pwc-archive/papers-with-abstracts huggingface dataset:
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
Authors: Tianfan Xue, Jiajun Wu, Katherine L. Bouman, William T. Freeman
Date: 2016-07-09 00:00:00
Proceeding: NeurIPS 2016 12

We study the problem of synthesizing a number of likely future frames from a
single input image. In contrast to traditional methods, which have tackled this
problem in a deterministic or non-parametric way, we propose a novel approach
that models future frames in a probabilistic manner. Our probabilistic model
makes it possible for us to sample and synthesize many possible future frames
from a single input image. Future frame synthesis is challenging, as it
involves low- and high-level image and motion understanding. We propose a novel
network structure, namely a Cross Convolutional Network to aid in synthesizing
future frames; this network structure encodes image and motion information as
feature maps and convolutional kernels, respectively. In experiments, our model
performs well on synthetic data, such as 2D shapes and animated game sprites,
as well as on real-wold videos. We also show that our model can be applied to
tasks such as visual analogy-making, and present an analysis of the learned
network representations.
Linear Contextual Bandits with Knapsacks
Authors: Shipra Agrawal, Nikhil R. Devanur
Date: 2015-07-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Multi-Armed Bandits

We consider the linear contextual bandit problem with resource consumption,
in addition to reward generation. In each round, the outcome of pulling an arm
is a reward as well as a vector of resource consumptions. The expected values
of these outcomes depend linearly on the context of that arm. The
budget/capacity constraints require that the total consumption doesn't exceed
the budget for each resource. The objective is once again to maximize the total
reward. This problem turns out to be a common generalization of classic linear
contextual bandits (linContextual), bandits with knapsacks (BwK), and the
online stochastic packing problem (OSPP). We present algorithms with
near-optimal regret bounds for this problem. Our bounds compare favorably to
results on the unstructured version of the problem where the relation between
the contexts and the outcomes could be arbitrary, but the algorithm only
competes against a fixed set of policies accessible through an optimization
oracle. We combine techniques from the work on linContextual, BwK, and OSPP in
a nontrivial manner while also tackling new difficulties that are not present
in any of these special cases.
Deep Learning of Appearance Models for Online Object Tracking
Authors: Mengyao Zhai, Mehrsan Javan Roshtkhari, Greg Mori
Date: 2016-07-09 00:00:00
Tasks: Deep Learning, Object Tracking

This paper introduces a novel deep learning based approach for vision based
single target tracking. We address this problem by proposing a network
architecture which takes the input video frames and directly computes the
tracking score for any candidate target location by estimating the probability
distributions of the positive and negative examples. This is achieved by
combining a deep convolutional neural network with a Bayesian loss layer in a
unified framework. In order to deal with the limited number of positive
training examples, the network is pre-trained offline for a generic image
feature representation and then is fine-tuned in multiple steps. An online
fine-tuning step is carried out at every frame to learn the appearance of the
target. We adopt a two-stage iterative algorithm to adaptively update the
network parameters and maintain a probability density for target/non-target
regions. The tracker has been tested on the standard tracking benchmark and the
results indicate that the proposed solution achieves state-of-the-art tracking
results.
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives
Authors: Shipra Agrawal, Nikhil R. Devanur, Lihong Li
Date: 2015-06-10 00:00:00
Tasks: Multi-Armed Bandits, Open-Ended Question Answering

We consider a contextual version of multi-armed bandit problem with global
knapsack constraints. In each round, the outcome of pulling an arm is a scalar
reward and a resource consumption vector, both dependent on the context, and
the global knapsack constraints require the total consumption for each resource
to be below some pre-fixed budget. The learning agent competes with an
arbitrary set of context-dependent policies. This problem was introduced by
Badanidiyuru et al. (2014), who gave a computationally inefficient algorithm
with near-optimal regret bounds for it. We give a computationally efficient
algorithm for this problem with slightly better regret bounds, by generalizing
the approach of Agarwal et al. (2014) for the non-constrained version of the
problem. The computational time of our algorithm scales logarithmically in the
size of the policy space. This answers the main open question of Badanidiyuru
et al. (2014). We also extend our results to a variant where there are no
knapsack constraints but the objective is an arbitrary Lipschitz concave
function of the sum of outcome vectors.
Uncovering Locally Discriminative Structure for Feature Analysis
Authors: Sen Wang, Feiping Nie, Xiaojun Chang, Xue Li, Quan Z. Sheng, Lina Yao
Date: 2016-07-09 00:00:00

