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Learning Sparse Polymatrix Games in Polynomial Time and Sample
Complexity | cs.LG | We consider the problem of learning sparse polymatrix games from observations
of strategic interactions. We show that a polynomial time method based on
$\ell_{1,2}$-group regularized logistic regression recovers a game, whose Nash
equilibria are the $\epsilon$-Nash equilibria of the game from which the data
was generated (true game), in $\mathcal{O}(m^4 d^4 \log (pd))$ samples of
strategy profiles --- where $m$ is the maximum number of pure strategies of a
player, $p$ is the number of players, and $d$ is the maximum degree of the game
graph. Under slightly more stringent separability conditions on the payoff
matrices of the true game, we show that our method learns a game with the exact
same Nash equilibria as the true game. We also show that $\Omega(d \log (pm))$
samples are necessary for any method to consistently recover a game, with the
same Nash-equilibria as the true game, from observations of strategic
interactions. We verify our theoretical results through simulation experiments.
| Asish Ghoshal and Jean Honorio | null | 1706.05648 | null | null |
On the convergence of mirror descent beyond stochastic convex
programming | math.OC cs.LG | In this paper, we examine the convergence of mirror descent in a class of
stochastic optimization problems that are not necessarily convex (or even
quasi-convex), and which we call variationally coherent. Since the standard
technique of "ergodic averaging" offers no tangible benefits beyond convex
programming, we focus directly on the algorithm's last generated sample (its
"last iterate"), and we show that it converges with probabiility $1$ if the
underlying problem is coherent. We further consider a localized version of
variational coherence which ensures local convergence of stochastic mirror
descent (SMD) with high probability. These results contribute to the landscape
of non-convex stochastic optimization by showing that (quasi-)convexity is not
essential for convergence to a global minimum: rather, variational coherence, a
much weaker requirement, suffices. Finally, building on the above, we reveal an
interesting insight regarding the convergence speed of SMD: in problems with
sharp minima (such as generic linear programs or concave minimization
problems), SMD reaches a minimum point in a finite number of steps (a.s.), even
in the presence of persistent gradient noise. This result is to be contrasted
with existing black-box convergence rate estimates that are only asymptotic.
| Zhengyuan Zhou and Panayotis Mertikopoulos and Nicholas Bambos and
Stephen Boyd and Peter Glynn | null | 1706.05681 | null | null |
Sparse Neural Networks Topologies | cs.LG cs.NE stat.ML | We propose Sparse Neural Network architectures that are based on random or
structured bipartite graph topologies. Sparse architectures provide compression
of the models learned and speed-ups of computations, they can also surpass
their unstructured or fully connected counterparts. As we show, even more
compact topologies of the so-called SNN (Sparse Neural Network) can be achieved
with the use of structured graphs of connections between consecutive layers of
neurons. In this paper, we investigate how the accuracy and training speed of
the models depend on the topology and sparsity of the neural network. Previous
approaches using sparcity are all based on fully connected neural network
models and create sparcity during training phase, instead we explicitly define
a sparse architectures of connections before the training. Building compact
neural network models is coherent with empirical observations showing that
there is much redundancy in learned neural network models. We show
experimentally that the accuracy of the models learned with neural networks
depends on expander-like properties of the underlying topologies such as the
spectral gap and algebraic connectivity rather than the density of the graphs
of connections.
| Alfred Bourely, John Patrick Boueri, Krzysztof Choromonski | null | 1706.05683 | null | null |
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning | cs.LG cs.DC | It has been experimentally observed that distributed implementations of
mini-batch stochastic gradient descent (SGD) algorithms exhibit speedup
saturation and decaying generalization ability beyond a particular batch-size.
In this work, we present an analysis hinting that high similarity between
concurrently processed gradients may be a cause of this performance
degradation. We introduce the notion of gradient diversity that measures the
dissimilarity between concurrent gradient updates, and show its key role in the
performance of mini-batch SGD. We prove that on problems with high gradient
diversity, mini-batch SGD is amenable to better speedups, while maintaining the
generalization performance of serial (one sample) SGD. We further establish
lower bounds on convergence where mini-batch SGD slows down beyond a particular
batch-size, solely due to the lack of gradient diversity. We provide
experimental evidence indicating the key role of gradient diversity in
distributed learning, and discuss how heuristics like dropout, Langevin
dynamics, and quantization can improve it.
| Dong Yin, Ashwin Pananjady, Max Lam, Dimitris Papailiopoulos, Kannan
Ramchandran, Peter Bartlett | null | 1706.05699 | null | null |
Addressing Item-Cold Start Problem in Recommendation Systems using Model
Based Approach and Deep Learning | cs.IR cs.LG stat.ML | Traditional recommendation systems rely on past usage data in order to
generate new recommendations. Those approaches fail to generate sensible
recommendations for new users and items into the system due to missing
information about their past interactions. In this paper, we propose a solution
for successfully addressing item-cold start problem which uses model-based
approach and recent advances in deep learning. In particular, we use latent
factor model for recommendation, and predict the latent factors from item's
descriptions using convolutional neural network when they cannot be obtained
from usage data. Latent factors obtained by applying matrix factorization to
the available usage data are used as ground truth to train the convolutional
neural network. To create latent factor representations for the new items, the
convolutional neural network uses their textual description. The results from
the experiments reveal that the proposed approach significantly outperforms
several baseline estimators.
| Ivica Obadi\'c, Gjorgji Madjarov (1), Ivica Dimitrovski (1), Dejan
Gjorgjevikj (1) ((1) Faculty of Computer Science and Engineering, Ss. Cyril
and Methodius University, Skopje, Macedonia) | null | 1706.0573 | null | null |
Learning Hierarchical Information Flow with Recurrent Neural Modules | cs.LG cs.AI | We propose ThalNet, a deep learning model inspired by neocortical
communication via the thalamus. Our model consists of recurrent neural modules
that send features through a routing center, endowing the modules with the
flexibility to share features over multiple time steps. We show that our model
learns to route information hierarchically, processing input data by a chain of
modules. We observe common architectures, such as feed forward neural networks
and skip connections, emerging as special cases of our architecture, while
novel connectivity patterns are learned for the text8 compression task. Our
model outperforms standard recurrent neural networks on several sequential
benchmarks.
| Danijar Hafner, Alex Irpan, James Davidson, Nicolas Heess | null | 1706.05744 | null | null |
Dex: Incremental Learning for Complex Environments in Deep Reinforcement
Learning | stat.ML cs.AI cs.LG | This paper introduces Dex, a reinforcement learning environment toolkit
specialized for training and evaluation of continual learning methods as well
as general reinforcement learning problems. We also present the novel continual
learning method of incremental learning, where a challenging environment is
solved using optimal weight initialization learned from first solving a similar
easier environment. We show that incremental learning can produce vastly
superior results than standard methods by providing a strong baseline method
across ten Dex environments. We finally develop a saliency method for
qualitative analysis of reinforcement learning, which shows the impact
incremental learning has on network attention.
