pwc-archive/papers-with-abstracts huggingface dataset:
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Authors: John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
Date: 2015-10-08 00:00:00
Tasks: Model-based Reinforcement Learning, Model Predictive Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Data-efficient reinforcement learning (RL) in continuous state-action spaces
using very high-dimensional observations remains a key challenge in developing
fully autonomous systems. We consider a particularly important instance of this
challenge, the pixels-to-torques problem, where an RL agent learns a
closed-loop control policy ("torques") from pixel information only. We
introduce a data-efficient, model-based reinforcement learning algorithm that
learns such a closed-loop policy directly from pixel information. The key
ingredient is a deep dynamical model for learning a low-dimensional feature
embedding of images jointly with a predictive model in this low-dimensional
feature space. Joint learning is crucial for long-term predictions, which lie
at the core of the adaptive nonlinear model predictive control strategy that we
use for closed-loop control. Compared to state-of-the-art RL methods for
continuous states and actions, our approach learns quickly, scales to
high-dimensional state spaces, is lightweight and an important step toward
fully autonomous end-to-end learning from pixels to torques.
Feedforward Sequential Memory Neural Networks without Recurrent Feedback
Authors: ShiLiang Zhang, Hui Jiang, Si Wei, Li-Rong Dai
Date: 2015-10-09 00:00:00
Tasks: Language Modeling, Language Modelling

We introduce a new structure for memory neural networks, called feedforward
sequential memory networks (FSMN), which can learn long-term dependency without
using recurrent feedback. The proposed FSMN is a standard feedforward neural
networks equipped with learnable sequential memory blocks in the hidden layers.
In this work, we have applied FSMN to several language modeling (LM) tasks.
Experimental results have shown that the memory blocks in FSMN can learn
effective representations of long history. Experiments have shown that FSMN
based language models can significantly outperform not only feedforward neural
network (FNN) based LMs but also the popular recurrent neural network (RNN)
LMs.
Clustering Network Layers With the Strata Multilayer Stochastic Block Model
Authors: Natalie Stanley, Saray Shai, Dane Taylor, Peter J. Mucha
Date: 2015-07-07 00:00:00
Tasks: Clustering, Stochastic Block Model

Multilayer networks are a useful data structure for simultaneously capturing
multiple types of relationships between a set of nodes. In such networks, each
relational definition gives rise to a layer. While each layer provides its own
set of information, community structure across layers can be collectively
utilized to discover and quantify underlying relational patterns between nodes.
To concisely extract information from a multilayer network, we propose to
identify and combine sets of layers with meaningful similarities in community
structure. In this paper, we describe the "strata multilayer stochastic block
model'' (sMLSBM), a probabilistic model for multilayer community structure. The
central extension of the model is that there exist groups of layers, called
"strata'', which are defined such that all layers in a given stratum have
community structure described by a common stochastic block model (SBM). That
is, layers in a stratum exhibit similar node-to-community assignments and SBM
probability parameters. Fitting the sMLSBM to a multilayer network provides a
joint clustering that yields node-to-community and layer-to-stratum
assignments, which cooperatively aid one another during inference. We describe
an algorithm for separating layers into their appropriate strata and an
inference technique for estimating the SBM parameters for each stratum. We
demonstrate our method using synthetic networks and a multilayer network
inferred from data collected in the Human Microbiome Project.
Some Theory For Practical Classifier Validation
Authors: Eric Bax, Ya Le
Date: 2015-10-09 00:00:00

We compare and contrast two approaches to validating a trained classifier
while using all in-sample data for training. One is simultaneous validation
over an organized set of hypotheses (SVOOSH), the well-known method that began
with VC theory. The other is withhold and gap (WAG). WAG withholds a validation
set, trains a holdout classifier on the remaining data, uses the validation
data to validate that classifier, then adds the rate of disagreement between
the holdout classifier and one trained using all in-sample data, which is an
upper bound on the difference in error rates. We show that complex hypothesis
classes and limited training data can make WAG a favorable alternative.
Technical Report of Participation in Higgs Boson Machine Learning Challenge
Authors: S. Raza Ahmad
Date: 2015-10-09 00:00:00
Tasks: BIG-bench Machine Learning