Manifold structure learning is often used to exploit geometric information
among data in semi-supervised feature learning algorithms. In this paper, we
find that local discriminative information is also of importance for
semi-supervised feature learning. We propose a method that utilizes both the
manifold structure of data and local discriminant information. Specifically, we
define a local clique for each data point. The k-Nearest Neighbors (kNN) is
used to determine the structural information within each clique. We then employ
a variant of Fisher criterion model to each clique for local discriminant
evaluation and sum all cliques as global integration into the framework. In
this way, local discriminant information is embedded. Labels are also utilized
to minimize distances between data from the same class. In addition, we use the
kernel method to extend our proposed model and facilitate feature learning in a
high-dimensional space after feature mapping. Experimental results show that
our method is superior to all other compared methods over a number of datasets.
Action Recognition with Joint Attention on Multi-Level Deep Features
Authors: Jialin Wu, Gu Wang, Wukui Yang, Xiangyang Ji
Date: 2016-07-09 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Temporal Action Localization, Visual Tracking

We propose a novel deep supervised neural network for the task of action
recognition in videos, which implicitly takes advantage of visual tracking and
shares the robustness of both deep Convolutional Neural Network (CNN) and
Recurrent Neural Network (RNN). In our method, a multi-branch model is proposed
to suppress noise from background jitters. Specifically, we firstly extract
multi-level deep features from deep CNNs and feed them into 3d-convolutional
network. After that we feed those feature cubes into our novel joint LSTM
module to predict labels and to generate attention regularization. We evaluate
our model on two challenging datasets: UCF101 and HMDB51. The results show that
our model achieves the state-of-art by only using convolutional features.
Screen Content Image Segmentation Using Robust Regression and Sparse Decomposition
Authors: Shervin Minaee, Yao Wang
Date: 2016-07-08 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, regression, Semantic Segmentation

This paper considers how to separate text and/or graphics from smooth
background in screen content and mixed document images and proposes two
approaches to perform this segmentation task. The proposed methods make use of
the fact that the background in each block is usually smoothly varying and can
be modeled well by a linear combination of a few smoothly varying basis
functions, while the foreground text and graphics create sharp discontinuity.
The algorithms separate the background and foreground pixels by trying to fit
background pixel values in the block into a smooth function using two different
schemes. One is based on robust regression, where the inlier pixels will be
considered as background, while remaining outlier pixels will be considered
foreground. The second approach uses a sparse decomposition framework where the
background and foreground layers are modeled with a smooth and sparse
components respectively. These algorithms have been tested on images extracted
from HEVC standard test sequences for screen content coding, and are shown to
have superior performance over previous approaches. The proposed methods can be
used in different applications such as text extraction, separate coding of
background and foreground for compression of screen content, and medical image
segmentation.
Avoiding pathologies in very deep networks
Authors: David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
Date: 2014-02-24 00:00:00
Tasks: Gaussian Processes

Choosing appropriate architectures and regularization strategies for deep
networks is crucial to good predictive performance. To shed light on this
problem, we analyze the analogous problem of constructing useful priors on
compositions of functions. Specifically, we study the deep Gaussian process, a
type of infinitely-wide, deep neural network. We show that in standard
architectures, the representational capacity of the network tends to capture
fewer degrees of freedom as the number of layers increases, retaining only a
single degree of freedom in the limit. We propose an alternate network
architecture which does not suffer from this pathology. We also examine deep
covariance functions, obtained by composing infinitely many feature transforms.
Lastly, we characterize the class of models obtained by performing dropout on
Gaussian processes.
Learning from Multiway Data: Simple and Efficient Tensor Regression
Authors: Rose Yu, Yan Liu
Date: 2016-07-08 00:00:00
Tasks: Multi-Task Learning, regression