| Nick Erickson and Qi Zhao | null | 1706.05749 | null | null |
Dipole: Diagnosis Prediction in Healthcare via Attention-based
Bidirectional Recurrent Neural Networks | cs.LG | Predicting the future health information of patients from the historical
Electronic Health Records (EHR) is a core research task in the development of
personalized healthcare. Patient EHR data consist of sequences of visits over
time, where each visit contains multiple medical codes, including diagnosis,
medication, and procedure codes. The most important challenges for this task
are to model the temporality and high dimensionality of sequential EHR data and
to interpret the prediction results. Existing work solves this problem by
employing recurrent neural networks (RNNs) to model EHR data and utilizing
simple attention mechanism to interpret the results. However, RNN-based
approaches suffer from the problem that the performance of RNNs drops when the
length of sequences is large, and the relationships between subsequent visits
are ignored by current RNN-based approaches. To address these issues, we
propose {\sf Dipole}, an end-to-end, simple and robust model for predicting
patients' future health information. Dipole employs bidirectional recurrent
neural networks to remember all the information of both the past visits and the
future visits, and it introduces three attention mechanisms to measure the
relationships of different visits for the prediction. With the attention
mechanisms, Dipole can interpret the prediction results effectively. Dipole
also allows us to interpret the learned medical code representations which are
confirmed positively by medical experts. Experimental results on two real world
EHR datasets show that the proposed Dipole can significantly improve the
prediction accuracy compared with the state-of-the-art diagnosis prediction
approaches and provide clinically meaningful interpretation.
| Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao | 10.1145/3097983.3098088 | 1706.05764 | null | null |
Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of
Deep Neural Network Models with Keras | cs.SD cs.LG cs.MM | We introduce Kapre, Keras layers for audio and music signal preprocessing.
Music research using deep neural networks requires a heavy and tedious
preprocessing stage, for which audio processing parameters are often ignored in
parameter optimisation. To solve this problem, Kapre implements time-frequency
conversions, normalisation, and data augmentation as Keras layers. We report
simple benchmark results, showing real-time on-GPU preprocessing adds a
reasonable amount of computation.
| Keunwoo Choi, Deokjin Joo, Juho Kim | null | 1706.05781 | null | null |
An a Priori Exponential Tail Bound for k-Folds Cross-Validation | stat.ML cs.LG | We consider a priori generalization bounds developed in terms of
cross-validation estimates and the stability of learners. In particular, we
first derive an exponential Efron-Stein type tail inequality for the
concentration of a general function of n independent random variables. Next,
under some reasonable notion of stability, we use this exponential tail bound
to analyze the concentration of the k-fold cross-validation (KFCV) estimate
around the true risk of a hypothesis generated by a general learning rule.
While the accumulated literature has often attributed this concentration to the
bias and variance of the estimator, our bound attributes this concentration to
the stability of the learning rule and the number of folds k. This insight
raises valid concerns related to the practical use of KFCV and suggests
research directions to obtain reliable empirical estimates of the actual risk.
| Karim Abou-Moustafa and Csaba Szepesvari | null | 1706.05801 | null | null |
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning
Dynamics and Interpretability | stat.ML cs.LG | We propose a new technique, Singular Vector Canonical Correlation Analysis
(SVCCA), a tool for quickly comparing two representations in a way that is both
invariant to affine transform (allowing comparison between different layers and
networks) and fast to compute (allowing more comparisons to be calculated than
with previous methods). We deploy this tool to measure the intrinsic
dimensionality of layers, showing in some cases needless over-parameterization;
to probe learning dynamics throughout training, finding that networks converge
to final representations from the bottom up; to show where class-specific
information in networks is formed; and to suggest new training regimes that
simultaneously save computation and overfit less. Code:
https://github.com/google/svcca/
| Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein | null | 1706.05806 | null | null |
Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector
Machine Classifiers | cs.LG cs.AI stat.ML | This work proposes a new algorithm for training a re-weighted L2 Support
Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Cand\`es
et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In
particular, the margin required for each training vector is set independently,
defining a new weighted SVM model. These weights are selected to be binary, and
they are automatically adapted during the training of the model, resulting in a
variation of the Frank-Wolfe optimization algorithm with essentially the same
computational complexity as the original algorithm. As shown experimentally,
this algorithm is computationally cheaper to apply since it requires less
iterations to converge, and it produces models with a sparser representation in
terms of support vectors and which are more stable with respect to the
selection of the regularization hyper-parameter.
| Carlos M. Ala\'iz and Johan A. K. Suykens | null | 1706.05928 | null | null |
Deep Counterfactual Networks with Propensity-Dropout | cs.LG stat.ML | We propose a novel approach for inferring the individualized causal effects
of a treatment (intervention) from observational data. Our approach
conceptualizes causal inference as a multitask learning problem; we model a
subject's potential outcomes using a deep multitask network with a set of
shared layers among the factual and counterfactual outcomes, and a set of
outcome-specific layers. The impact of selection bias in the observational data
is alleviated via a propensity-dropout regularization scheme, in which the
network is thinned for every training example via a dropout probability that
depends on the associated propensity score. The network is trained in
alternating phases, where in each phase we use the training examples of one of
the two potential outcomes (treated and control populations) to update the
weights of the shared layers and the respective outcome-specific layers.
Experiments conducted on data based on a real-world observational study show
that our algorithm outperforms the state-of-the-art.
| Ahmed M. Alaa, Michael Weisz, and Mihaela van der Schaar | null | 1706.05966 | null | null |
Clustering is semidefinitely not that hard: Nonnegative SDP for manifold
disentangling | cs.LG | In solving hard computational problems, semidefinite program (SDP)
relaxations often play an important role because they come with a guarantee of
optimality. Here, we focus on a popular semidefinite relaxation of K-means
clustering which yields the same solution as the non-convex original
formulation for well segregated datasets. We report an unexpected finding: when
data contains (greater than zero-dimensional) manifolds, the SDP solution
captures such geometrical structures. Unlike traditional manifold embedding
techniques, our approach does not rely on manually defining a kernel but rather
enforces locality via a nonnegativity constraint. We thus call our approach
NOnnegative MAnifold Disentangling, or NOMAD. To build an intuitive
understanding of its manifold learning capabilities, we develop a theoretical
analysis of NOMAD on idealized datasets. While NOMAD is convex and the globally
optimal solution can be found by generic SDP solvers with polynomial time
complexity, they are too slow for modern datasets. To address this problem, we
analyze a non-convex heuristic and present a new, convex and yet efficient,
algorithm, based on the conditional gradient method. Our results render NOMAD a
versatile, understandable, and powerful tool for manifold learning.
| Mariano Tepper, Anirvan M. Sengupta, and Dmitri Chklovskii | null | 1706.06028 | null | null |
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned
Linear Systems | cs.IT cs.LG math.IT | The problem of estimating a random vector x from noisy linear measurements y
= A x + w with unknown parameters on the distributions of x and w, which must
also be learned, arises in a wide range of statistical learning and linear
inverse problems. We show that a computationally simple iterative
message-passing algorithm can provably obtain asymptotically consistent
estimates in a certain high-dimensional large-system limit (LSL) under very
general parameterizations. Previous message passing techniques have required
i.i.d. sub-Gaussian A matrices and often fail when the matrix is
ill-conditioned. The proposed algorithm, called adaptive vector approximate
message passing (Adaptive VAMP) with auto-tuning, applies to all
right-rotationally random A. Importantly, this class includes matrices with
arbitrarily poor conditioning. We show that the parameter estimates and mean
squared error (MSE) of x in each iteration converge to deterministic limits
that can be precisely predicted by a simple set of state evolution (SE)
equations. In addition, a simple testable condition is provided in which the
MSE matches the Bayes-optimal value predicted by the replica method. The paper
thus provides a computationally simple method with provable guarantees of
optimality and consistency over a large class of linear inverse problems.
