposed method is
consistent, and its performance is explored using various examples.
Generalized No Free Lunch Theorem for Adversarial Robustness
Authors: Elvis Dohmatob
Date: 2018-10-08 00:00:00
Tasks: Adversarial Robustness

This manuscript presents some new impossibility results on adversarial robustness in machine learning, a very important yet largely open problem. We show that if conditioned on a class label the data distribution satisfies the $W_2$ Talagrand transportation-cost inequality (for example, this condition is satisfied if the conditional distribution has density which is log-concave; is the uniform measure on a compact Riemannian manifold with positive Ricci curvature, any classifier can be adversarially fooled with high probability once the perturbations are slightly greater than the natural noise level in the problem. We call this result The Strong "No Free Lunch" Theorem as some recent results (Tsipras et al. 2018, Fawzi et al. 2018, etc.) on the subject can be immediately recovered as very particular cases. Our theoretical bounds are demonstrated on both simulated and real data (MNIST). We conclude the manuscript with some speculation on possible future research directions.
Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video
Authors: Samvit Jain, Joseph E. Gonzalez
Date: 2018-10-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Optical Flow Estimation, Semantic Segmentation

Models optimized for accuracy on single images are often prohibitively slow
to run on each frame in a video. Recent work exploits the use of optical flow
to warp image features forward from select keyframes, as a means to conserve
computation on video. This approach, however, achieves only limited speedup,
even when optimized, due to the accuracy degradation introduced by repeated
forward warping, and the inference cost of optical flow estimation. To address
these problems, we propose a new scheme that propagates features using the
block motion vectors (BMV) present in compressed video (e.g. H.264 codecs),
instead of optical flow, and bi-directionally warps and fuses features from
enclosing keyframes to capture scene context on each video frame. Our
technique, interpolation-BMV, enables us to accurately estimate the features of
intermediate frames, while keeping inference costs low. We evaluate our system
on the CamVid and Cityscapes datasets, comparing to both a strong single-frame
baseline and related work. We find that we are able to substantially accelerate
segmentation on video, achieving near real-time frame rates (20+ frames per
second) on large images (e.g. 960 x 720 pixels), while maintaining competitive
accuracy. This represents an improvement of almost 6x over the single-frame
baseline and 2.5x over the fastest prior work.