pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting knowledge transfer for training object class detectors
Authors: Jasper Uijlings, Stefan Popov, Vittorio Ferrari
Date: 2017-08-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Object, Transfer Learning

We propose to revisit knowledge transfer for training object detectors on
target classes from weakly supervised training images, helped by a set of
source classes with bounding-box annotations. We present a unified knowledge
transfer framework based on training a single neural network multi-class object
detector over all source classes, organized in a semantic hierarchy. This
generates proposals with scores at multiple levels in the hierarchy, which we
use to explore knowledge transfer over a broad range of generality, ranging
from class-specific (bicycle to motorbike) to class-generic (objectness to any
class). Experiments on the 200 object classes in the ILSVRC 2013 detection
dataset show that our technique: (1) leads to much better performance on the
target classes (70.3% CorLoc, 36.9% mAP) than a weakly supervised baseline
which uses manually engineered objectness [11] (50.5% CorLoc, 25.4% mAP). (2)
delivers target object detectors reaching 80% of the mAP of their fully
supervised counterparts. (3) outperforms the best reported transfer learning
results on this dataset (+41% CorLoc and +3% mAP over [18, 46], +16.2% mAP over
[32]). Moreover, we also carry out several across-dataset knowledge transfer
experiments [27, 24, 35] and find that (4) our technique outperforms the weakly
supervised baseline in all dataset pairs by 1.5x-1.9x, establishing its general
applicability.
Network Traffic Anomaly Detection Using Recurrent Neural Networks
Authors: Benjamin J. Radford, Leonardo M. Apolonio, Antonio J. Trias, Jim A. Simpson
Date: 2018-03-28 00:00:00
Tasks: Anomaly Detection, Language Modeling, Language Modelling

We show that a recurrent neural network is able to learn a model to represent
sequences of communications between computers on a network and can be used to
identify outlier network traffic. Defending computer networks is a challenging
problem and is typically addressed by manually identifying known malicious
actor behavior and then specifying rules to recognize such behavior in network
communications. However, these rule-based approaches often generalize poorly
and identify only those patterns that are already known to researchers. An
alternative approach that does not rely on known malicious behavior patterns
can potentially also detect previously unseen patterns. We tokenize and
compress netflow into sequences of "words" that form "sentences" representative
of a conversation between computers. These sentences are then used to generate
a model that learns the semantic and syntactic grammar of the newly generated
language. We use Long-Short-Term Memory (LSTM) cell Recurrent Neural Networks
(RNN) to capture the complex relationships and nuances of this language. The
language model is then used predict the communications between two IPs and the
prediction error is used as a measurement of how typical or atyptical the
observed communication are. By learning a model that is specific to each
network, yet generalized to typical computer-to-computer traffic within and
outside the network, a language model is able to identify sequences of network
activity that are outliers with respect to the model. We demonstrate positive
unsupervised attack identification performance (AUC 0.84) on the ISCX IDS
dataset which contains seven days of network activity with normal traffic and
four distinct attack patterns.
A Distributed Extension of the Turing Machine
Authors: Luis A. Pineda
Date: 2018-03-28 00:00:00
Tasks: Retrieval

The Turing Machine has two implicit properties that depend on its underlying
notion of computing: the format is fully determinate and computations are
information preserving. Distributed representations lack these properties and
cannot be fully captured by Turing's standard model. To address this limitation
a distributed extension of the Turing Machine is introduced in this paper. In
the extended machine, functions and abstractions are expressed extensionally
and computations are entropic. The machine is applied to the definition of an
associative memory, with its corresponding memory register, recognition and
retrieval operations. The memory is tested with an experiment for storing and
recognizing hand written digits with satisfactory results. The experiment can
be seen as a proof of concept that information can be stored and processed
effectively in a highly distributed fashion using a symbolic but not fully
determinate format. The new machine augments the symbolic mode of computing
with consequences on the way Church Thesis is understood. The paper is
concluded with a discussion of some implications of the extended machine for
Artificial Intelligence and Cognition.
Active Metric Learning for Supervised Classification
Authors: Krishnan Kumaran, Dimitri Papageorgiou, Yutong Chang, Minhan Li, Martin Takáč
Date: 2018-03-28 00:00:00
Tasks: Active Learning, Classification, Clustering, General Classification, Metric Learning