| Alyson K. Fletcher, Mojtaba Sahraee-Ardakan, Philip Schniter, and
Sundeep Rangan | null | 1706.06054 | null | null |
Consistent feature attribution for tree ensembles | cs.AI cs.LG stat.ML | Note that a newer expanded version of this paper is now available at:
arXiv:1802.03888
It is critical in many applications to understand what features are important
for a model, and why individual predictions were made. For tree ensemble
methods these questions are usually answered by attributing importance values
to input features, either globally or for a single prediction. Here we show
that current feature attribution methods are inconsistent, which means changing
the model to rely more on a given feature can actually decrease the importance
assigned to that feature. To address this problem we develop fast exact
solutions for SHAP (SHapley Additive exPlanation) values, which were recently
shown to be the unique additive feature attribution method based on conditional
expectations that is both consistent and locally accurate. We integrate these
improvements into the latest version of XGBoost, demonstrate the
inconsistencies of current methods, and show how using SHAP values results in
significantly improved supervised clustering performance. Feature importance
values are a key part of understanding widely used models such as gradient
boosting trees and random forests, so improvements to them have broad practical
implications.
| Scott M. Lundberg and Su-In Lee | null | 1706.0606 | null | null |
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex
Sparse Learning in High Dimensions | stat.ML cs.LG math.OC | We propose a DC proximal Newton algorithm for solving nonconvex regularized
sparse learning problems in high dimensions. Our proposed algorithm integrates
the proximal Newton algorithm with multi-stage convex relaxation based on the
difference of convex (DC) programming, and enjoys both strong computational and
statistical guarantees. Specifically, by leveraging a sophisticated
characterization of sparse modeling structures/assumptions (i.e., local
restricted strong convexity and Hessian smoothness), we prove that within each
stage of convex relaxation, our proposed algorithm achieves (local) quadratic
convergence, and eventually obtains a sparse approximate local optimum with
optimal statistical properties after only a few convex relaxations. Numerical
experiments are provided to support our theory.
| Xingguo Li, Lin F. Yang, Jason Ge, Jarvis Haupt, Tong Zhang, Tuo Zhao | null | 1706.06066 | null | null |
Towards Deep Learning Models Resistant to Adversarial Attacks | stat.ML cs.LG cs.NE | Recent work has demonstrated that deep neural networks are vulnerable to
adversarial examples---inputs that are almost indistinguishable from natural
data and yet classified incorrectly by the network. In fact, some of the latest
findings suggest that the existence of adversarial attacks may be an inherent
weakness of deep learning models. To address this problem, we study the
adversarial robustness of neural networks through the lens of robust
optimization. This approach provides us with a broad and unifying view on much
of the prior work on this topic. Its principled nature also enables us to
identify methods for both training and attacking neural networks that are
reliable and, in a certain sense, universal. In particular, they specify a
concrete security guarantee that would protect against any adversary. These
methods let us train networks with significantly improved resistance to a wide
range of adversarial attacks. They also suggest the notion of security against
a first-order adversary as a natural and broad security guarantee. We believe
that robustness against such well-defined classes of adversaries is an
important stepping stone towards fully resistant deep learning models. Code and
pre-trained models are available at https://github.com/MadryLab/mnist_challenge
and https://github.com/MadryLab/cifar10_challenge.
| Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris
Tsipras, Adrian Vladu | null | 1706.06083 | null | null |
Multi-Label Annotation Aggregation in Crowdsourcing | cs.LG cs.AI cs.HC | As a means of human-based computation, crowdsourcing has been widely used to
annotate large-scale unlabeled datasets. One of the obvious challenges is how
to aggregate these possibly noisy labels provided by a set of heterogeneous
annotators. Another challenge stems from the difficulty in evaluating the
annotator reliability without even knowing the ground truth, which can be used
to build incentive mechanisms in crowdsourcing platforms. When each instance is
associated with many possible labels simultaneously, the problem becomes even
harder because of its combinatorial nature. In this paper, we present new
flexible Bayesian models and efficient inference algorithms for multi-label
annotation aggregation by taking both annotator reliability and label
dependency into account. Extensive experiments on real-world datasets confirm
that the proposed methods outperform other competitive alternatives, and the
model can recover the type of the annotators with high accuracy.
| Xuan Wei, Daniel Dajun Zeng, Junming Yin | null | 1706.0612 | null | null |
VAIN: Attentional Multi-agent Predictive Modeling | cs.LG cs.AI | Multi-agent predictive modeling is an essential step for understanding
physical, social and team-play systems. Recently, Interaction Networks (INs)
were proposed for the task of modeling multi-agent physical systems, INs scale
with the number of interactions in the system (typically quadratic or higher
order in the number of agents). In this paper we introduce VAIN, a novel
attentional architecture for multi-agent predictive modeling that scales
linearly with the number of agents. We show that VAIN is effective for
multi-agent predictive modeling. Our method is evaluated on tasks from
challenging multi-agent prediction domains: chess and soccer, and outperforms
competing multi-agent approaches.
| Yedid Hoshen | null | 1706.06122 | null | null |
Element-centric clustering comparison unifies overlaps and hierarchy | stat.ML cs.LG | Clustering is one of the most universal approaches for understanding complex
data. A pivotal aspect of clustering analysis is quantitatively comparing
clusterings; clustering comparison is the basis for many tasks such as
clustering evaluation, consensus clustering, and tracking the temporal
evolution of clusters. In particular, the extrinsic evaluation of clustering
methods requires comparing the uncovered clusterings to planted clusterings or
known metadata. Yet, as we demonstrate, existing clustering comparison measures
have critical biases which undermine their usefulness, and no measure
accommodates both overlapping and hierarchical clusterings. Here we unify the
comparison of disjoint, overlapping, and hierarchically structured clusterings
by proposing a new element-centric framework: elements are compared based on
the relationships induced by the cluster structure, as opposed to the
traditional cluster-centric philosophy. We demonstrate that, in contrast to
standard clustering similarity measures, our framework does not suffer from
critical biases and naturally provides unique insights into how the clusterings
differ. We illustrate the strengths of our framework by revealing new insights
into the organization of clusters in two applications: the improved
classification of schizophrenia based on the overlapping and hierarchical
community structure of fMRI brain networks, and the disentanglement of various
social homophily factors in Facebook social networks. The universality of
clustering suggests far-reaching impact of our framework throughout all areas
of science.
| Alexander J. Gates, Ian B. Wood, William P. Hetrick and Yong-Yeol Ahn | 10.1038/s41598-019-44892-y | 1706.06136 | null | null |
Unsure When to Stop? Ask Your Semantic Neighbors | cs.NE cs.LG stat.ML | In iterative supervised learning algorithms it is common to reach a point in
the search where no further induction seems to be possible with the available
data. If the search is continued beyond this point, the risk of overfitting
increases significantly. Following the recent developments in inductive
semantic stochastic methods, this paper studies the feasibility of using
information gathered from the semantic neighborhood to decide when to stop the
search. Two semantic stopping criteria are proposed and experimentally assessed
in Geometric Semantic Genetic Programming (GSGP) and in the Semantic Learning
Machine (SLM) algorithm (the equivalent algorithm for neural networks). The
experiments are performed on real-world high-dimensional regression datasets.