This report entails the detailed description of the approach and
methodologies taken as part of competing in the Higgs Boson Machine Learning
Competition hosted by Kaggle Inc. and organized by CERN et al. It briefly
describes the theoretical background of the problem and the motivation for
taking part in the competition. Furthermore, the various machine learning
models and algorithms analyzed and implemented during the 4 month period of
participation are discussed and compared. Special attention is paid to the Deep
Learning techniques and architectures implemented from scratch using Python and
NumPy for this competition.
Free-hand Sketch Synthesis with Deformable Stroke Models
Authors: Yi Li, Yi-Zhe Song, Timothy Hospedales, Shaogang Gong
Date: 2015-10-09 00:00:00

We present a generative model which can automatically summarize the stroke
composition of free-hand sketches of a given category. When our model is fit to
a collection of sketches with similar poses, it discovers and learns the
structure and appearance of a set of coherent parts, with each part represented
by a group of strokes. It represents both consistent (topology) as well as
diverse aspects (structure and appearance variations) of each sketch category.
Key to the success of our model are important insights learned from a
comprehensive study performed on human stroke data. By fitting this model to
images, we are able to synthesize visually similar and pleasant free-hand
sketches.
Stochastic recursive inclusion in two timescales with an application to the Lagrangian dual problem
Authors: Arunselvan Ramaswamy, Shalabh Bhatnagar
Date: 2015-02-06 00:00:00

In this paper we present a framework to analyze the asymptotic behavior of
two timescale stochastic approximation algorithms including those with
set-valued mean fields. This paper builds on the works of Borkar and Perkins &
Leslie. The framework presented herein is more general as compared to the
synchronous two timescale framework of Perkins \& Leslie, however the
assumptions involved are easily verifiable. As an application, we use this
framework to analyze the two timescale stochastic approximation algorithm
corresponding to the Lagrangian dual problem in optimization theory.
Functional Frank-Wolfe Boosting for General Loss Functions
Authors: Chu Wang, Yingfei Wang, Weinan E, Robert Schapire
Date: 2015-10-09 00:00:00
Tasks: Binary Classification, General Classification, regression

Boosting is a generic learning method for classification and regression. Yet,
as the number of base hypotheses becomes larger, boosting can lead to a
deterioration of test performance. Overfitting is an important and ubiquitous
phenomenon, especially in regression settings. To avoid overfitting, we
consider using $l_1$ regularization. We propose a novel Frank-Wolfe type
boosting algorithm (FWBoost) applied to general loss functions. By using
exponential loss, the FWBoost algorithm can be rewritten as a variant of
AdaBoost for binary classification. FWBoost algorithms have exactly the same
form as existing boosting methods, in terms of making calls to a base learning
algorithm with different weights update. This direct connection between
boosting and Frank-Wolfe yields a new algorithm that is as practical as
existing boosting methods but with new guarantees and rates of convergence.
Experimental results show that the test performance of FWBoost is not degraded
with larger rounds in boosting, which is consistent with the theoretical
analysis.
Fixed-point algorithms for learning determinantal point processes
Authors: Zelda Mariet, Suvrit Sra
Date: 2015-08-04 00:00:00
Tasks: Point Processes

Determinantal point processes (DPPs) offer an elegant tool for encoding
probabilities over subsets of a ground set. Discrete DPPs are parametrized by a
positive semidefinite matrix (called the DPP kernel), and estimating this
kernel is key to learning DPPs from observed data. We consider the task of
learning the DPP kernel, and develop for it a surprisingly simple yet effective
new algorithm. Our algorithm offers the following benefits over previous
approaches: (a) it is much simpler; (b) it yields equally good and sometimes
even better local maxima; and (c) it runs an order of magnitude faster on large
problems. We present experimental results on both real and simulated data to
illustrate the numerical performance of our technique.
Statistical Analysis of Persistence Intensity Functions
Authors: Yen-Chi Chen, Daren Wang, Alessandro Rinaldo, Larry Wasserman
Date: 2015-10-08 00:00:00
Tasks: Clustering, Topological Data Analysis