Clustering and classification critically rely on distance metrics that
provide meaningful comparisons between data points. We present mixed-integer
optimization approaches to find optimal distance metrics that generalize the
Mahalanobis metric extensively studied in the literature. Additionally, we
generalize and improve upon leading methods by removing reliance on
pre-designated "target neighbors," "triplets," and "similarity pairs." Another
salient feature of our method is its ability to enable active learning by
recommending precise regions to sample after an optimal metric is computed to
improve classification performance. This targeted acquisition can significantly
reduce computational burden by ensuring training data completeness,
representativeness, and economy. We demonstrate classification and
computational performance of the algorithms through several simple and
intuitive examples, followed by results on real image and medical datasets.
Unreasonable Effectivness of Deep Learning
Authors: Finn Macleod
Date: 2018-03-28 00:00:00
Tasks: Deep Learning

We show how well known rules of back propagation arise from a weighted
combination of finite automata. By redefining a finite automata as a predictor
we combine the set of all $k$-state finite automata using a weighted majority
algorithm. This aggregated prediction algorithm can be simplified using
symmetry, and we prove the equivalence of an algorithm that does this. We
demonstrate that this algorithm is equivalent to a form of a back propagation
acting in a completely connected $k$-node neural network. Thus the use of the
weighted majority algorithm allows a bound on the general performance of deep
learning approaches to prediction via known results from online statistics. The
presented framework opens more detailed questions about network topology; it is
a bridge to the well studied techniques of semigroup theory and applying these
techniques to answer what specific network topologies are capable of
predicting. This informs both the design of artificial networks and the
exploration of neuroscience models.
On Learning Graphs with Edge-Detecting Queries
Authors: Hasan Abasi, Nader H. Bshouty
Date: 2018-03-28 00:00:00

We consider the problem of learning a general graph $G=(V,E)$ using
edge-detecting queries, where the number of vertices $|V|=n$ is given to the
learner. The information theoretic lower bound gives $m\log n$ for the number
of queries, where $m=|E|$ is the number of edges. In case the number of edges
$m$ is also given to the learner, Angluin-Chen's Las Vegas algorithm
\cite{AC08} runs in $4$ rounds and detects the edges in $O(m\log n)$ queries.
In the other harder case where the number of edges $m$ is unknown, their
algorithm runs in $5$ rounds and asks $O(m\log n+\sqrt{m}\log^2 n)$ queries.
There have been two open problems: \emph{(i)} can the number of queries be
reduced to $O(m\log n)$ in the second case, and, \emph{(ii)} can the number of
rounds be reduced without substantially increasing the number of queries (in
both cases). For the first open problem (when $m$ is unknown) we give two
algorithms. The first is an $O(1)$-round Las Vegas algorithm that asks $m\log
n+\sqrt{m}(\log^{[k]}n)\log n$ queries for any constant $k$ where
$\log^{[k]}n=\log \stackrel{k}{\cdots} \log n$. The second is an
$O(\log^*n)$-round Las Vegas algorithm that asks $O(m\log n)$ queries. This
solves the first open problem for any practical $n$, for example,
$n<2^{65536}$. We also show that no deterministic algorithm can solve this
problem in a constant number of rounds. To solve the second problem we study
the case when $m$ is known. We first show that any non-adaptive Monte Carlo
algorithm (one-round) must ask at least $\Omega(m^2\log n)$ queries, and any
two-round Las Vegas algorithm must ask at least $m^{4/3-o(1)}\log n$ queries on
average. We then give two two-round Monte Carlo algorithms, the first asks
$O(m^{4/3}\log n)$ queries for any $n$ and $m$, and the second asks $O(m\log
n)$ queries when $n>2^m$. Finally, we give a $3$-round Monte Carlo algorithm
that asks $O(m\log n)$ queries for any $n$ and $m$.
Weight-based Fish School Search algorithm for Many-Objective Optimization
Authors: F. B. Lima Neto, I. M. C. Albuquerque, J. B. Monteiro Filho
Date: 2017-08-16 00:00:00
Tasks: Clustering

