pwc-archive/papers-with-abstracts huggingface dataset:
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations
Authors: Hao Wu, Feliks Nüske, Fabian Paul, Stefan Klus, Peter Koltai, Frank Noé
Date: 2016-10-20 00:00:00
Tasks: Clustering, Dimensionality Reduction

Markov state models (MSMs) and Master equation models are popular approaches
to approximate molecular kinetics, equilibria, metastable states, and reaction
coordinates in terms of a state space discretization usually obtained by
clustering. Recently, a powerful generalization of MSMs has been introduced,
the variational approach (VA) of molecular kinetics and its special case the
time-lagged independent component analysis (TICA), which allow us to
approximate slow collective variables and molecular kinetics by linear
combinations of smooth basis functions or order parameters. While it is known
how to estimate MSMs from trajectories whose starting points are not sampled
from an equilibrium ensemble, this has not yet been the case for TICA and the
VA. Previous estimates from short trajectories, have been strongly biased and
thus not variationally optimal. Here, we employ Koopman operator theory and
ideas from dynamic mode decomposition (DMD) to extend the VA and TICA to
non-equilibrium data. The main insight is that the VA and TICA provide a
coefficient matrix that we call Koopman model, as it approximates the
underlying dynamical (Koopman) operator in conjunction with the basis set used.
This Koopman model can be used to compute a stationary vector to reweight the
data to equilibrium. From such a Koopman-reweighted sample, equilibrium
expectation values and variationally optimal reversible Koopman models can be
constructed even with short simulations. The Koopman model can be used to
propagate densities, and its eigenvalue decomposition provide estimates of
relaxation timescales and slow collective variables for dimension reduction.
Koopman models are generalizations of Markov state models, TICA and the linear
VA and allow molecular kinetics to be described without a cluster
discretization.
Image Compression with SVD : A New Quality Metric Based On Energy Ratio
Authors: Henri Bruno Razafindradina, Paul Auguste Randriamitantsoa, Nicolas Raft Razafindrakoto
Date: 2017-01-22 00:00:00
Tasks: Image Compression, SSIM

Digital image compression is a technique that allows to reduce the size of an
image in order to increase the capacity storage devices and to optimize the use
of network bandwidth. The quality of compressed images with the techniques
based on the discrete cosine transform or the wavelet transform is generally
measured with PSNR or SSIM. Theses metrics are not suitable to images
compressed with the singular values decomposition. This paper presents a new
metric based on the energy ratio to measure the quality of the images coded
with the SVD. A series of tests on 512 * 512 pixels images show that, for a
rank k = 40 corresponding to a SSIM = 0,94 or PSNR = 35 dB, 99,9% of the energy
are restored. Three areas of image quality assessments were identified. This
new metric is also very accurate and could overcome the weaknesses of PSNR and
SSIM.
Binary Matrix Guessing Problem
Authors: Çağrı Latifoğlu
Date: 2017-01-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce the Binary Matrix Guessing Problem and provide two algorithms to
solve this problem. The first algorithm we introduce is Elementwise Probing
Algorithm (EPA) which is very fast under a score which utilizes Frobenius
Distance. The second algorithm is Additive Reinforcement Learning Algorithm
which combines ideas from perceptron algorithm and reinforcement learning
algorithm. This algorithm is significantly slower compared to first one, but
less restrictive and generalizes better. We compare computational performance
of both algorithms and provide numerical results.
  reason for withdrawal: Paper will be rewritten with experiments replicated on
verified and validated hardware and software.
A Novel Progressive Learning Technique for Multi-class Classification
Authors: Rajasekar Venkatesan, Meng Joo Er
Date: 2016-09-01 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

In this paper, a progressive learning technique for multi-class
classification is proposed. This newly developed learning technique is
independent of the number of class constraints and it can learn new classes
while still retaining the knowledge of previous classes. Whenever a new class
(non-native to the knowledge learnt thus far) is encountered, the neural
network structure gets remodeled automatically by facilitating new neurons and
interconnections, and the parameters are calculated in such a way that it
retains the knowledge learnt thus far. This technique is suitable for
real-world applications where the number of classes is often unknown and online
learning from real-time data is required. The consistency and the complexity of
the progressive learning technique are analyzed. Several standard datasets are
used to evaluate the performance of the developed technique. A comparative
study shows that the developed technique is superior.
Neural Multi-Source Morphological Reinflection
Authors: Katharina Kann, Ryan Cotterell, Hinrich Schütze
Date: 2016-12-19 00:00:00
Proceeding: EACL 2017 4
Tasks: Decoder, Form, LEMMA, TAG