Persistence diagrams are two-dimensional plots that summarize the topological
features of functions and are an important part of topological data analysis. A
problem that has received much attention is how deal with sets of persistence
diagrams. How do we summarize them, average them or cluster them? One approach
-- the persistence intensity function -- was introduced informally by
Edelsbrunner, Ivanov, and Karasev (2012). Here we provide a modification and
formalization of this approach. Using the persistence intensity function, we
can visualize multiple diagrams, perform clustering and conduct two-sample
tests.
Uniform Learning in a Deep Neural Network via "Oddball" Stochastic Gradient Descent
Authors: Andrew J. R. Simpson
Date: 2015-10-08 00:00:00
Tasks: valid

When training deep neural networks, it is typically assumed that the training
examples are uniformly difficult to learn. Or, to restate, it is assumed that
the training error will be uniformly distributed across the training examples.
Based on these assumptions, each training example is used an equal number of
times. However, this assumption may not be valid in many cases. "Oddball SGD"
(novelty-driven stochastic gradient descent) was recently introduced to drive
training probabilistically according to the error distribution - training
frequency is proportional to training error magnitude. In this article, using a
deep neural network to encode a video, we show that oddball SGD can be used to
enforce uniform error across the training set.
Distilling Model Knowledge
Authors: George Papamakarios
Date: 2015-10-08 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, Knowledge Distillation, model, Model Compression

Top-performing machine learning systems, such as deep neural networks, large
ensembles and complex probabilistic graphical models, can be expensive to
store, slow to evaluate and hard to integrate into larger systems. Ideally, we
would like to replace such cumbersome models with simpler models that perform
equally well.
  In this thesis, we study knowledge distillation, the idea of extracting the
knowledge contained in a complex model and injecting it into a more convenient
model. We present a general framework for knowledge distillation, whereby a
convenient model of our choosing learns how to mimic a complex model, by
observing the latter's behaviour and being penalized whenever it fails to
reproduce it.
  We develop our framework within the context of three distinct machine
learning applications: (a) model compression, where we compress large
discriminative models, such as ensembles of neural networks, into models of
much smaller size; (b) compact predictive distributions for Bayesian inference,
where we distil large bags of MCMC samples into compact predictive
distributions in closed form; (c) intractable generative models, where we
distil unnormalizable models such as RBMs into tractable models such as NADEs.
  We contribute to the state of the art with novel techniques and ideas. In
model compression, we describe and implement derivative matching, which allows
for better distillation when data is scarce. In compact predictive
distributions, we introduce online distillation, which allows for significant
savings in memory. Finally, in intractable generative models, we show how to
use distilled models to robustly estimate intractable quantities of the
original model, such as its intractable partition function.
Learning Data-driven Reflectance Priors for Intrinsic Image Decomposition
Authors: Tinghui Zhou, Philipp Krähenbühl, Alexei A. Efros
Date: 2015-10-08 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Relighting, Intrinsic Image Decomposition

We propose a data-driven approach for intrinsic image decomposition, which is
the process of inferring the confounding factors of reflectance and shading in
an image. We pose this as a two-stage learning problem. First, we train a model
to predict relative reflectance ordering between image patches (`brighter',
`darker', `same') from large-scale human annotations, producing a data-driven
reflectance prior. Second, we show how to naturally integrate this learned
prior into existing energy minimization frameworks for intrinsic image
decomposition. We compare our method to the state-of-the-art approach of Bell
et al. on both decomposition and image relighting tasks, demonstrating the
benefits of the simple relative reflectance prior, especially for scenes under
challenging lighting conditions.
Encoding Reality: Prediction-Assisted Cortical Learning Algorithm in Hierarchical Temporal Memory
Authors: Fergal Byrne
Date: 2015-09-28 00:00:00