Optimization problems with more than one objective consist in a very
attractive topic for researchers due to its applicability in real-world
situations. Over the years, the research effort in the Computational
Intelligence field resulted in algorithms able to achieve good results by
solving problems with more than one conflicting objective. However, these
techniques do not exhibit the same performance as the number of objectives
increases and become greater than 3. This paper proposes an adaptation of the
metaheuristic Fish School Search to solve optimization problems with many
objectives. This adaptation is based on the division of the candidate solutions
in clusters that are specialized in solving a single-objective problem
generated by the decomposition of the original problem. For this, we used
concepts and ideas often employed by state-of-the-art algorithms, namely: (i)
reference points and lines in the objectives space; (ii) clustering process;
and (iii) the decomposition technique Penalty-based Boundary Intersection. The
proposed algorithm was compared with two state-of-the-art bio-inspired
algorithms. Moreover, a version of the proposed technique tailored to solve
multi-modal problems was also presented. The experiments executed have shown
that the performance obtained by both versions is competitive with
state-of-the-art results.
Meta-Learning a Dynamical Language Model
Authors: Thomas Wolf, Julien Chaumond, Clement Delangue
Date: 2018-03-28 00:00:00
Tasks: Language Modeling, Language Modelling, Meta-Learning, model

We consider the task of word-level language modeling and study the
possibility of combining hidden-states-based short-term representations with
medium-term representations encoded in dynamical weights of a language model.
Our work extends recent experiments on language models with dynamically
evolving weights by casting the language modeling problem into an online
learning-to-learn framework in which a meta-learner is trained by
gradient-descent to continuously update a language model weights.
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
Authors: Jon Barker, Shinji Watanabe, Emmanuel Vincent, Jan Trmal
Date: 2018-03-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Speech Enhancement, speech-recognition, Speech Recognition, Speech Separation

The CHiME challenge series aims to advance robust automatic speech
recognition (ASR) technology by promoting research at the interface of speech
and language processing, signal processing , and machine learning. This paper
introduces the 5th CHiME Challenge, which considers the task of distant
multi-microphone conversational ASR in real home environments. Speech material
was elicited using a dinner party scenario with efforts taken to capture data
that is representative of natural conversational speech and recorded by 6
Kinect microphone arrays and 4 binaural microphone pairs. The challenge
features a single-array track and a multiple-array track and, for each track,
distinct rankings will be produced for systems focusing on robustness with
respect to distant-microphone capture vs. systems attempting to address all
aspects of the task including conversational language modeling. We discuss the
rationale for the challenge and provide a detailed description of the data
collection procedure, the task, and the baseline systems for array
synchronization, speech enhancement, and conventional and end-to-end ASR.
Am I Done? Predicting Action Progress in Videos
Authors: Federico Becattini, Tiberio Uricchio, Lorenzo Seidenari, Lamberto Ballan, Alberto del Bimbo
Date: 2017-05-04 00:00:00
Tasks: Action Detection, Temporal Localization

In this paper we deal with the problem of predicting action progress in videos. We argue that this is an extremely important task since it can be valuable for a wide range of interaction applications. To this end we introduce a novel approach, named ProgressNet, capable of predicting when an action takes place in a video, where it is located within the frames, and how far it has progressed during its execution. To provide a general definition of action progress, we ground our work in the linguistics literature, borrowing terms and concepts to understand which actions can be the subject of progress estimation. As a result, we define a categorization of actions and their phases. Motivated by the recent success obtained from the interaction of Convolutional and Recurrent Neural Networks, our model is based on a combination of the Faster R-CNN framework, to make frame-wise predictions, and LSTM networks, to estimate action progress through time. After introducing two evaluation protocols for the task at hand, we demonstrate the capability of our model to effectively predict action progress on the UCF-101 and J-HMDB datasets.
FeaStNet: Feature-Steered Graph Convolutions for 3D Shape Analysis
Authors: Nitika Verma, Edmond Boyer, Jakob Verbeek
Date: 2017-06-16 00:00:00
Proceeding: CVPR 2018 6

