pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Associate Words and Images Using a Large-scale Graph
Authors: Heqing Ya, Haonan Sun, Jeffrey Helt, Tai Sing Lee
Date: 2017-05-22 00:00:00

We develop an approach for unsupervised learning of associations between
co-occurring perceptual events using a large graph. We applied this approach to
successfully solve the image captcha of China's railroad system. The approach
is based on the principle of suspicious coincidence. In this particular
problem, a user is presented with a deformed picture of a Chinese phrase and
eight low-resolution images. They must quickly select the relevant images in
order to purchase their train tickets. This problem presents several
challenges: (1) the teaching labels for both the Chinese phrases and the images
were not available for supervised learning, (2) no pre-trained deep
convolutional neural networks are available for recognizing these Chinese
phrases or the presented images, and (3) each captcha must be solved within a
few seconds. We collected 2.6 million captchas, with 2.6 million deformed
Chinese phrases and over 21 million images. From these data, we constructed an
association graph, composed of over 6 million vertices, and linked these
vertices based on co-occurrence information and feature similarity between
pairs of images. We then trained a deep convolutional neural network to learn a
projection of the Chinese phrases onto a 230-dimensional latent space. Using
label propagation, we computed the likelihood of each of the eight images
conditioned on the latent space projection of the deformed phrase for each
captcha. The resulting system solved captchas with 77% accuracy in 2 seconds on
average. Our work, in answering this practical challenge, illustrates the power
of this class of unsupervised association learning techniques, which may be
related to the brain's general strategy for associating language stimuli with
visual objects on the principle of suspicious coincidence.
An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges
Authors: Bikash Joshi, Franck Iutzeler, Massih-Reza Amini
Date: 2017-05-22 00:00:00
Tasks: Binary Classification, General Classification, Recommendation Systems

In many distributed learning problems, the heterogeneous loading of computing
machines may harm the overall performance of synchronous strategies. In this
paper, we propose an effective asynchronous distributed framework for the
minimization of a sum of smooth functions, where each machine performs
iterations in parallel on its local function and updates a shared parameter
asynchronously. In this way, all machines can continuously work even though
they do not have the latest version of the shared parameter. We prove the
convergence of the consistency of this general distributed asynchronous method
for gradient iterations then show its efficiency on the matrix factorization
problem for recommender systems and on binary classification.
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks
Authors: Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés, Jonatan Moreno, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura
Date: 2017-05-22 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, image-classification, Image Classification, Network Embedding, Transfer Learning

Transfer learning for feature extraction can be used to exploit deep
representations in contexts where there is very few training data, where there
are limited computational resources, or when tuning the hyper-parameters needed
for training is not an option. While previous contributions to feature
extraction propose embeddings based on a single layer of the network, in this
paper we propose a full-network embedding which successfully integrates
convolutional and fully connected features, coming from all layers of a deep
convolutional neural network. To do so, the embedding normalizes features in
the context of the problem, and discretizes their values to reduce noise and
regularize the embedding space. Significantly, this also reduces the
computational cost of processing the resultant representations. The proposed
method is shown to outperform single layer embeddings on several image
classification tasks, while also being more robust to the choice of the
pre-trained model used for obtaining the initial features. The performance gap
in classification accuracy between thoroughly tuned solutions and the
full-network embedding is also reduced, which makes of the proposed approach a
competitive solution for a large set of applications.
Data Filtering for Cluster Analysis by $\ell_0$-Norm Regularization
Authors: Andrea Cristofari
Date: 2016-07-29 00:00:00
Tasks: Clustering

A data filtering method for cluster analysis is proposed, based on minimizing
a least squares function with a weighted $\ell_0$-norm penalty. To overcome the
discontinuity of the objective function, smooth non-convex functions are
employed to approximate the $\ell_0$-norm. The convergence of the global
minimum points of the approximating problems towards global minimum points of
the original problem is stated. The proposed method also exploits a suitable
technique to choose the penalty parameter. Numerical results on synthetic and
real data sets are finally provided, showing how some existing clustering
methods can take advantages from the proposed filtering strategy.
UntrimmedNets for Weakly Supervised Action Recognition and Detection
Authors: Limin Wang, Yuanjun Xiong, Dahua Lin, Luc van Gool
Date: 2017-03-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Temporal Action Localization, Weakly Supervised Action Localization, Weakly-Supervised Action Recognition