Tensor regression has shown to be advantageous in learning tasks with
multi-directional relatedness. Given massive multiway data, traditional methods
are often too slow to operate on or suffer from memory bottleneck. In this
paper, we introduce subsampled tensor projected gradient to solve the problem.
Our algorithm is impressively simple and efficient. It is built upon projected
gradient method with fast tensor power iterations, leveraging randomized
sketching for further acceleration. Theoretical analysis shows that our
algorithm converges to the correct solution in fixed number of iterations. The
memory requirement grows linearly with the size of the problem. We demonstrate
superior empirical performance on both multi-linear multi-task learning and
spatio-temporal applications.
Pseudo-Marginal Hamiltonian Monte Carlo
Authors: Johan Alenlöv, Arnaud Doucet, Fredrik Lindsten
Date: 2016-07-08 00:00:00
Tasks: Bayesian Inference

Bayesian inference in the presence of an intractable likelihood function is computationally challenging. When following a Markov chain Monte Carlo (MCMC) approach to approximate the posterior distribution in this context, one typically either uses MCMC schemes which target the joint posterior of the parameters and some auxiliary latent variables, or pseudo-marginal Metropolis--Hastings (MH) schemes. The latter mimic a MH algorithm targeting the marginal posterior of the parameters by approximating unbiasedly the intractable likelihood. However, in scenarios where the parameters and auxiliary variables are strongly correlated under the posterior and/or this posterior is multimodal, Gibbs sampling or Hamiltonian Monte Carlo (HMC) will perform poorly and the pseudo-marginal MH algorithm, as any other MH scheme, will be inefficient for high dimensional parameters. We propose here an original MCMC algorithm, termed pseudo-marginal HMC, which combines the advantages of both HMC and pseudo-marginal schemes. Specifically, the pseudo-marginal HMC method is controlled by a precision parameter N, controlling the approximation of the likelihood and, for any N, it samples the marginal posterior of the parameters. Additionally, as N tends to infinity, its sample trajectories and acceptance probability converge to those of an ideal, but intractable, HMC algorithm which would have access to the marginal posterior of parameters and its gradient. We demonstrate through experiments that pseudo-marginal HMC can outperform significantly both standard HMC and pseudo-marginal MH schemes.
Fast Predictive Image Registration
Authors: Xiao Yang, Roland Kwitt, Marc Niethammer
Date: 2016-07-08 00:00:00
Tasks: Decoder, GPU, Image Registration

We present a method to predict image deformations based on patch-wise image
appearance. Specifically, we design a patch-based deep encoder-decoder network
which learns the pixel/voxel-wise mapping between image appearance and
registration parameters. Our approach can predict general deformation
parameterizations, however, we focus on the large deformation diffeomorphic
metric mapping (LDDMM) registration model. By predicting the LDDMM
momentum-parameterization we retain the desirable theoretical properties of
LDDMM, while reducing computation time by orders of magnitude: combined with
patch pruning, we achieve a 1500x/66x speed up compared to GPU-based
optimization for 2D/3D image registration. Our approach has better prediction
accuracy than predicting deformation or velocity fields and results in
diffeomorphic transformations. Additionally, we create a Bayesian probabilistic
version of our network, which allows evaluation of deformation field
uncertainty through Monte Carlo sampling using dropout at test time. We show
that deformation uncertainty highlights areas of ambiguous deformations. We
test our method on the OASIS brain image dataset in 2D and 3D.
Gaussian Error Linear Units (GELUs)
Authors: Dan Hendrycks, Kevin Gimpel
Date: 2016-06-27 00:00:00