Convolutional neural networks (CNNs) have massively impacted visual
recognition in 2D images, and are now ubiquitous in state-of-the-art
approaches. CNNs do not easily extend, however, to data that are not
represented by regular grids, such as 3D shape meshes or other graph-structured
data, to which traditional local convolution operators do not directly apply.
To address this problem, we propose a novel graph-convolution operator to
establish correspondences between filter weights and graph neighborhoods with
arbitrary connectivity. The key novelty of our approach is that these
correspondences are dynamically computed from features learned by the network,
rather than relying on predefined static coordinates over the graph as in
previous work. We obtain excellent experimental results that significantly
improve over previous state-of-the-art shape correspondence results. This shows
that our approach can learn effective shape representations from raw input
coordinates, without relying on shape descriptors.
Feed-forward Uncertainty Propagation in Belief and Neural Networks
Authors: Alexander Shekhovtsov, Boris Flach, Michal Busta
Date: 2018-03-28 00:00:00
Tasks: All

We propose a feed-forward inference method applicable to belief and neural
networks. In a belief network, the method estimates an approximate factorized
posterior of all hidden units given the input. In neural networks the method
propagates uncertainty of the input through all the layers. In neural networks
with injected noise, the method analytically takes into account uncertainties
resulting from this noise. Such feed-forward analytic propagation is
differentiable in parameters and can be trained end-to-end. Compared to
standard NN, which can be viewed as propagating only the means, we propagate
the mean and variance. The method can be useful in all scenarios that require
knowledge of the neuron statistics, e.g. when dealing with uncertain inputs,
considering sigmoid activations as probabilities of Bernoulli units, training
the models regularized by injected noise (dropout) or estimating activation
statistics over the dataset (as needed for normalization methods). In the
experiments we show the possible utility of the method in all these tasks as
well as its current limitations.
Stochastic Variational Inference with Gradient Linearization
Authors: Tobias Plötz, Anne S. Wannenwetsch, Stefan Roth
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Denoising, Optical Flow Estimation, Surface Reconstruction, Variational Inference

Variational inference has experienced a recent surge in popularity owing to
stochastic approaches, which have yielded practical tools for a wide range of
model classes. A key benefit is that stochastic variational inference obviates
the tedious process of deriving analytical expressions for closed-form variable
updates. Instead, one simply needs to derive the gradient of the log-posterior,
which is often much easier. Yet for certain model classes, the log-posterior
itself is difficult to optimize using standard gradient techniques. One such
example are random field models, where optimization based on gradient
linearization has proven popular, since it speeds up convergence significantly
and can avoid poor local optima. In this paper we propose stochastic
variational inference with gradient linearization (SVIGL). It is similarly
convenient as standard stochastic variational inference - all that is required
is a local linearization of the energy gradient. Its benefit over stochastic
variational inference with conventional gradient methods is a clear improvement
in convergence speed, while yielding comparable or even better variational
approximations in terms of KL divergence. We demonstrate the benefits of SVIGL
in three applications: Optical flow estimation, Poisson-Gaussian denoising, and
3D surface reconstruction.
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
Authors: Paul Rolland, Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher
Date: 2018-02-20 00:00:00
Tasks: Additive models, Bayesian Optimization, Hyperparameter Optimization, Vocal Bursts Intensity Prediction