Current action recognition methods heavily rely on trimmed videos for model
training. However, it is expensive and time-consuming to acquire a large-scale
trimmed video dataset. This paper presents a new weakly supervised
architecture, called UntrimmedNet, which is able to directly learn action
recognition models from untrimmed videos without the requirement of temporal
annotations of action instances. Our UntrimmedNet couples two important
components, the classification module and the selection module, to learn the
action models and reason about the temporal duration of action instances,
respectively. These two components are implemented with feed-forward networks,
and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit
the learned models for action recognition (WSR) and detection (WSD) on the
untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet
only employs weak supervision, our method achieves performance superior or
comparable to that of those strongly supervised approaches on these two
datasets.
Semantic Softmax Loss for Zero-Shot Learning
Authors: Zhong Ji, Yunxin Sun, Yulong Yu, Jichang Guo, Yanwei Pang
Date: 2017-05-22 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Retrieval, zero-shot-classification, Zero-Shot Learning

A typical pipeline for Zero-Shot Learning (ZSL) is to integrate the visual
features and the class semantic descriptors into a multimodal framework with a
linear or bilinear model. However, the visual features and the class semantic
descriptors locate in different structural spaces, a linear or bilinear model
can not capture the semantic interactions between different modalities well. In
this letter, we propose a nonlinear approach to impose ZSL as a multi-class
classification problem via a Semantic Softmax Loss by embedding the class
semantic descriptors into the softmax layer of multi-class classification
network. To narrow the structural differences between the visual features and
semantic descriptors, we further use an L2 normalization constraint to the
differences between the visual features and visual prototypes reconstructed
with the semantic descriptors. The results on three benchmark datasets, i.e.,
AwA, CUB and SUN demonstrate the proposed approach can boost the performances
steadily and achieve the state-of-the-art performance for both zero-shot
classification and zero-shot retrieval.
Phone-aware Neural Language Identification
Authors: Zhiyuan Tang, Dong Wang, Yixiang Chen, Ying Shi, Lantian Li
Date: 2017-05-09 00:00:00
Tasks: Language Identification

Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great success. We present a phone-aware neural LID architecture,
which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR
system. By utilizing the phonetic knowledge, the LID performance can be
significantly improved. Interestingly, even if the test language is not
involved in the ASR training, the phonetic knowledge still presents a large
contribution. Our experiments conducted on four languages within the Babel
corpus demonstrated that the phone-aware approach is highly effective.
Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model
Authors: Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar
Date: 2017-05-22 00:00:00
Tasks: Prognosis

We report the development and validation of a data-driven real-time risk
score that provides timely assessments for the clinical acuity of ward patients
based on their temporal lab tests and vital signs, which allows for timely
intensive care unit (ICU) admissions. Unlike the existing risk scoring
technologies, the proposed score is individualized; it uses the electronic
health record (EHR) data to cluster the patients based on their static
covariates into subcohorts of similar patients, and then learns a separate
temporal, non-stationary multi-task Gaussian Process (GP) model that captures
the physiology of every subcohort. Experiments conducted on data from a
heterogeneous cohort of 6,094 patients admitted to the Ronald Reagan UCLA
medical center show that our risk score significantly outperforms the
state-of-the-art risk scoring technologies, such as the Rothman index and MEWS,
in terms of timeliness, true positive rate (TPR), and positive predictive value
(PPV). In particular, the proposed score increases the AUC with 20% and 38% as
compared to Rothman index and MEWS respectively, and can predict ICU admissions
8 hours before clinicians at a PPV of 35% and a TPR of 50%. Moreover, we show
that the proposed risk score allows for better decisions on when to discharge
clinically stable patients from the ward, thereby improving the efficiency of
hospital resource utilization.
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
Authors: Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
Date: 2017-05-22 00:00:00
Tasks: Community Detection, Deep Learning, General Classification, image-classification, Image Classification, Matrix Completion, Node Classification