The results show that the proposed semantic stopping criteria are able to
detect stopping points that result in a competitive generalization for both
GSGP and SLM. This approach also yields computationally efficient algorithms as
it allows the evolution of neural networks in less than 3 seconds on average,
and of GP trees in at most 10 seconds. The usage of the proposed semantic
stopping criteria in conjunction with the computation of optimal
mutation/learning steps also results in small trees and neural networks.
| Ivo Gon\c{c}alves, Sara Silva, Carlos M. Fonseca, Mauro Castelli | 10.1145/3071178.3071328 | 1706.06195 | null | null |
meProp: Sparsified Back Propagation for Accelerated Deep Learning with
Reduced Overfitting | cs.LG cs.AI cs.CL cs.CV | We propose a simple yet effective technique for neural network learning. The
forward propagation is computed as usual. In back propagation, only a small
subset of the full gradient is computed to update the model parameters. The
gradient vectors are sparsified in such a way that only the top-$k$ elements
(in terms of magnitude) are kept. As a result, only $k$ rows or columns
(depending on the layout) of the weight matrix are modified, leading to a
linear reduction ($k$ divided by the vector dimension) in the computational
cost. Surprisingly, experimental results demonstrate that we can update only
1-4% of the weights at each back propagation pass. This does not result in a
larger number of training iterations. More interestingly, the accuracy of the
resulting models is actually improved rather than degraded, and a detailed
analysis is given. The code is available at https://github.com/lancopku/meProp
| Xu Sun, Xuancheng Ren, Shuming Ma, Houfeng Wang | null | 1706.06197 | null | null |
Dualing GANs | cs.LG cs.AI cs.CV stat.ML | Generative adversarial nets (GANs) are a promising technique for modeling a
distribution from samples. It is however well known that GAN training suffers
from instability due to the nature of its maximin formulation. In this paper,
we explore ways to tackle the instability problem by dualizing the
discriminator. We start from linear discriminators in which case conjugate
duality provides a mechanism to reformulate the saddle point objective into a
maximization problem, such that both the generator and the discriminator of
this 'dualing GAN' act in concert. We then demonstrate how to extend this
intuition to non-linear formulations. For GANs with linear discriminators our
approach is able to remove the instability in training, while for GANs with
nonlinear discriminators our approach provides an alternative to the commonly
used GAN training algorithm.
| Yujia Li, Alexander Schwing, Kuan-Chieh Wang, Richard Zemel | null | 1706.06216 | null | null |
Learning Graphical Models Using Multiplicative Weights | cs.LG cs.DS math.ST stat.TH | We give a simple, multiplicative-weight update algorithm for learning
undirected graphical models or Markov random fields (MRFs). The approach is
new, and for the well-studied case of Ising models or Boltzmann machines, we
obtain an algorithm that uses a nearly optimal number of samples and has
quadratic running time (up to logarithmic factors), subsuming and improving on
all prior work. Additionally, we give the first efficient algorithm for
learning Ising models over general alphabets.
Our main application is an algorithm for learning the structure of t-wise
MRFs with nearly-optimal sample complexity (up to polynomial losses in
necessary terms that depend on the weights) and running time that is
$n^{O(t)}$. In addition, given $n^{O(t)}$ samples, we can also learn the
parameters of the model and generate a hypothesis that is close in statistical
distance to the true MRF. All prior work runs in time $n^{\Omega(d)}$ for
graphs of bounded degree d and does not generate a hypothesis close in
statistical distance even for t=3. We observe that our runtime has the correct
dependence on n and t assuming the hardness of learning sparse parities with
noise.
Our algorithm--the Sparsitron-- is easy to implement (has only one parameter)
and holds in the on-line setting. Its analysis applies a regret bound from
Freund and Schapire's classic Hedge algorithm. It also gives the first solution
to the problem of learning sparse Generalized Linear Models (GLMs).
| Adam Klivans, Raghu Meka | null | 1706.06274 | null | null |
Short-Term Forecasting of Passenger Demand under On-Demand Ride
Services: A Spatio-Temporal Deep Learning Approach | cs.LG | Short-term passenger demand forecasting is of great importance to the
on-demand ride service platform, which can incentivize vacant cars moving from
over-supply regions to over-demand regions. The spatial dependences, temporal
dependences, and exogenous dependences need to be considered simultaneously,
however, which makes short-term passenger demand forecasting challenging. We
propose a novel deep learning (DL) approach, named the fusion convolutional
long short-term memory network (FCL-Net), to address these three dependences
within one end-to-end learning architecture. The model is stacked and fused by
multiple convolutional long short-term memory (LSTM) layers, standard LSTM
layers, and convolutional layers. The fusion of convolutional techniques and
the LSTM network enables the proposed DL approach to better capture the
spatio-temporal characteristics and correlations of explanatory variables. A
tailored spatially aggregated random forest is employed to rank the importance
of the explanatory variables. The ranking is then used for feature selection.
The proposed DL approach is applied to the short-term forecasting of passenger
demand under an on-demand ride service platform in Hangzhou, China.
Experimental results, validated on real-world data provided by DiDi Chuxing,
show that the FCL-Net achieves better predictive performance than traditional
approaches including both classical time-series prediction models and neural
network based algorithms (e.g., artificial neural network and LSTM). This paper
is one of the first DL studies to forecast the short-term passenger demand of
an on-demand ride service platform by examining the spatio-temporal
correlations.
| Jintao Ke, Hongyu Zheng, Hai Yang, Xiqun (Michael) Chen | 10.1016/j.trc.2017.10.016 | 1706.06279 | null | null |
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced
Learning | cs.CV cs.LG stat.ML | It is known that Boosting can be interpreted as a gradient descent technique
to minimize an underlying loss function. Specifically, the underlying loss
being minimized by the traditional AdaBoost is the exponential loss, which is
proved to be very sensitive to random noise/outliers. Therefore, several
Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to
improve the robustness of AdaBoost by replacing the exponential loss with some
designed robust loss functions. In this work, we present a new way to robustify
AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning
(SPL) into Boosting framework. Specifically, we design a new robust Boosting
algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented
by slightly modifying off-the-shelf Boosting packages. Extensive experiments
and a theoretical characterization are also carried out to illustrate the
merits of the proposed SPLBoost.
| Kaidong Wang, Yao Wang, Qian Zhao, Deyu Meng and Zongben Xu | null | 1706.06341 | null | null |
Frank-Wolfe Optimization for Symmetric-NMF under Simplicial Constraint | cs.LG math.OC stat.ML | Symmetric nonnegative matrix factorization has found abundant applications in
various domains by providing a symmetric low-rank decomposition of nonnegative
matrices. In this paper we propose a Frank-Wolfe (FW) solver to optimize the
symmetric nonnegative matrix factorization problem under a simplicial
constraint, which has recently been proposed for probabilistic clustering.