We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU activation function is $x\Phi(x)$, where $\Phi(x)$ the standard Gaussian cumulative distribution function. The GELU nonlinearity weights inputs by their value, rather than gates inputs by their sign as in ReLUs ($x\mathbf{1}_{x>0}$). We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all considered computer vision, natural language processing, and speech tasks.
Proposal Flow
Authors: Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce
Date: 2015-11-16 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object

Finding image correspondences remains a challenging problem in the presence
of intra-class variations and large changes in scene layout.~Semantic flow
methods are designed to handle images depicting different instances of the same
object or scene category. We introduce a novel approach to semantic flow,
dubbed proposal flow, that establishes reliable correspondences using object
proposals. Unlike prevailing semantic flow approaches that operate on pixels or
regularly sampled local regions, proposal flow benefits from the
characteristics of modern object proposals, that exhibit high repeatability at
multiple scales, and can take advantage of both local and geometric consistency
constraints among proposals. We also show that proposal flow can effectively be
transformed into a conventional dense flow field. We introduce a new dataset
that can be used to evaluate both general semantic flow techniques and
region-based approaches such as proposal flow. We use this benchmark to compare
different matching algorithms, object proposals, and region features within
proposal flow, to the state of the art in semantic flow. This comparison, along
with experiments on standard datasets, demonstrates that proposal flow
significantly outperforms existing semantic flow methods in various settings.
Real-Time Anomaly Detection for Streaming Analytics
Authors: Subutai Ahmad, Scott Purdy
Date: 2016-07-08 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Much of the worlds data is streaming, time-series data, where anomalies give
significant information in critical situations. Yet detecting anomalies in
streaming data is a difficult task, requiring detectors to process data in
real-time, and learn while simultaneously making predictions. We present a
novel anomaly detection technique based on an on-line sequence memory algorithm
called Hierarchical Temporal Memory (HTM). We show results from a live
application that detects anomalies in financial metrics in real-time. We also
test the algorithm on NAB, a published benchmark for real-time anomaly
detection, where our algorithm achieves best-in-class results.
Explaining Deep Convolutional Neural Networks on Music Classification
Authors: Keunwoo Choi, George Fazekas, Mark Sandler
Date: 2016-07-08 00:00:00
Tasks: Chord Recognition, Classification, General Classification, Genre classification, Information Retrieval, Music Classification, Music Information Retrieval, Retrieval

Deep convolutional neural networks (CNNs) have been actively adopted in the
field of music information retrieval, e.g. genre classification, mood
detection, and chord recognition. However, the process of learning and
prediction is little understood, particularly when it is applied to
spectrograms. We introduce auralisation of a CNN to understand its underlying
mechanism, which is based on a deconvolution procedure introduced in [2].
Auralisation of a CNN is converting the learned convolutional features that are
obtained from deconvolution into audio signals. In the experiments and
discussions, we explain trained features of a 5-layer CNN based on the
deconvolved spectrograms and auralised signals. The pairwise correlations per
layers with varying different musical attributes are also investigated to
understand the evolution of the learnt features. It is shown that in the deep
layers, the features are learnt to capture textures, the patterns of continuous
distributions, rather than shapes of lines.
Document Clustering Games in Static and Dynamic Scenarios
Authors: Rocco Tripodi, Marcello Pelillo
Date: 2016-07-08 00:00:00
Tasks: Clustering

In this work we propose a game theoretic model for document clustering. Each
document to be clustered is represented as a player and each cluster as a
strategy. The players receive a reward interacting with other players that they
try to maximize choosing their best strategies. The geometry of the data is
modeled with a weighted graph that encodes the pairwise similarity among
documents, so that similar players are constrained to choose similar
strategies, updating their strategy preferences at each iteration of the games.
We used different approaches to find the prototypical elements of the clusters
and with this information we divided the players into two disjoint sets, one
collecting players with a definite strategy and the other one collecting
players that try to learn from others the correct strategy to play. The latter
set of players can be considered as new data points that have to be clustered
according to previous information. This representation is useful in scenarios
in which the data are streamed continuously. The evaluation of the system was
conducted on 13 document datasets using different settings. It shows that the
proposed method performs well compared to different document clustering
algorithms.
Learning opening books in partially observable games: using random seeds in Phantom Go
Authors: Tristan Cazenave, Jialin Liu, Fabien Teytaud, Olivier Teytaud
Date: 2016-07-08 00:00:00

