pwc-archive/papers-with-abstracts huggingface dataset:
Fully Convolutional Attention Networks for Fine-Grained Recognition
Authors: Xiao Liu, Tian Xia, Jiang Wang, Yi Yang, Feng Zhou, Yuanqing Lin
Date: 2016-03-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Fine-grained recognition is challenging due to its subtle local inter-class
differences versus large intra-class variations such as poses. A key to address
this problem is to localize discriminative parts to extract pose-invariant
features. However, ground-truth part annotations can be expensive to acquire.
Moreover, it is hard to define parts for many fine-grained classes. This work
introduces Fully Convolutional Attention Networks (FCANs), a reinforcement
learning framework to optimally glimpse local discriminative regions adaptive
to different fine-grained domains. Compared to previous methods, our approach
enjoys three advantages: 1) the weakly-supervised reinforcement learning
procedure requires no expensive part annotations; 2) the fully-convolutional
architecture speeds up both training and testing; 3) the greedy reward strategy
accelerates the convergence of the learning. We demonstrate the effectiveness
of our method with extensive experiments on four challenging fine-grained
benchmark datasets, including CUB-200-2011, Stanford Dogs, Stanford Cars and
Food-101.
A Survey of Available Corpora for Building Data-Driven Dialogue Systems
Authors: Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau
Date: 2015-12-17 00:00:00
Tasks: Transfer Learning

During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheless, several recent results suggest that data-driven approaches are
feasible and quite promising. To facilitate research in this area, we have
carried out a wide survey of publicly available datasets suitable for
data-driven learning of dialogue systems. We discuss important characteristics
of these datasets, how they can be used to learn diverse dialogue strategies,
and their other potential uses. We also examine methods for transfer learning
between datasets and the use of external knowledge. Finally, we discuss
appropriate choice of evaluation metrics for the learning objective.
The Use of Autoencoders for Discovering Patient Phenotypes
Authors: Harini Suresh, Peter Szolovits, Marzyeh Ghassemi
Date: 2017-03-20 00:00:00

We use autoencoders to create low-dimensional embeddings of underlying
patient phenotypes that we hypothesize are a governing factor in determining
how different patients will react to different interventions. We compare the
performance of autoencoders that take fixed length sequences of concatenated
timesteps as input with a recurrent sequence-to-sequence autoencoder. We
evaluate our methods on around 35,500 patients from the latest MIMIC III
dataset from Beth Israel Deaconess Hospital.
Metalearning for Feature Selection
Authors: Ben Goertzel, Nil Geisweiller, Chris Poulin
Date: 2017-03-20 00:00:00
Tasks: Classification, feature selection, General Classification, text-classification, Text Classification

A general formulation of optimization problems in which various candidate
solutions may use different feature-sets is presented, encompassing supervised
classification, automated program learning and other cases. A novel
characterization of the concept of a "good quality feature" for such an
optimization problem is provided; and a proposal regarding the integration of
quality based feature selection into metalearning is suggested, wherein the
quality of a feature for a problem is estimated using knowledge about related
features in the context of related problems. Results are presented regarding
extensive testing of this "feature metalearning" approach on supervised text
classification problems; it is demonstrated that, in this context, feature
metalearning can provide significant and sometimes dramatic speedup over
standard feature selection heuristics.
Learning from the Hindsight Plan -- Episodic MPC Improvement
Authors: Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter Abbeel
Date: 2016-09-28 00:00:00
Tasks: Contact-rich Manipulation, Model Predictive Control

Model predictive control (MPC) is a popular control method that has proved
effective for robotics, among other fields. MPC performs re-planning at every
time step. Re-planning is done with a limited horizon per computational and
real-time constraints and often also for robustness to potential model errors.
However, the limited horizon leads to suboptimal performance. In this work, we
consider the iterative learning setting, where the same task can be repeated
several times, and propose a policy improvement scheme for MPC. The main idea
is that between executions we can, offline, run MPC with a longer horizon,
resulting in a hindsight plan. To bring the next real-world execution closer to
the hindsight plan, our approach learns to re-shape the original cost function
with the goal of satisfying the following property: short horizon planning (as
realistic during real executions) with respect to the shaped cost should result
in mimicking the hindsight plan. This effectively consolidates long-term
reasoning into the short-horizon planning. We empirically evaluate our approach
in contact-rich manipulation tasks both in simulated and real environments,
such as peg insertion by a real PR2 robot.
Value Iteration Networks
Authors: Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel
Date: 2016-02-09 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce the value iteration network (VIN): a fully differentiable neural
network with a `planning module' embedded within. VINs can learn to plan, and
are suitable for predicting outcomes that involve planning-based reasoning,
such as policies for reinforcement learning. Key to our approach is a novel
differentiable approximation of the value-iteration algorithm, which can be
represented as a convolutional neural network, and trained end-to-end using
standard backpropagation. We evaluate VIN based policies on discrete and
continuous path-planning domains, and on a natural-language based search task.
We show that by learning an explicit planning computation, VIN policies
generalize better to new, unseen domains.
Learning to Generate Samples from Noise through Infusion Training
Authors: Florian Bordes, Sina Honari, Pascal Vincent
Date: 2017-03-20 00:00:00
Tasks: Denoising