The rise of graph-structured data such as social networks, regulatory
networks, citation graphs, and functional brain networks, in combination with
resounding success of deep learning in various applications, has brought the
interest in generalizing deep learning models to non-Euclidean domains. In this
paper, we introduce a new spectral domain convolutional architecture for deep
learning on graphs. The core ingredient of our model is a new class of
parametric rational complex functions (Cayley polynomials) allowing to
efficiently compute spectral filters on graphs that specialize on frequency
bands of interest. Our model generates rich spectral filters that are localized
in space, scales linearly with the size of the input data for
sparsely-connected graphs, and can handle different constructions of Laplacian
operators. Extensive experimental results show the superior performance of our
approach, in comparison to other spectral domain convolutional architectures,
on spectral image classification, community detection, vertex classification
and matrix completion tasks.
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
Authors: El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer
Date: 2017-04-10 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning, agents learn by performing actions and observing
their outcomes. Sometimes, it is desirable for a human operator to
\textit{interrupt} an agent in order to prevent dangerous situations from
happening. Yet, as part of their learning process, agents may link these
interruptions, that impact their reward, to specific states and deliberately
avoid them. The situation is particularly challenging in a multi-agent context
because agents might not only learn from their own past interruptions, but also
from those of other agents. Orseau and Armstrong defined \emph{safe
interruptibility} for one learner, but their work does not naturally extend to
multi-agent systems. This paper introduces \textit{dynamic safe
interruptibility}, an alternative definition more suited to decentralized
learning problems, and studies this notion in two learning frameworks:
\textit{joint action learners} and \textit{independent learners}. We give
realistic sufficient conditions on the learning algorithm to enable dynamic
safe interruptibility in the case of joint action learners, yet show that these
conditions are not sufficient for independent learners. We show however that if
agents can detect interruptions, it is possible to prune the observations to
ensure dynamic safe interruptibility even for independent learners.
Approximate Inference with Amortised MCMC
Authors: Yingzhen Li, Richard E. Turner, Qiang Liu
Date: 2017-02-27 00:00:00

We propose a novel approximate inference algorithm that approximates a target
distribution by amortising the dynamics of a user-selected MCMC sampler. The
idea is to initialise MCMC using samples from an approximation network, apply
the MCMC operator to improve these samples, and finally use the samples to
update the approximation network thereby improving its quality. This provides a
new generic framework for approximate inference, allowing us to deploy highly
complex, or implicitly defined approximation families with intractable
densities, including approximations produced by warping a source of randomness
through a deep neural network. Experiments consider image modelling with deep
generative models as a challenging test for the method. Deep models trained
using amortised MCMC are shown to generate realistic looking samples as well as
producing diverse imputations for images with regions of missing pixels.
ReFACTor: Practical Low-Rank Matrix Estimation Under Column-Sparsity
Authors: Matan Gavish, Regev Schweiger, Elior Rahmani, Eran Halperin
Date: 2017-05-22 00:00:00

Various problems in data analysis and statistical genetics call for recovery
of a column-sparse, low-rank matrix from noisy observations. We propose
ReFACTor, a simple variation of the classical Truncated Singular Value
Decomposition (TSVD) algorithm. In contrast to previous sparse principal
component analysis (PCA) algorithms, our algorithm can provably reveal a
low-rank signal matrix better, and often significantly better, than the widely
used TSVD, making it the algorithm of choice whenever column-sparsity is
suspected. Empirically, we observe that ReFACTor consistently outperforms TSVD
even when the underlying signal is not sparse, suggesting that it is generally
safe to use ReFACTor instead of TSVD and PCA. The algorithm is extremely simple
to implement and its running time is dominated by the runtime of PCA, making it
as practical as standard principal component analysis.
From optimal transport to generative modeling: the VEGAN cookbook
Authors: Olivier Bousquet, Sylvain Gelly, Ilya Tolstikhin, Carl-Johann Simon-Gabriel, Bernhard Schoelkopf
Date: 2017-05-22 00:00:00

We study unsupervised generative modeling in terms of the optimal transport
(OT) problem between true (but unknown) data distribution $P_X$ and the latent
variable model distribution $P_G$. We show that the OT problem can be
equivalently written in terms of probabilistic encoders, which are constrained
to match the posterior and prior distributions over the latent space. When
relaxed, this constrained optimization problem leads to a penalized optimal
transport (POT) objective, which can be efficiently minimized using stochastic
gradient descent by sampling from $P_X$ and $P_G$. We show that POT for the
2-Wasserstein distance coincides with the objective heuristically employed in
adversarial auto-encoders (AAE) (Makhzani et al., 2016), which provides the
first theoretical justification for AAEs known to the authors. We also compare
POT to other popular techniques like variational auto-encoders (VAE) (Kingma
and Welling, 2014). Our theoretical results include (a) a better understanding
of the commonly observed blurriness of images generated by VAEs, and (b)
establishing duality between Wasserstein GAN (Arjovsky and Bottou, 2017) and
POT for the 1-Wasserstein distance.
Dynamics Based 3D Skeletal Hand Tracking
Authors: Stan Melax, Leonid Keselman, Sterling Orsten
Date: 2017-05-22 00:00:00
Tasks: 3D Object Tracking, CPU, Object Tracking