Compared with existing solutions, this algorithm is simple to implement, and
has no hyperparameters to be tuned. Building on the recent advances of FW
algorithms in nonconvex optimization, we prove an $O(1/\varepsilon^2)$
convergence rate to $\varepsilon$-approximate KKT points, via a tight bound
$\Theta(n^2)$ on the curvature constant, which matches the best known result in
unconstrained nonconvex setting using gradient methods. Numerical results
demonstrate the effectiveness of our algorithm. As a side contribution, we
construct a simple nonsmooth convex problem where the FW algorithm fails to
converge to the optimum. This result raises an interesting question about
necessary conditions of the success of the FW algorithm on convex problems.
| Han Zhao, Geoff Gordon | null | 1706.06348 | null | null |
An online sequence-to-sequence model for noisy speech recognition | cs.CL cs.LG stat.ML | Generative models have long been the dominant approach for speech
recognition. The success of these models however relies on the use of
sophisticated recipes and complicated machinery that is not easily accessible
to non-practitioners. Recent innovations in Deep Learning have given rise to an
alternative - discriminative models called Sequence-to-Sequence models, that
can almost match the accuracy of state of the art generative models. While
these models are easy to train as they can be trained end-to-end in a single
step, they have a practical limitation that they can only be used for offline
recognition. This is because the models require that the entirety of the input
sequence be available at the beginning of inference, an assumption that is not
valid for instantaneous speech recognition. To address this problem, online
sequence-to-sequence models were recently introduced. These models are able to
start producing outputs as data arrives, and the model feels confident enough
to output partial transcripts. These models, like sequence-to-sequence are
causal - the output produced by the model until any time, $t$, affects the
features that are computed subsequently. This makes the model inherently more
powerful than generative models that are unable to change features that are
computed from the data. This paper highlights two main contributions - an
improvement to online sequence-to-sequence model training, and its application
to noisy settings with mixed speech from two speakers.
| Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin
Swersky, Ilya Sutskever, Navdeep Jaitly | null | 1706.06428 | null | null |
On Pairwise Clustering with Side Information | cs.LG | Pairwise clustering, in general, partitions a set of items via a known
similarity function. In our treatment, clustering is modeled as a transductive
prediction problem. Thus rather than beginning with a known similarity
function, the function instead is hidden and the learner only receives a random
sample consisting of a subset of the pairwise similarities. An additional set
of pairwise side-information may be given to the learner, which then determines
the inductive bias of our algorithms. We measure performance not based on the
recovery of the hidden similarity function, but instead on how well we classify
each item. We give tight bounds on the number of misclassifications. We provide
two algorithms. The first algorithm SACA is a simple agglomerative clustering
algorithm which runs in near linear time, and which serves as a baseline for
our analyses. Whereas the second algorithm, RGCA, enables the incorporation of
side-information which may lead to improved bounds at the cost of a longer
running time.
| Stephen Pasteris, Fabio Vitale, Claudio Gentile, Mark Herbster | null | 1706.06474 | null | null |
Unperturbed: spectral analysis beyond Davis-Kahan | stat.ML cs.LG | Classical matrix perturbation results, such as Weyl's theorem for eigenvalues
and the Davis-Kahan theorem for eigenvectors, are general purpose. These
classical bounds are tight in the worst case, but in many settings sub-optimal
in the typical case. In this paper, we present perturbation bounds which
consider the nature of the perturbation and its interaction with the
unperturbed structure in order to obtain significant improvements over the
classical theory in many scenarios, such as when the perturbation is random. We
demonstrate the utility of these new results by analyzing perturbations in the
stochastic blockmodel where we derive much tighter bounds than provided by the
classical theory. We use our new perturbation theory to show that a very simple
and natural clustering algorithm -- whose analysis was difficult using the
classical tools -- nevertheless recovers the communities of the blockmodel
exactly even in very sparse graphs.
| Justin Eldridge, Mikhail Belkin, Yusu Wang | null | 1706.06516 | null | null |
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural
Networks for Acoustic Scene Classification | cs.SD cs.AI cs.LG | In Acoustic Scene Classification (ASC) two major approaches have been
followed . While one utilizes engineered features such as
mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features
that are the outcome of an optimization algorithm. I-vectors are the result of
a modeling technique that usually takes engineered features as input. It has
been shown that standard MFCCs extracted from monaural audio signals lead to
i-vectors that exhibit poor performance, especially on indoor acoustic scenes.
At the same time, Convolutional Neural Networks (CNNs) are well known for their
ability to learn features by optimizing their filters. They have been applied
on ASC and have shown promising results. In this paper, we first propose a
novel multi-channel i-vector extraction and scoring scheme for ASC, improving
their performance on indoor and outdoor scenes. Second, we propose a CNN
architecture that achieves promising ASC results. Further, we show that
i-vectors and CNNs capture complementary information from acoustic scenes.
Finally, we propose a hybrid system for ASC using multi-channel i-vectors and
CNNs by utilizing a score fusion technique. Using our method, we participated
in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st
rank among 49 submissions, substantially improving the previous state of the
art.
| Hamid Eghbal-zadeh, Bernhard Lehner, Matthias Dorfer, Gerhard Widmer | null | 1706.06525 | null | null |
A Divergence Bound for Hybrids of MCMC and Variational Inference and an
Application to Langevin Dynamics and SGVI | cs.LG stat.ML | Two popular classes of methods for approximate inference are Markov chain
Monte Carlo (MCMC) and variational inference. MCMC tends to be accurate if run
for a long enough time, while variational inference tends to give better
approximations at shorter time horizons. However, the amount of time needed for
MCMC to exceed the performance of variational methods can be quite high,
motivating more fine-grained tradeoffs. This paper derives a distribution over
variational parameters, designed to minimize a bound on the divergence between
the resulting marginal distribution and the target, and gives an example of how
to sample from this distribution in a way that interpolates between the
behavior of existing methods based on Langevin dynamics and stochastic gradient
variational inference (SGVI).
| Justin Domke | null | 1706.06529 | null | null |
Robust and Efficient Transfer Learning with Hidden-Parameter Markov
Decision Processes | stat.ML cs.AI cs.LG | We introduce a new formulation of the Hidden Parameter Markov Decision
Process (HiP-MDP), a framework for modeling families of related tasks using
low-dimensional latent embeddings. Our new framework correctly models the joint
uncertainty in the latent parameters and the state space. We also replace the
original Gaussian Process-based model with a Bayesian Neural Network, enabling
more scalable inference. Thus, we expand the scope of the HiP-MDP to
applications with higher dimensions and more complex dynamics.
| Taylor Killian, Samuel Daulton, George Konidaris, Finale Doshi-Velez | null | 1706.06544 | null | null |
Inference in Deep Networks in High Dimensions | cs.LG cs.IT math.IT stat.ML | Deep generative networks provide a powerful tool for modeling complex data in
a wide range of applications. In inverse problems that use these networks as
generative priors on data, one must often perform inference of the inputs of
the networks from the outputs. Inference is also required for sampling during
stochastic training on these generative models. This paper considers inference
in a deep stochastic neural network where the parameters (e.g., weights, biases
and activation functions) are known and the problem is to estimate the values
of the input and hidden units from the output. While several approximate
algorithms have been proposed for this task, there are few analytic tools that
can provide rigorous guarantees in the reconstruction error. This work presents
a novel and computationally tractable output-to-input inference method called
Multi-Layer Vector Approximate Message Passing (ML-VAMP). The proposed
algorithm, derived from expectation propagation, extends earlier AMP methods
that are known to achieve the replica predictions for optimality in simple
linear inverse problems. Our main contribution shows that the mean-squared
error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit
(LSL) where the numbers of layers is fixed and weight matrices are random and
orthogonally-invariant with dimensions that grow to infinity. ML-VAMP is thus a
principled method for output-to-input inference in deep networks with a
rigorous and precise performance achievability result in high dimensions.
| Alyson K. Fletcher and Sundeep Rangan | null | 1706.06549 | null | null |
Grounded Language Learning in a Simulated 3D World | cs.CL cs.LG stat.ML | We are increasingly surrounded by artificially intelligent technology that
takes decisions and executes actions on our behalf. This creates a pressing
need for general means to communicate with, instruct and guide artificial
agents, with human language the most compelling means for such communication.