In this work, we investigate a novel training procedure to learn a generative
model as the transition operator of a Markov chain, such that, when applied
repeatedly on an unstructured random noise sample, it will denoise it into a
sample that matches the target distribution from the training set. The novel
training procedure to learn this progressive denoising operation involves
sampling from a slightly different chain than the model chain used for
generation in the absence of a denoising target. In the training chain we
infuse information from the training target example that we would like the
chains to reach with a high probability. The thus learned transition operator
is able to produce quality and varied samples in a small number of steps.
Experiments show competitive results compared to the samples generated with a
basic Generative Adversarial Net
Bayesian Adaptive Data Analysis Guarantees from Subgaussianity
Authors: Sam Elder
Date: 2016-10-31 00:00:00

The new field of adaptive data analysis seeks to provide algorithms and
provable guarantees for models of machine learning that allow researchers to
reuse their data, which normally falls outside of the usual statistical
paradigm of static data analysis. In 2014, Dwork, Feldman, Hardt, Pitassi,
Reingold and Roth introduced one potential model and proposed several solutions
based on differential privacy. In previous work in 2016, we described a problem
with this model and instead proposed a Bayesian variant, but also found that
the analogous Bayesian methods cannot achieve the same statistical guarantees
as in the static case.
  In this paper, we prove the first positive results for the Bayesian model,
showing that with a Dirichlet prior, the posterior mean algorithm indeed
matches the statistical guarantees of the static case. The main ingredient is a
new theorem showing that the $\mathrm{Beta}(\alpha,\beta)$ distribution is
subgaussian with variance proxy $O(1/(\alpha+\beta+1))$, a concentration result
also of independent interest. We provide two proofs of this result: a
probabilistic proof utilizing a simple condition for the raw moments of a
positive random variable and a learning-theoretic proof based on considering
the beta distribution as a posterior, both of which have implications to other
related problems.
Challenges in Bayesian Adaptive Data Analysis
Authors: Sam Elder
Date: 2016-04-08 00:00:00
Tasks: Holdout Set

Traditional statistical analysis requires that the analysis process and data
are independent. By contrast, the new field of adaptive data analysis hopes to
understand and provide algorithms and accuracy guarantees for research as it is
commonly performed in practice, as an iterative process of interacting
repeatedly with the same data set, such as repeated tests against a holdout
set. Previous work has defined a model with a rather strong lower bound on
sample complexity in terms of the number of queries, $n\sim\sqrt q$, arguing
that adaptive data analysis is much harder than static data analysis, where
$n\sim\log q$ is possible. Instead, we argue that those strong lower bounds
point to a limitation of the previous model in that it must consider wildly
asymmetric scenarios which do not hold in typical applications.
  To better understand other difficulties of adaptivity, we propose a new
Bayesian version of the problem that mandates symmetry. Since the other lower
bound techniques are ruled out, we can more effectively see difficulties that
might otherwise be overshadowed. As a first contribution to this model, we
produce a new problem using error-correcting codes on which a large family of
methods, including all previously proposed algorithms, require roughly
$n\sim\sqrt[4]q$. These early results illustrate new difficulties in adaptive
data analysis regarding slightly correlated queries on problems with
concentrated uncertainty.
Online Robust Principal Component Analysis with Change Point Detection
Authors: Wei Xiao, Xiaolin Huang, Jorge Silva, Saba Emrani, Arin Chaudhuri
Date: 2017-02-19 00:00:00
Tasks: Change Point Detection, Two-sample testing

Robust PCA methods are typically batch algorithms which requires loading all
observations into memory before processing. This makes them inefficient to
process big data. In this paper, we develop an efficient online robust
principal component methods, namely online moving window robust principal
component analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can
successfully track not only slowly changing subspace but also abruptly changed
subspace. By embedding hypothesis testing into the algorithm, OMWRPCA can
detect change points of the underlying subspaces. Extensive simulation studies
demonstrate the superior performance of OMWRPCA compared with other
state-of-art approaches. We also apply the algorithm for real-time background
subtraction of surveillance video.
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
Authors: Or Litany, Tal Remez, Alex Bronstein
Date: 2016-12-15 00:00:00
Tasks: Autonomous Driving, Denoising, Gesture Recognition