In the decade since Jeff Hawkins proposed Hierarchical Temporal Memory (HTM)
as a model of neocortical computation, the theory and the algorithms have
evolved dramatically. This paper presents a detailed description of HTM's
Cortical Learning Algorithm (CLA), including for the first time a rigorous
mathematical formulation of all aspects of the computations. Prediction
Assisted CLA (paCLA), a refinement of the CLA is presented, which is both
closer to the neuroscience and adds significantly to the computational power.
Finally, we summarise the key functions of neocortex which are expressed in
paCLA implementations.
Distance-weighted Support Vector Machine
Authors: Xingye Qiao, Lingsong Zhang
Date: 2013-10-11 00:00:00
Tasks: Classification, General Classification

A novel linear classification method that possesses the merits of both the
Support Vector Machine (SVM) and the Distance-weighted Discrimination (DWD) is
proposed in this article. The proposed Distance-weighted Support Vector Machine
method can be viewed as a hybrid of SVM and DWD that finds the classification
direction by minimizing mainly the DWD loss, and determines the intercept term
in the SVM manner. We show that our method inheres the merit of DWD, and hence,
overcomes the data-piling and overfitting issue of SVM. On the other hand, the
new method is not subject to imbalanced data issue which was a main advantage
of SVM over DWD. It uses an unusual loss which combines the Hinge loss (of SVM)
and the DWD loss through a trick of axillary hyperplane. Several theoretical
properties, including Fisher consistency and asymptotic normality of the DWSVM
solution are developed. We use some simulated examples to show that the new
method can compete DWD and SVM on both classification performance and
interpretability. A real data application further establishes the usefulness of
our approach.
Texture Modelling with Nested High-order Markov-Gibbs Random Fields
Authors: Ralph Versteegen, Georgy Gimel'farb, Patricia Riddle
Date: 2015-10-08 00:00:00
Tasks: Texture Synthesis, Vocal Bursts Intensity Prediction

Currently, Markov-Gibbs random field (MGRF) image models which include
high-order interactions are almost always built by modelling responses of a
stack of local linear filters. Actual interaction structure is specified
implicitly by the filter coefficients. In contrast, we learn an explicit
high-order MGRF structure by considering the learning process in terms of
general exponential family distributions nested over base models, so that
potentials added later can build on previous ones. We relatively rapidly add
new features by skipping over the costly optimisation of parameters.
  We introduce the use of local binary patterns as features in MGRF texture
models, and generalise them by learning offsets to the surrounding pixels.
These prove effective as high-order features, and are fast to compute. Several
schemes for selecting high-order features by composition or search of a small
subclass are compared. Additionally we present a simple modification of the
maximum likelihood as a texture modelling-specific objective function which
aims to improve generalisation by local windowing of statistics.
  The proposed method was experimentally evaluated by learning high-order MGRF
models for a broad selection of complex textures and then performing texture
synthesis, and succeeded on much of the continuum from stochastic through
irregularly structured to near-regular textures. Learning interaction structure
is very beneficial for textures with large-scale structure, although those with
complex irregular structure still provide difficulties. The texture models were
also quantitatively evaluated on two tasks and found to be competitive with
other works: grading of synthesised textures by a panel of observers; and
comparison against several recent MGRF models by evaluation on a constrained
inpainting task.
The Knowledge Gradient with Logistic Belief Models for Binary Classification
Authors: Yingfei Wang, Chu Wang, Warren Powell
Date: 2015-10-08 00:00:00
Tasks: Binary Classification, Classification, Decision Making, General Classification, Sequential Decision Making