We explore the task of multi-source morphological reinflection, which
generalizes the standard, single-source version. The input consists of (i) a
target tag and (ii) multiple pairs of source form and source tag for a lemma.
The motivation is that it is beneficial to have access to more than one source
form since different source forms can provide complementary information, e.g.,
different stems. We further present a novel extension to the encoder- decoder
recurrent neural architecture, consisting of multiple encoders, to better solve
the task. We show that our new architecture outperforms single-source
reinflection models and publish our dataset for multi-source morphological
reinflection to facilitate future research.
Perception-based energy functions in seam-cutting
Authors: Nan Li, Tianli Liao, Chao Wang
Date: 2017-01-22 00:00:00
Tasks: Image Stitching

Image stitching is challenging in consumer-level photography, due to
alignment difficulties in unconstrained shooting environment. Recent studies
show that seam-cutting approaches can effectively relieve artifacts generated
by local misalignment. Normally, seam-cutting is described in terms of energy
minimization, however, few of existing methods consider human perception in
their energy functions, which sometimes causes that a seam with minimum energy
is not most invisible in the overlapping region. In this paper, we propose a
novel perception-based energy function in the seam-cutting framework, which
considers the nonlinearity and the nonuniformity of human perception in energy
minimization. Our perception-based approach adopts a sigmoid metric to
characterize the perception of color discrimination, and a saliency weight to
simulate that human eyes incline to pay more attention to salient objects. In
addition, our seam-cutting composition can be easily implemented into other
stitching pipelines. Experiments show that our method outperforms the
seam-cutting method of the normal energy function, and a user study
demonstrates that our composed results are more consistent with human
perception.
Multimodal Fusion via a Series of Transfers for Noise Removal
Authors: Chang-Hwan Son, Xiao-Ping Zhang
Date: 2017-01-22 00:00:00

Near-infrared imaging has been considered as a solution to provide high
quality photographs in dim lighting conditions. This imaging system captures
two types of multimodal images: one is near-infrared gray image (NGI) and the
other is the visible color image (VCI). NGI is noise-free but it is grayscale,
whereas the VCI has colors but it contains noise. Moreover, there exist serious
edge and brightness discrepancies between NGI and VCI. To deal with this
problem, a new transfer-based fusion method is proposed for noise removal.
Different from conventional fusion approaches, the proposed method conducts a
series of transfers: contrast, detail, and color transfers. First, the proposed
contrast and detail transfers aim at solving the serious discrepancy problem,
thereby creating a new noise-free and detail-preserving NGI. Second, the
proposed color transfer models the unknown colors from the denoised VCI via a
linear transform, and then transfers natural-looking colors into the newly
generated NGI. Experimental results show that the proposed transfer-based
fusion method is highly successful in solving the discrepancy problem, thereby
describing edges and textures clearly as well as removing noise completely on
the fused images. Most of all, the proposed method is superior to conventional
fusion methods and guided filtering, and even the state-of-the-art fusion
methods based on scale map and layer decomposition.
Estimating Uncertainty Online Against an Adversary
Authors: Volodymyr Kuleshov, Stefano Ermon
Date: 2016-07-13 00:00:00
Tasks: General Classification, Medical Diagnosis, Question Answering

Assessing uncertainty is an important step towards ensuring the safety and
reliability of machine learning systems. Existing uncertainty estimation
techniques may fail when their modeling assumptions are not met, e.g. when the
data distribution differs from the one seen at training time. Here, we propose
techniques that assess a classification algorithm's uncertainty via calibrated
probabilities (i.e. probabilities that match empirical outcome frequencies in
the long run) and which are guaranteed to be reliable (i.e. accurate and
calibrated) on out-of-distribution input, including input generated by an
adversary. This represents an extension of classical online learning that
handles uncertainty in addition to guaranteeing accuracy under adversarial
assumptions. We establish formal guarantees for our methods, and we validate
them on two real-world problems: question answering and medical diagnosis from
genomic data.
Effective and Extensible Feature Extraction Method Using Genetic Algorithm-Based Frequency-Domain Feature Search for Epileptic EEG Multi-classification
Authors: Tingxi Wen, Zhongnan Zhang
Date: 2017-01-22 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), feature selection, General Classification

In this paper, a genetic algorithm-based frequency-domain feature search
(GAFDS) method is proposed for the electroencephalogram (EEG) analysis of
epilepsy. In this method, frequency-domain features are first searched and then
combined with nonlinear features. Subsequently, these features are selected and
optimized to classify EEG signals. The extracted features are analyzed
experimentally. The features extracted by GAFDS show remarkable independence,
and they are superior to the nonlinear features in terms of the ratio of
inter-class distance and intra-class distance. Moreover, the proposed feature
search method can additionally search for features of instantaneous frequency
in a signal after Hilbert transformation. The classification results achieved
using these features are reasonable, thus, GAFDS exhibits good extensibility.
Multiple classic classifiers (i.e., $k$-nearest neighbor, linear discriminant
analysis, decision tree, AdaBoost, multilayer perceptron, and Na\"ive Bayes)
achieve good results by using the features generated by GAFDS method and the
optimized selection. Specifically, the accuracies for the two-classification
and three-classification problems may reach up to 99% and 97%, respectively.
Results of several cross-validation experiments illustrate that GAFDS is
effective in feature extraction for EEG classification. Therefore, the proposed
feature selection and optimization model can improve classification accuracy.
DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets
Authors: David Richmond, Anna Payne-Tobin Jost, Talley Lambert, Jennifer Waters, Hunter Elliott
Date: 2017-01-22 00:00:00
Tasks: General Classification