To achieve this in a scalable fashion, agents must be able to relate language
to the world and to actions; that is, their understanding of language must be
grounded and embodied. However, learning grounded language is a notoriously
challenging problem in artificial intelligence research. Here we present an
agent that learns to interpret language in a simulated 3D environment where it
is rewarded for the successful execution of written instructions. Trained via a
combination of reinforcement and unsupervised learning, and beginning with
minimal prior knowledge, the agent learns to relate linguistic symbols to
emergent perceptual representations of its physical surroundings and to
pertinent sequences of actions. The agent's comprehension of language extends
beyond its prior experience, enabling it to apply familiar language to
unfamiliar situations and to interpret entirely novel instructions. Moreover,
the speed with which this agent learns new words increases as its semantic
knowledge grows. This facility for generalising and bootstrapping semantic
knowledge indicates the potential of the present approach for reconciling
ambiguous natural language with the complexity of the physical world.
| Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan
Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max
Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis,
Phil Blunsom | null | 1706.06551 | null | null |
A Unified Approach to Adaptive Regularization in Online and Stochastic
Optimization | cs.LG math.OC stat.ML | We describe a framework for deriving and analyzing online optimization
algorithms that incorporate adaptive, data-dependent regularization, also
termed preconditioning. Such algorithms have been proven useful in stochastic
optimization by reshaping the gradients according to the geometry of the data.
Our framework captures and unifies much of the existing literature on adaptive
online methods, including the AdaGrad and Online Newton Step algorithms as well
as their diagonal versions. As a result, we obtain new convergence proofs for
these algorithms that are substantially simpler than previous analyses. Our
framework also exposes the rationale for the different preconditioned updates
used in common stochastic optimization methods.
| Vineet Gupta, Tomer Koren, Yoram Singer | null | 1706.06569 | null | null |
Observational Learning by Reinforcement Learning | cs.LG cs.AI stat.ML | Observational learning is a type of learning that occurs as a function of
observing, retaining and possibly replicating or imitating the behaviour of
another agent. It is a core mechanism appearing in various instances of social
learning and has been found to be employed in several intelligent species,
including humans. In this paper, we investigate to what extent the explicit
modelling of other agents is necessary to achieve observational learning
through machine learning. Especially, we argue that observational learning can
emerge from pure Reinforcement Learning (RL), potentially coupled with memory.
Through simple scenarios, we demonstrate that an RL agent can leverage the
information provided by the observations of an other agent performing a task in
a shared environment. The other agent is only observed through the effect of
its actions on the environment and never explicitly modeled. Two key aspects
are borrowed from observational learning: i) the observer behaviour needs to
change as a result of viewing a 'teacher' (another agent) and ii) the observer
needs to be motivated somehow to engage in making use of the other agent's
behaviour. The later is naturally modeled by RL, by correlating the learning
agent's reward with the teacher agent's behaviour.
| Diana Borsa, Bilal Piot, R\'emi Munos and Olivier Pietquin | null | 1706.06617 | null | null |
Most Ligand-Based Classification Benchmarks Reward Memorization Rather
than Generalization | q-bio.QM cs.LG stat.ML | Undetected overfitting can occur when there are significant redundancies
between training and validation data. We describe AVE, a new measure of
training-validation redundancy for ligand-based classification problems that
accounts for the similarity amongst inactive molecules as well as active. We
investigated seven widely-used benchmarks for virtual screening and
classification, and show that the amount of AVE bias strongly correlates with
the performance of ligand-based predictive methods irrespective of the
predicted property, chemical fingerprint, similarity measure, or
previously-applied unbiasing techniques. Therefore, it may be that the
previously-reported performance of most ligand-based methods can be explained
by overfitting to benchmarks rather than good prospective accuracy.
| Izhar Wallach and Abraham Heifets | 10.1021/acs.jcim.7b00403 | 1706.06619 | null | null |
Analog CMOS-based Resistive Processing Unit for Deep Neural Network
Training | cs.ET cs.LG | Recently we have shown that an architecture based on resistive processing
unit (RPU) devices has potential to achieve significant acceleration in deep
neural network (DNN) training compared to today's software-based DNN
implementations running on CPU/GPU. However, currently available device
candidates based on non-volatile memory technologies do not satisfy all the
requirements to realize the RPU concept. Here, we propose an analog CMOS-based
RPU design (CMOS RPU) which can store and process data locally and can be
operated in a massively parallel manner. We analyze various properties of the
CMOS RPU to evaluate the functionality and feasibility for acceleration of DNN
training.
| Seyoung Kim, Tayfun Gokmen, Hyung-Min Lee and Wilfried E. Haensch | 10.1109/MWSCAS.2017.8052950 | 1706.0662 | null | null |
Policy Gradient Methods for Reinforcement Learning with Function
Approximation and Action-Dependent Baselines | cs.AI cs.LG | We show how an action-dependent baseline can be used by the policy gradient
theorem using function approximation, originally presented with
action-independent baselines by (Sutton et al. 2000).
| Philip S. Thomas and Emma Brunskill | null | 1706.06643 | null | null |
Crowdsourcing with Sparsely Interacting Workers | cs.LG | We consider estimation of worker skills from worker-task interaction data
(with unknown labels) for the single-coin crowd-sourcing binary classification
model in symmetric noise. We define the (worker) interaction graph whose nodes
are workers and an edge between two nodes indicates whether or not the two
workers participated in a common task. We show that skills are asymptotically
identifiable if and only if an appropriate limiting version of the interaction
graph is irreducible and has odd-cycles. We then formulate a weighted rank-one
optimization problem to estimate skills based on observations on an
irreducible, aperiodic interaction graph. We propose a gradient descent scheme
and show that for such interaction graphs estimates converge asymptotically to
the global minimum. We characterize noise robustness of the gradient scheme in
terms of spectral properties of signless Laplacians of the interaction graph.
We then demonstrate that a plug-in estimator based on the estimated skills
achieves state-of-art performance on a number of real-world datasets. Our
results have implications for rank-one matrix completion problem in that
gradient descent can provably recover $W \times W$ rank-one matrices based on
$W+1$ off-diagonal observations of a connected graph with a single odd-cycle.
| Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvari | null | 1706.0666 | null | null |
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed
Anomaly Detection via Cache Lookups | cs.DB cs.LG stat.CO stat.ML | Anomaly detection is one of the frequent and important subroutines deployed
in large-scale data processing systems. Even being a well-studied topic,
existing techniques for unsupervised anomaly detection require storing
significant amounts of data, which is prohibitive from memory and latency
perspective. In the big-data world existing methods fail to address the new set
of memory and latency constraints. In this paper, we propose ACE (Arrays of
(locality-sensitive) Count Estimators) algorithm that can be 60x faster than
the ELKI package~\cite{DBLP:conf/ssd/AchtertBKSZ09}, which has the fastest
implementation of the unsupervised anomaly detection algorithms. ACE algorithm
requires less than $4MB$ memory, to dynamically compress the full data
information into a set of count arrays. These tiny $4MB$ arrays of counts are
sufficient for unsupervised anomaly detection. At the core of the ACE
algorithm, there is a novel statistical estimator which is derived from the
sampling view of Locality Sensitive Hashing(LSH). This view is significantly
different and efficient than the widely popular view of LSH for near-neighbor
search. We show the superiority of ACE algorithm over 11 popular baselines on 3
benchmark datasets, including the KDD-Cup99 data which is the largest available
benchmark comprising of more than half a million entries with ground truth
anomaly labels.