With the development of range sensors such as LIDAR and time-of-flight
cameras, 3D point cloud scans have become ubiquitous in computer vision
applications, the most prominent ones being gesture recognition and autonomous
driving. Parsimony-based algorithms have shown great success on images and
videos where data points are sampled on a regular Cartesian grid. We propose an
adaptation of these techniques to irregularly sampled signals by using
continuous dictionaries. We present an example application in the form of point
cloud denoising.
Distributed Constraint Problems for Utilitarian Agents with Privacy Concerns, Recast as POMDPs
Authors: Julien Savaux, Julien Vion, Sylvain Piechowiak, René Mandiau, Toshihiro Matsui, Katsutoshi Hirayama, Makoto Yokoo, Shakre Elmane, Marius Silaghi
Date: 2017-03-20 00:00:00

Privacy has traditionally been a major motivation for distributed problem
solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as
Distributed Constraint Optimization Problem (DCOP) are fundamental models used
to solve various families of distributed problems. Even though several
approaches have been proposed to quantify and preserve privacy in such
problems, none of them is exempt from limitations. Here we approach the problem
by assuming that computation is performed among utilitarian agents. We
introduce a utilitarian approach where the utility of each state is estimated
as the difference between the reward for reaching an agreement on assignments
of shared variables and the cost of privacy loss. We investigate extensions to
solvers where agents integrate the utility function to guide their search and
decide which action to perform, defining thereby their policy. We show that
these extended solvers succeed in significantly reducing privacy loss without
significant degradation of the solution quality.
Black-Box Optimization in Machine Learning with Trust Region Based Derivative Free Algorithm
Authors: Hiva Ghanbari, Katya Scheinberg
Date: 2017-03-20 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Text-to-Image Generation

In this work, we utilize a Trust Region based Derivative Free Optimization
(DFO-TR) method to directly maximize the Area Under Receiver Operating
Characteristic Curve (AUC), which is a nonsmooth, noisy function. We show that
AUC is a smooth function, in expectation, if the distributions of the positive
and negative data points obey a jointly normal distribution. The practical
performance of this algorithm is compared to three prominent Bayesian
optimization methods and random search. The presented numerical results show
that DFO-TR surpasses Bayesian optimization and random search on various
black-box optimization problem, such as maximizing AUC and hyperparameter
tuning.
Voronoi-based compact image descriptors: Efficient Region-of-Interest retrieval with VLAD and deep-learning-based descriptors
Authors: Aaron Chadha, Yiannis Andreopoulos
Date: 2016-11-27 00:00:00
Tasks: Image Retrieval, Retrieval

We investigate the problem of image retrieval based on visual queries when
the latter comprise arbitrary regions-of-interest (ROI) rather than entire
images. Our proposal is a compact image descriptor that combines the
state-of-the-art in content-based descriptor extraction with a multi-level,
Voronoi-based spatial partitioning of each dataset image. The proposed
multi-level Voronoi-based encoding uses a spatial hierarchical K-means over
interest-point locations, and computes a content-based descriptor over each
cell. In order to reduce the matching complexity with minimal or no sacrifice
in retrieval performance: (i) we utilize the tree structure of the spatial
hierarchical K-means to perform a top-to-bottom pruning for local similarity
maxima; (ii) we propose a new image similarity score that combines relevant
information from all partition levels into a single measure for similarity;
(iii) we combine our proposal with a novel and efficient approach for optimal
bit allocation within quantized descriptor representations. By deriving both a
Voronoi-based VLAD descriptor (termed as Fast-VVLAD) and a Voronoi-based deep
convolutional neural network (CNN) descriptor (termed as Fast-VDCNN), we
demonstrate that our Voronoi-based framework is agnostic to the descriptor
basis, and can easily be slotted into existing frameworks. Via a range of ROI
queries in two standard datasets, it is shown that the Voronoi-based
descriptors achieve comparable or higher mean Average Precision against
conventional grid-based spatial search, while offering more than two-fold
reduction in complexity. Finally, beyond ROI queries, we show that Voronoi
partitioning improves the geometric invariance of compact CNN descriptors,
thereby resulting in competitive performance to the current state-of-the-art on
whole image retrieval.
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
Authors: Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel
Date: 2017-03-20 00:00:00
Tasks: Object Localization