Exposure to intense illumination light is an unavoidable consequence of
fluorescence microscopy, and poses a risk to the health of the sample in every
live-cell fluorescence microscopy experiment. Furthermore, the possible
side-effects of phototoxicity on the scientific conclusions that are drawn from
an imaging experiment are often unaccounted for. Previously, controlling for
phototoxicity in imaging experiments required additional labels and
experiments, limiting its widespread application. Here we provide a
proof-of-principle demonstration that the phototoxic effects of an imaging
experiment can be identified directly from a single phase-contrast image using
deep convolutional neural networks (ConvNets). This lays the groundwork for an
automated tool for assessing cell health in a wide range of imaging
experiments. Interpretability of such a method is crucial for its adoption. We
take steps towards interpreting the classification mechanism of the trained
ConvNet by visualizing salient features of images that contribute to accurate
classification.
Label Propagation on K-partite Graphs with Heterophily
Authors: Dingxiong Deng, Fan Bai, Yiqi Tang, Shuigeng Zhou, Cyrus Shahabi, Linhong Zhu
Date: 2017-01-21 00:00:00

In this paper, for the first time, we study label propagation in
heterogeneous graphs under heterophily assumption. Homophily label propagation
(i.e., two connected nodes share similar labels) in homogeneous graph (with
same types of vertices and relations) has been extensively studied before.
Unfortunately, real-life networks are heterogeneous, they contain different
types of vertices (e.g., users, images, texts) and relations (e.g.,
friendships, co-tagging) and allow for each node to propagate both the same and
opposite copy of labels to its neighbors. We propose a $\mathcal{K}$-partite
label propagation model to handle the mystifying combination of heterogeneous
nodes/relations and heterophily propagation. With this model, we develop a
novel label inference algorithm framework with update rules in near-linear time
complexity. Since real networks change over time, we devise an incremental
approach, which supports fast updates for both new data and evidence (e.g.,
ground truth labels) with guaranteed efficiency. We further provide a utility
function to automatically determine whether an incremental or a re-modeling
approach is favored. Extensive experiments on real datasets have verified the
effectiveness and efficiency of our approach, and its superiority over the
state-of-the-art label propagation methods.
Interactive Learning from Policy-Dependent Human Feedback
Authors: James MacGlashan, Mark K. Ho, Robert Loftin, Bei Peng, Guan Wang, David Roberts, Matthew E. Taylor, Michael L. Littman
Date: 2017-01-21 00:00:00
Proceeding: ICML 2017 8

This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has made the assumption that people provide feedback for decisions that is dependent on the behavior they are teaching and is independent from the learner's current policy. We present empirical results that show this assumption to be false -- whether human trainers give a positive or negative feedback for a decision is influenced by the learner's current policy. Based on this insight, we introduce {\em Convergent Actor-Critic by Humans} (COACH), an algorithm for learning from policy-dependent feedback that converges to a local optimum. Finally, we demonstrate that COACH can successfully learn multiple behaviors on a physical robot.
Neuromorphic Deep Learning Machines
Authors: Emre Neftci, Charles Augustine, Somnath Paul, Georgios Detorakis
Date: 2016-12-16 00:00:00
Tasks: Deep Learning

An ongoing challenge in neuromorphic computing is to devise general and
computationally efficient models of inference and learning which are compatible
with the spatial and temporal constraints of the brain. One increasingly
popular and successful approach is to take inspiration from inference and
learning algorithms used in deep neural networks. However, the workhorse of
deep learning, the gradient descent Back Propagation (BP) rule, often relies on
the immediate availability of network-wide information stored with
high-precision memory, and precise operations that are difficult to realize in
neuromorphic hardware. Remarkably, recent work showed that exact backpropagated
weights are not essential for learning deep representations. Random BP replaces
feedback weights with random ones and encourages the network to adjust its
feed-forward weights to learn pseudo-inverses of the (random) feedback weights.
Building on these results, we demonstrate an event-driven random BP (eRBP) rule
that uses an error-modulated synaptic plasticity for learning deep
representations in neuromorphic computing hardware. The rule requires only one
addition and two comparisons for each synaptic weight using a two-compartment
leaky Integrate & Fire (I&F) neuron, making it very suitable for implementation
in digital or mixed-signal neuromorphic hardware. Our results show that using
eRBP, deep representations are rapidly learned, achieving nearly identical
classification accuracies compared to artificial neural network simulations on
GPUs, while being robust to neural and synaptic state quantizations during
learning.
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogates
Authors: Ilija Ilievski, Taimoor Akhtar, Jiashi Feng, Christine Annette Shoemaker
Date: 2016-07-28 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, Hyperparameter Optimization, SMAC, SMAC+