| Chen Luo, Anshumali Shrivastava | null | 1706.06664 | null | null |
Graph-based Neural Multi-Document Summarization | cs.CL cs.LG | We propose a neural multi-document summarization (MDS) system that
incorporates sentence relation graphs. We employ a Graph Convolutional Network
(GCN) on the relation graphs, with sentence embeddings obtained from Recurrent
Neural Networks as input node features. Through multiple layer-wise
propagation, the GCN generates high-level hidden sentence features for salience
estimation. We then use a greedy heuristic to extract salient sentences while
avoiding redundancy. In our experiments on DUC 2004, we consider three types of
sentence relation graphs and demonstrate the advantage of combining sentence
relations in graphs with the representation power of deep neural networks. Our
model improves upon traditional graph-based extractive approaches and the
vanilla GRU sequence model with no graph, and it achieves competitive results
against other state-of-the-art multi-document summarization systems.
| Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan
Srinivasan and Dragomir Radev | null | 1706.06681 | null | null |
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge
Matches the Performance of Expert-developed QSAR/QSPR Models | stat.ML cs.AI cs.CE cs.CV cs.LG | In the last few years, we have seen the transformative impact of deep
learning in many applications, particularly in speech recognition and computer
vision. Inspired by Google's Inception-ResNet deep convolutional neural network
(CNN) for image classification, we have developed "Chemception", a deep CNN for
the prediction of chemical properties, using just the images of 2D drawings of
molecules. We develop Chemception without providing any additional explicit
chemistry knowledge, such as basic concepts like periodicity, or advanced
features like molecular descriptors and fingerprints. We then show how
Chemception can serve as a general-purpose neural network architecture for
predicting toxicity, activity, and solvation properties when trained on a
modest database of 600 to 40,000 compounds. When compared to multi-layer
perceptron (MLP) deep neural networks trained with ECFP fingerprints,
Chemception slightly outperforms in activity and solvation prediction and
slightly underperforms in toxicity prediction. Having matched the performance
of expert-developed QSAR/QSPR deep learning models, our work demonstrates the
plausibility of using deep neural networks to assist in computational chemistry
research, where the feature engineering process is performed primarily by a
deep learning algorithm.
| Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan O. Hodas,
Nathan Baker | null | 1706.06689 | null | null |
Neural-based Natural Language Generation in Dialogue using RNN
Encoder-Decoder with Semantic Aggregation | cs.CL cs.LG | Natural language generation (NLG) is an important component in spoken
dialogue systems. This paper presents a model called Encoder-Aggregator-Decoder
which is an extension of an Recurrent Neural Network based Encoder-Decoder
architecture. The proposed Semantic Aggregator consists of two components: an
Aligner and a Refiner. The Aligner is a conventional attention calculated over
the encoded input information, while the Refiner is another attention or gating
mechanism stacked over the attentive Aligner in order to further select and
aggregate the semantic elements. The proposed model can be jointly trained both
sentence planning and surface realization to produce natural language
utterances. The model was extensively assessed on four different NLG domains,
in which the experimental results showed that the proposed generator
consistently outperforms the previous methods on all the NLG domains.
| Van-Khanh Tran and Le-Minh Nguyen | null | 1706.06714 | null | null |
NPGLM: A Non-Parametric Method for Temporal Link Prediction | cs.LG cs.AI cs.SI | In this paper, we try to solve the problem of temporal link prediction in
information networks. This implies predicting the time it takes for a link to
appear in the future, given its features that have been extracted at the
current network snapshot. To this end, we introduce a probabilistic
non-parametric approach, called "Non-Parametric Generalized Linear Model"
(NP-GLM), which infers the hidden underlying probability distribution of the
link advent time given its features. We then present a learning algorithm for
NP-GLM and an inference method to answer time-related queries. Extensive
experiments conducted on both synthetic data and real-world Sina Weibo social
network demonstrate the effectiveness of NP-GLM in solving temporal link
prediction problem vis-a-vis competitive baselines.
| Sina Sajadmanesh, Jiawei Zhang, Hamid R. Rabiee | null | 1706.06783 | null | null |
Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep
Convolutional Neural Networks for Music Classification | cs.SD cs.LG cs.MM | Music tag words that describe music audio by text have different levels of
abstraction. Taking this issue into account, we propose a music classification
approach that aggregates multi-level and multi-scale features using pre-trained
feature extractors. In particular, the feature extractors are trained in
sample-level deep convolutional neural networks using raw waveforms. We show
that this approach achieves state-of-the-art results on several music
classification datasets.
| Jongpil Lee, Juhan Nam | null | 1706.0681 | null | null |
A giant with feet of clay: on the validity of the data that feed machine
learning in medicine | cs.LG stat.ML | This paper considers the use of Machine Learning (ML) in medicine by focusing
on the main problem that this computational approach has been aimed at solving
or at least minimizing: uncertainty. To this aim, we point out how uncertainty
is so ingrained in medicine that it biases also the representation of clinical
phenomena, that is the very input of ML models, thus undermining the clinical
significance of their output. Recognizing this can motivate both medical
doctors, in taking more responsibility in the development and use of these
decision aids, and the researchers, in pursuing different ways to assess the
value of these systems. In so doing, both designers and users could take this
intrinsic characteristic of medicine more seriously and consider alternative
approaches that do not "sweep uncertainty under the rug" within an objectivist
fiction, which everyone can come up by believing as true.
| Federico Cabitza, Davide Ciucci, Raffaele Rasoini | null | 1706.06838 | null | null |
Analysis of dropout learning regarded as ensemble learning | cs.LG stat.ML | Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of
layers, huge number of units, and connections. Therefore, overfitting is a
serious problem. To avoid this problem, dropout learning is proposed. Dropout
learning neglects some inputs and hidden units in the learning process with a
probability, p, and then, the neglected inputs and hidden units are combined
with the learned network to express the final output. We find that the process
of combining the neglected hidden units with the learned network can be
regarded as ensemble learning, so we analyze dropout learning from this point
of view.
| Kazuyuki Hara, Daisuke Saitoh, and Hayaru Shouno | 10.1007/978-3-319-44781-0_9 | 1706.06859 | null | null |
MEC: Memory-efficient Convolution for Deep Neural Network | cs.LG cs.CV | Convolution is a critical component in modern deep neural networks, thus
several algorithms for convolution have been developed. Direct convolution is
simple but suffers from poor performance. As an alternative, multiple indirect
methods have been proposed including im2col-based convolution, FFT-based
convolution, or Winograd-based algorithm. However, all these indirect methods
have high memory-overhead, which creates performance degradation and offers a
poor trade-off between performance and memory consumption. In this work, we
propose a memory-efficient convolution or MEC with compact lowering, which
reduces memory-overhead substantially and accelerates convolution process. MEC
lowers the input matrix in a simple yet efficient/compact way (i.e., much less
memory-overhead), and then executes multiple small matrix multiplications in
parallel to get convolution completed. Additionally, the reduced memory
footprint improves memory sub-system efficiency, improving performance. Our
experimental results show that MEC reduces memory consumption significantly
with good speedup on both mobile and server platforms, compared with other
indirect convolution algorithms.
| Minsik Cho, Daniel Brand | null | 1706.06873 | null | null |
Exact Learning of Juntas from Membership Queries | cs.LG | In this paper, we study adaptive and non-adaptive exact learning of Juntas
from membership queries. We use new techniques to find new bounds, narrow some
of the gaps between the lower bounds and upper bounds and find new
deterministic and randomized algorithms with small query and time complexities.