Many artificial intelligences (AIs) are randomized. One can be lucky or
unlucky with the random seed; we quantify this effect and show that, maybe
contrarily to intuition, this is far from being negligible. Then, we apply two
different existing algorithms for selecting good seeds and good probability
distributions over seeds. This mainly leads to learning an opening book. We
apply this to Phantom Go, which, as all phantom games, is hard for opening book
learning. We improve the winning rate from 50% to 70% in 5x5 against the same
AI, and from approximately 0% to 40% in 5x5, 7x7 and 9x9 against a stronger
(learning) opponent.
On the Difficulty of Selecting Ising Models with Approximate Recovery
Authors: Jonathan Scarlett, Volkan Cevher
Date: 2016-02-11 00:00:00

In this paper, we consider the problem of estimating the underlying graph
associated with an Ising model given a number of independent and identically
distributed samples. We adopt an \emph{approximate recovery} criterion that
allows for a number of missed edges or incorrectly-included edges, in contrast
with the widely-studied exact recovery problem. Our main results provide
information-theoretic lower bounds on the sample complexity for graph classes
imposing constraints on the number of edges, maximal degree, and other
properties. We identify a broad range of scenarios where, either up to constant
factors or logarithmic factors, our lower bounds match the best known lower
bounds for the exact recovery criterion, several of which are known to be tight
or near-tight. Hence, in these cases, approximate recovery has a similar
difficulty to exact recovery in the minimax sense.
  Our bounds are obtained via a modification of Fano's inequality for handling
the approximate recovery criterion, along with suitably-designed ensembles of
graphs that can broadly be classed into two categories: (i) Those containing
graphs that contain several isolated edges or cliques and are thus difficult to
distinguish from the empty graph; (ii) Those containing graphs for which
certain groups of nodes are highly correlated, thus making it difficult to
determine precisely which edges connect them. We support our theoretical
results on these ensembles with numerical experiments.
Translating Bayesian Networks into Entity Relationship Models, Extended Version
Authors: Frank Rosner, Alexander Hinneburg
Date: 2016-07-08 00:00:00
Tasks: BIG-bench Machine Learning, Management, Topic Models

Big data analytics applications drive the convergence of data management and
machine learning. But there is no conceptual language available that is spoken
in both worlds. The main contribution of the paper is a method to translate
Bayesian networks, a main conceptual language for probabilistic graphical
models, into usable entity relationship models. The transformed representation
of a Bayesian network leaves out mathematical details about probabilistic
relationships but unfolds all information relevant for data management tasks.
As a real world example, we present the TopicExplorer system that uses Bayesian
topic models as a core component in an interactive, database-supported web
application. Last, we sketch a conceptual framework that eases machine learning
specific development tasks while building big data analytics applications.
Diffusion-Convolutional Neural Networks
Authors: James Atwood, Don Towsley
Date: 2015-11-06 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Classification, General Classification, GPU, Node Classification

We present diffusion-convolutional neural networks (DCNNs), a new model for
graph-structured data. Through the introduction of a diffusion-convolution
operation, we show how diffusion-based representations can be learned from
graph-structured data and used as an effective basis for node classification.
DCNNs have several attractive qualities, including a latent representation for
graphical data that is invariant under isomorphism, as well as polynomial-time
prediction and learning that can be represented as tensor operations and
efficiently implemented on the GPU. Through several experiments with real
structured datasets, we demonstrate that DCNNs are able to outperform
probabilistic relational models and kernel-on-graph methods at relational node
classification tasks.