Automatically searching for optimal hyperparameter configurations is of
crucial importance for applying deep learning algorithms in practice. Recently,
Bayesian optimization has been proposed for optimizing hyperparameters of
various machine learning algorithms. Those methods adopt probabilistic
surrogate models like Gaussian processes to approximate and minimize the
validation error function of hyperparameter values. However, probabilistic
surrogates require accurate estimates of sufficient statistics (e.g.,
covariance) of the error distribution and thus need many function evaluations
with a sizeable number of hyperparameters. This makes them inefficient for
optimizing hyperparameters of deep learning algorithms, which are highly
expensive to evaluate. In this work, we propose a new deterministic and
efficient hyperparameter optimization method that employs radial basis
functions as error surrogates. The proposed mixed integer algorithm, called
HORD, searches the surrogate for the most promising hyperparameter values
through dynamic coordinate search and requires many fewer function evaluations.
HORD does well in low dimensions but it is exceptionally better in higher
dimensions. Extensive evaluations on MNIST and CIFAR-10 for four deep neural
networks demonstrate HORD significantly outperforms the well-established
Bayesian optimization methods such as GP, SMAC, and TPE. For instance, on
average, HORD is more than 6 times faster than GP-EI in obtaining the best
configuration of 19 hyperparameters.
Learning Policies for Markov Decision Processes from Data
Authors: Manjesh K. Hanawal, Hao liu, Henghui Zhu, Ioannis Ch. Paschalidis
Date: 2017-01-21 00:00:00
Tasks: Robot Navigation

We consider the problem of learning a policy for a Markov decision process
consistent with data captured on the state-actions pairs followed by the
policy. We assume that the policy belongs to a class of parameterized policies
which are defined using features associated with the state-action pairs. The
features are known a priori, however, only an unknown subset of them could be
relevant. The policy parameters that correspond to an observed target policy
are recovered using $\ell_1$-regularized logistic regression that best fits the
observed state-action samples. We establish bounds on the difference between
the average reward of the estimated and the original policy (regret) in terms
of the generalization error and the ergodic coefficient of the underlying
Markov chain. To that end, we combine sample complexity theory and sensitivity
analysis of the stationary distribution of Markov chains. Our analysis suggests
that to achieve regret within order $O(\sqrt{\epsilon})$, it suffices to use
training sample size on the order of $\Omega(\log n \cdot poly(1/\epsilon))$,
where $n$ is the number of the features. We demonstrate the effectiveness of
our method on a synthetic robot navigation example.
Integration of Preferences in Decomposition Multi-Objective Optimization
Authors: Ke Li, Kalyanmoy Deb, Xin Yao
Date: 2017-01-20 00:00:00
Tasks: Decision Making

Most existing studies on evolutionary multi-objective optimization focus on
approximating the whole Pareto-optimal front. Nevertheless, rather than the
whole front, which demands for too many points (especially in a
high-dimensional space), the decision maker might only interest in a partial
region, called the region of interest. In this case, solutions outside this
region can be noisy to the decision making procedure. Even worse, there is no
guarantee that we can find the preferred solutions when tackling problems with
complicated properties or a large number of objectives. In this paper, we
develop a systematic way to incorporate the decision maker's preference
information into the decomposition-based evolutionary multi-objective
optimization methods. Generally speaking, our basic idea is a non-uniform
mapping scheme by which the originally uniformly distributed reference points
on a canonical simplex can be mapped to the new positions close to the
aspiration level vector specified by the decision maker. By these means, we are
able to steer the search process towards the region of interest either directly
or in an interactive manner and also handle a large number of objectives. In
the meanwhile, the boundary solutions can be approximated given the decision
maker's requirements. Furthermore, the extent of the region of the interest is
intuitively understandable and controllable in a closed form. Extensive
experiments, both proof-of-principle and on a variety of problems with 3 to 10
objectives, fully demonstrate the effectiveness of our proposed method for
approximating the preferred solutions in the region of interest.
Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks
Authors: Rahul Dey, Fathi M. Salem
Date: 2017-01-20 00:00:00
Tasks: Model Optimization

The paper evaluates three variants of the Gated Recurrent Unit (GRU) in
recurrent neural networks (RNN) by reducing parameters in the update and reset
gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and
show that these GRU-RNN variant models perform as well as the original GRU RNN
model while reducing the computational expense.