We consider sequential decision making problems for binary classification
scenario in which the learner takes an active role in repeatedly selecting
samples from the action pool and receives the binary label of the selected
alternatives. Our problem is motivated by applications where observations are
time consuming and/or expensive, resulting in small samples. The goal is to
identify the best alternative with the highest response. We use Bayesian
logistic regression to predict the response of each alternative. By formulating
the problem as a Markov decision process, we develop a knowledge-gradient type
policy to guide the experiment by maximizing the expected value of information
of labeling each alternative and provide a finite-time analysis on the
estimated error. Experiments on benchmark UCI datasets demonstrate the
effectiveness of the proposed method.
Automata networks for memory loss effects in the formation of linguistic conventions
Authors: Javier Vera, Eric Goles
Date: 2015-08-07 00:00:00

This work attempts to give new theoretical insights to the absence of
intermediate stages in the evolution of language. In particular, it is
developed an automata networks approach to a crucial question: how a population
of language users can reach agreement on a linguistic convention? To describe
the appearance of sharp transitions in the self-organization of language, it is
adopted an extremely simple model of (working) memory. At each time step,
language users simply loss part of their word-memories. Through computer
simulations of low-dimensional lattices, it appear sharp transitions at
critical values that depend on the size of the vicinities of the individuals.
DeepMatching: Hierarchical Deformable Dense Matching
Authors: Jerome Revaud, Philippe Weinzaepfel, Zaid Harchaoui, Cordelia Schmid
Date: 2015-06-25 00:00:00
Tasks: Dense Pixel Correspondence Estimation, Optical Flow Estimation

We introduce a novel matching algorithm, called DeepMatching, to compute
dense correspondences between images. DeepMatching relies on a hierarchical,
multi-layer, correlational architecture designed for matching images and was
inspired by deep convolutional approaches. The proposed matching algorithm can
handle non-rigid deformations and repetitive textures and efficiently
determines dense correspondences in the presence of significant changes between
images. We evaluate the performance of DeepMatching, in comparison with
state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al
2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013)
datasets. DeepMatching outperforms the state-of-the-art algorithms and shows
excellent results in particular for repetitive textures.We also propose a
method for estimating optical flow, called DeepFlow, by integrating
DeepMatching in the large displacement optical flow (LDOF) approach of Brox and
Malik (2011). Compared to existing matching algorithms, additional robustness
to large displacements and complex motion is obtained thanks to our matching
approach. DeepFlow obtains competitive performance on public benchmarks for
optical flow estimation.
Empirical Analysis of Sampling Based Estimators for Evaluating RBMs
Authors: Vidyadhar Upadhya, P. S. Sastry
Date: 2015-10-08 00:00:00

The Restricted Boltzmann Machines (RBM) can be used either as classifiers or
as generative models. The quality of the generative RBM is measured through the
average log-likelihood on test data. Due to the high computational complexity
of evaluating the partition function, exact calculation of test log-likelihood
is very difficult. In recent years some estimation methods are suggested for
approximate computation of test log-likelihood. In this paper we present an
empirical comparison of the main estimation methods, namely, the AIS algorithm
for estimating the partition function, the CSL method for directly estimating
the log-likelihood, and the RAISE algorithm that combines these two ideas. We
use the MNIST data set to learn the RBM and then compare these methods for
estimating the test log-likelihood.
Attribute-Graph: A Graph based approach to Image Ranking
Authors: Nikita Prabhu, R. Venkatesh Babu
Date: 2015-09-22 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Semantic Similarity, Semantic Textual Similarity

We propose a novel image representation, termed Attribute-Graph, to rank
images by their semantic similarity to a given query image. An Attribute-Graph
is an undirected fully connected graph, incorporating both local and global
image characteristics. The graph nodes characterise objects as well as the
overall scene context using mid-level semantic attributes, while the edges
capture the object topology. We demonstrate the effectiveness of
Attribute-Graphs by applying them to the problem of image ranking. We benchmark
the performance of our algorithm on the 'rPascal' and 'rImageNet' datasets,
which we have created in order to evaluate the ranking performance on complex
queries containing multiple objects. Our experimental evaluation shows that
modelling images as Attribute-Graphs results in improved ranking performance
over existing techniques.