Bayesian optimization (BO) is a popular technique for sequential black-box
function optimization, with applications including parameter tuning, robotics,
environmental monitoring, and more. One of the most important challenges in BO
is the development of algorithms that scale to high dimensions, which remains a
key open problem despite recent progress. In this paper, we consider the
approach of Kandasamy et al. (2015), in which the high-dimensional function
decomposes as a sum of lower-dimensional functions on subsets of the underlying
variables. In particular, we significantly generalize this approach by lifting
the assumption that the subsets are disjoint, and consider additive models with
arbitrary overlap among the subsets. By representing the dependencies via a
graph, we deduce an efficient message passing algorithm for optimizing the
acquisition function. In addition, we provide an algorithm for learning the
graph from samples based on Gibbs sampling. We empirically demonstrate the
effectiveness of our methods on both synthetic and real-world data.
Image Generation and Translation with Disentangled Representations
Authors: Tobias Hinz, Stefan Wermter
Date: 2018-03-28 00:00:00
Tasks: Conditional Image Generation, Face Generation, Image Generation, Image-to-Image Translation, Representation Learning, Translation

Generative models have made significant progress in the tasks of modeling
complex data distributions such as natural images. The introduction of
Generative Adversarial Networks (GANs) and auto-encoders lead to the
possibility of training on big data sets in an unsupervised manner. However,
for many generative models it is not possible to specify what kind of image
should be generated and it is not possible to translate existing images into
new images of similar domains. Furthermore, models that can perform
image-to-image translation often need distinct models for each domain, making
it hard to scale these systems to multiple domain image-to-image translation.
We introduce a model that can do both, controllable image generation and
image-to-image translation between multiple domains. We split our image
representation into two parts encoding unstructured and structured information
respectively. The latter is designed in a disentangled manner, so that
different parts encode different image characteristics. We train an encoder to
encode images into these representations and use a small amount of labeled data
to specify what kind of information should be encoded in the disentangled part.
A generator is trained to generate images from these representations using the
characteristics provided by the disentangled part of the representation.
Through this we can control what kind of images the generator generates,
translate images between different domains, and even learn unknown
data-generating factors while only using one single model.
The Multiscale Bowler-Hat Transform for Blood Vessel Enhancement in Retinal Images
Authors: Çiğdem Sazak, Carl J. Nelson, Boguslaw Obara
Date: 2017-09-16 00:00:00

Enhancement, followed by segmentation, quantification and modelling, of blood
vessels in retinal images plays an essential role in computer-aid retinopathy
diagnosis. In this paper, we introduce a new vessel enhancement method which is
the bowler-hat transform based on mathematical morphology. The proposed method
combines different structuring elements to detect innate features of
vessel-like structures. We evaluate the proposed method qualitatively and
quantitatively, and compare it with the existing, state-of-the-art methods
using both synthetic and real datasets. Our results show that the proposed
method achieves high-quality vessel-like structure enhancement in both
synthetic examples and in clinically relevant retinal images, and is shown to
be able to detect fine vessels while remaining robust at junctions.
ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes
Authors: Taihong Xiao, Jiapeng Hong, Jinwen Ma
Date: 2018-03-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute

Recent studies on face attribute transfer have achieved great success. A lot
of models are able to transfer face attributes with an input image. However,
they suffer from three limitations: (1) incapability of generating image by
exemplars; (2) being unable to transfer multiple face attributes
simultaneously; (3) low quality of generated images, such as low-resolution or
artifacts. To address these limitations, we propose a novel model which
receives two images of opposite attributes as inputs. Our model can transfer
exactly the same type of attributes from one image to another by exchanging
certain part of their encodings. All the attributes are encoded in a
disentangled manner in the latent space, which enables us to manipulate several
attributes simultaneously. Besides, our model learns the residual images so as
to facilitate training on higher resolution images. With the help of
multi-scale discriminators for adversarial training, it can even generate
high-quality images with finer details and less artifacts. We demonstrate the
effectiveness of our model on overcoming the above three limitations by
comparing with other methods on the CelebA face database. A pytorch
implementation is available at https://github.com/Prinsphield/ELEGANT.