Bridging the 'reality gap' that separates simulated robotics from experiments
on hardware could accelerate robotic research through improved data
availability. This paper explores domain randomization, a simple technique for
training models on simulated images that transfer to real images by randomizing
rendering in the simulator. With enough variability in the simulator, the real
world may appear to the model as just another variation. We focus on the task
of object localization, which is a stepping stone to general robotic
manipulation skills. We find that it is possible to train a real-world object
detector that is accurate to $1.5$cm and robust to distractors and partial
occlusions using only data from a simulator with non-realistic random textures.
To demonstrate the capabilities of our detectors, we show they can be used to
perform grasping in a cluttered environment. To our knowledge, this is the
first successful transfer of a deep neural network trained only on simulated
RGB images (without pre-training on real images) to the real world for the
purpose of robotic control.
A Comparison of deep learning methods for environmental sound
Authors: Juncheng Li, Wei Dai, Florian Metze, Shuhui Qu, Samarjit Das
Date: 2017-03-20 00:00:00
Tasks: Avg, Deep Learning

Environmental sound detection is a challenging application of machine
learning because of the noisy nature of the signal, and the small amount of
(labeled) data that is typically available. This work thus presents a
comparison of several state-of-the-art Deep Learning models on the IEEE
challenge on Detection and Classification of Acoustic Scenes and Events (DCASE)
2016 challenge task and data, classifying sounds into one of fifteen common
indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest
path, library, train, etc. In total, 13 hours of stereo audio recordings are
available, making this one of the largest datasets available. We perform
experiments on six sets of features, including standard Mel-frequency cepstral
coefficients (MFCC), Binaural MFCC, log Mel-spectrum and two different large-
scale temporal pooling features extracted using OpenSMILE. On these features,
we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN),
Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and
i-vector. Using the late-fusion approach, we improve the performance of the
baseline 72.5% by 15.6% in 4-fold Cross Validation (CV) avg. accuracy and 11%
in test accuracy, which matches the best result of the DCASE 2016 challenge.
With large feature sets, deep neural network models out- perform traditional
methods and achieve the best performance among all the studied methods.
Consistent with other work, the best performing single model is the
non-temporal DNN model, which we take as evidence that sounds in the DCASE
challenge do not exhibit strong temporal dynamics.
Towards a Neural Statistician
Authors: Harrison Edwards, Amos Storkey
Date: 2016-06-07 00:00:00
Tasks: Clustering, Few-Shot Image Classification

An efficient learner is one who reuses what they already know to tackle a new
problem. For a machine learner, this means understanding the similarities
amongst datasets. In order to do this, one must take seriously the idea of
working with datasets, rather than datapoints, as the key objects to model.
Towards this goal, we demonstrate an extension of a variational autoencoder
that can learn a method for computing representations, or statistics, of
datasets in an unsupervised fashion. The network is trained to produce
statistics that encapsulate a generative model for each dataset. Hence the
network enables efficient learning from new datasets for both unsupervised and
supervised tasks. We show that we are able to learn statistics that can be used
for: clustering datasets, transferring generative models to new datasets,
selecting representative samples of datasets and classifying previously unseen
classes. We refer to our model as a neural statistician, and by this we mean a
neural network that can learn to compute summary statistics of datasets without
supervision.
Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration
Authors: Abhishek Gupta, Clemens Eppner, Sergey Levine, Pieter Abbeel
Date: 2016-03-21 00:00:00
Tasks: Reinforcement Learning

Dexterous multi-fingered hands can accomplish fine manipulation behaviors
that are infeasible with simple robotic grippers. However, sophisticated
multi-fingered hands are often expensive and fragile. Low-cost soft hands offer
an appealing alternative to more conventional devices, but present considerable
challenges in sensing and actuation, making them difficult to apply to more
complex manipulation tasks. In this paper, we describe an approach to learning
from demonstration that can be used to train soft robotic hands to perform
dexterous manipulation tasks. Our method uses object-centric demonstrations,
where a human demonstrates the desired motion of manipulated objects with their
own hands, and the robot autonomously learns to imitate these demonstrations
using reinforcement learning. We propose a novel algorithm that allows us to
blend and select a subset of the most feasible demonstrations to learn to
imitate on the hardware, which we use with an extension of the guided policy
search framework to use multiple demonstrations to learn generalizable neural
network policies. We demonstrate our approach on the RBO Hand 2, with learned
motor skills for turning a valve, manipulating an abacus, and grasping.