Tracking the full skeletal pose of the hands and fingers is a challenging
problem that has a plethora of applications for user interaction. Existing
techniques either require wearable hardware, add restrictions to user pose, or
require significant computation resources. This research explores a new
approach to tracking hands, or any articulated model, by using an augmented
rigid body simulation. This allows us to phrase 3D object tracking as a linear
complementarity problem with a well-defined solution. Based on a depth sensor's
samples, the system generates constraints that limit motion orthogonal to the
rigid body model's surface. These constraints, along with prior motion,
collision/contact constraints, and joint mechanics, are resolved with a
projected Gauss-Seidel solver. Due to camera noise properties and attachment
errors, the numerous surface constraints are impulse capped to avoid
overpowering mechanical constraints. To improve tracking accuracy, multiple
simulations are spawned at each frame and fed a variety of heuristics,
constraints and poses. A 3D error metric selects the best-fit simulation,
helping the system handle challenging hand motions. Such an approach enables
real-time, robust, and accurate 3D skeletal tracking of a user's hand on a
variety of depth cameras, while only utilizing a single x86 CPU core for
processing.
A Cross-Entropy-based Method to Perform Information-based Feature Selection
Authors: Pietro Cassara, Alessandro Rozza, Mirco Nanni
Date: 2016-07-25 00:00:00
Tasks: feature selection, General Classification

From a machine learning point of view, identifying a subset of relevant
features from a real data set can be useful to improve the results achieved by
classification methods and to reduce their time and space complexity. To
achieve this goal, feature selection methods are usually employed. These
approaches assume that the data contains redundant or irrelevant attributes
that can be eliminated. In this work, we propose a novel algorithm to manage
the optimization problem that is at the foundation of the Mutual Information
feature selection methods. Furthermore, our novel approach is able to estimate
automatically the number of dimensions to retain. The quality of our method is
confirmed by the promising results achieved on standard real data sets.
AIXIjs: A Software Demo for General Reinforcement Learning
Authors: John Aslanides
Date: 2017-05-22 00:00:00
Tasks: General Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Reinforcement learning is a general and powerful framework with which to
study and implement artificial intelligence. Recent advances in deep learning
have enabled RL algorithms to achieve impressive performance in restricted
domains such as playing Atari video games (Mnih et al., 2015) and, recently,
the board game Go (Silver et al., 2016). However, we are still far from
constructing a generally intelligent agent. Many of the obstacles and open
questions are conceptual: What does it mean to be intelligent? How does one
explore and learn optimally in general, unknown environments? What, in fact,
does it mean to be optimal in the general sense? The universal Bayesian agent
AIXI (Hutter, 2005) is a model of a maximally intelligent agent, and plays a
central role in the sub-field of general reinforcement learning (GRL).
Recently, AIXI has been shown to be flawed in important ways; it doesn't
explore enough to be asymptotically optimal (Orseau, 2010), and it can perform
poorly with certain priors (Leike and Hutter, 2015). Several variants of AIXI
have been proposed to attempt to address these shortfalls: among them are
entropy-seeking agents (Orseau, 2011), knowledge-seeking agents (Orseau et al.,
2013), Bayes with bursts of exploration (Lattimore, 2013), MDL agents (Leike,
2016a), Thompson sampling (Leike et al., 2016), and optimism (Sunehag and
Hutter, 2015). We present AIXIjs, a JavaScript implementation of these GRL
agents. This implementation is accompanied by a framework for running
experiments against various environments, similar to OpenAI Gym (Brockman et
al., 2016), and a suite of interactive demos that explore different properties
of the agents, similar to REINFORCEjs (Karpathy, 2015). We use AIXIjs to
present numerous experiments illustrating fundamental properties of, and
differences between, these agents.
Finding Statistically Significant Interactions between Continuous Features
Authors: Mahito Sugiyama, Karsten Borgwardt
Date: 2017-02-28 00:00:00
Tasks: Computational Efficiency

The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing. While recent progress has been made regarding this challenge for binary features, we here present the first solution for continuous features. We propose an algorithm which overcomes the combinatorial explosion of the search space of higher-order interactions by deriving a lower bound on the p-value for each interaction, which enables us to massively prune interactions that can never reach significance and to thereby gain more statistical power. In our experiments, our approach efficiently detects all significant interactions in a variety of synthetic and real-world datasets.
View-Invariant Recognition of Action Style Self-Dissimilarity
Authors: Yuping Shen, Hassan Foroosh
Date: 2017-05-22 00:00:00
Tasks: Classification, General Classification

Self-similarity was recently introduced as a measure of inter-class
congruence for classification of actions. Herein, we investigate the dual
problem of intra-class dissimilarity for classification of action styles. We
introduce self-dissimilarity matrices that discriminate between same actions
performed by different subjects regardless of viewing direction and camera
parameters. We investigate two frameworks using these invariant style
dissimilarity measures based on Principal Component Analysis (PCA) and Fisher
Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset
indicate remarkably good discriminant characteristics for the proposed
invariant measures for gender recognition from video data.