Some of the bounds are tight in the sense that finding better ones either
gives a breakthrough result in some long-standing combinatorial open problem or
needs a new technique that is beyond the existing ones.
| Nader H. Bshouty and Areej Costa | null | 1706.06934 | null | null |
Concept Drift and Anomaly Detection in Graph Streams | cs.LG stat.ML | Graph representations offer powerful and intuitive ways to describe data in a
multitude of application domains. Here, we consider stochastic processes
generating graphs and propose a methodology for detecting changes in
stationarity of such processes. The methodology is general and considers a
process generating attributed graphs with a variable number of vertices/edges,
without the need to assume one-to-one correspondence between vertices at
different time steps. The methodology acts by embedding every graph of the
stream into a vector domain, where a conventional multivariate change detection
procedure can be easily applied. We ground the soundness of our proposal by
proving several theoretical results. In addition, we provide a specific
implementation of the methodology and evaluate its effectiveness on several
detection problems involving attributed graphs representing biological
molecules and drawings. Experimental results are contrasted with respect to
suitable baseline methods, demonstrating the effectiveness of our approach.
| Daniele Zambon, Cesare Alippi, Lorenzo Livi | 10.1109/TNNLS.2018.2804443 | 1706.06941 | null | null |
Statistical Mechanics of Node-perturbation Learning with Noisy Baseline | stat.ML cs.LG | Node-perturbation learning is a type of statistical gradient descent
algorithm that can be applied to problems where the objective function is not
explicitly formulated, including reinforcement learning. It estimates the
gradient of an objective function by using the change in the object function in
response to the perturbation. The value of the objective function for an
unperturbed output is called a baseline. Cho et al. proposed node-perturbation
learning with a noisy baseline. In this paper, we report on building the
statistical mechanics of Cho's model and on deriving coupled differential
equations of order parameters that depict learning dynamics. We also show how
to derive the generalization error by solving the differential equations of
order parameters. On the basis of the results, we show that Cho's results are
also apply in general cases and show some general performances of Cho's model.
| Kazuyuki Hara, Kentaro Katahira, and Masato Okada | 10.7566/JPSJ.86.024002 | 1706.06953 | null | null |
The Theory is Predictive, but is it Complete? An Application to Human
Perception of Randomness | cs.LG cs.GT cs.SI stat.ML | When we test a theory using data, it is common to focus on correctness: do
the predictions of the theory match what we see in the data? But we also care
about completeness: how much of the predictable variation in the data is
captured by the theory? This question is difficult to answer, because in
general we do not know how much "predictable variation" there is in the
problem. In this paper, we consider approaches motivated by machine learning
algorithms as a means of constructing a benchmark for the best attainable level
of prediction.
We illustrate our methods on the task of predicting human-generated random
sequences. Relative to an atheoretical machine learning algorithm benchmark, we
find that existing behavioral models explain roughly 15 percent of the
predictable variation in this problem. This fraction is robust across several
variations on the problem. We also consider a version of this approach for
analyzing field data from domains in which human perception and generation of
randomness has been used as a conceptual framework; these include sequential
decision-making and repeated zero-sum games. In these domains, our framework
for testing the completeness of theories provides a way of assessing their
effectiveness over different contexts; we find that despite some differences,
the existing theories are fairly stable across our field domains in their
performance relative to the benchmark. Overall, our results indicate that (i)
there is a significant amount of structure in this problem that existing models
have yet to capture and (ii) there are rich domains in which machine learning
may provide a viable approach to testing completeness.
| Jon Kleinberg, Annie Liang, Sendhil Mullainathan | null | 1706.06974 | null | null |
Deep Interest Network for Click-Through Rate Prediction | stat.ML cs.LG | Click-through rate prediction is an essential task in industrial
applications, such as online advertising. Recently deep learning based models
have been proposed, which follow a similar Embedding\&MLP paradigm. In these
methods large scale sparse input features are first mapped into low dimensional
embedding vectors, and then transformed into fixed-length vectors in a
group-wise manner, finally concatenated together to fed into a multilayer
perceptron (MLP) to learn the nonlinear relations among features. In this way,
user features are compressed into a fixed-length representation vector, in
regardless of what candidate ads are. The use of fixed-length vector will be a
bottleneck, which brings difficulty for Embedding\&MLP methods to capture
user's diverse interests effectively from rich historical behaviors. In this
paper, we propose a novel model: Deep Interest Network (DIN) which tackles this
challenge by designing a local activation unit to adaptively learn the
representation of user interests from historical behaviors with respect to a
certain ad. This representation vector varies over different ads, improving the
expressive ability of model greatly. Besides, we develop two techniques:
mini-batch aware regularization and data adaptive activation function which can
help training industrial deep networks with hundreds of millions of parameters.
Experiments on two public datasets as well as an Alibaba real production
dataset with over 2 billion samples demonstrate the effectiveness of proposed
approaches, which achieve superior performance compared with state-of-the-art
methods. DIN now has been successfully deployed in the online display
advertising system in Alibaba, serving the main traffic.
| Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Ying Fan, Han Zhu, Xiao Ma,
Yanghui Yan, Junqi Jin, Han Li, Kun Gai | null | 1706.06978 | null | null |
Improved Optimization of Finite Sums with Minibatch Stochastic Variance
Reduced Proximal Iterations | math.OC cs.LG stat.ML | We present novel minibatch stochastic optimization methods for empirical risk
minimization problems, the methods efficiently leverage variance reduced
first-order and sub-sampled higher-order information to accelerate the
convergence speed. For quadratic objectives, we prove improved iteration
complexity over state-of-the-art under reasonable assumptions. We also provide
empirical evidence of the advantages of our method compared to existing
approaches in the literature.
| Jialei Wang, Tong Zhang | null | 1706.07001 | null | null |
Learning Efficient Point Cloud Generation for Dense 3D Object
Reconstruction | cs.CV cs.LG | Conventional methods of 3D object generative modeling learn volumetric
predictions using deep networks with 3D convolutional operations, which are
direct analogies to classical 2D ones. However, these methods are
computationally wasteful in attempt to predict 3D shapes, where information is
rich only on the surfaces. In this paper, we propose a novel 3D generative
modeling framework to efficiently generate object shapes in the form of dense
point clouds. We use 2D convolutional operations to predict the 3D structure
from multiple viewpoints and jointly apply geometric reasoning with 2D
projection optimization. We introduce the pseudo-renderer, a differentiable
module to approximate the true rendering operation, to synthesize novel depth
maps for optimization. Experimental results for single-image 3D object
reconstruction tasks show that we outperforms state-of-the-art methods in terms
of shape similarity and prediction density.
| Chen-Hsuan Lin, Chen Kong, Simon Lucey | null | 1706.07036 | null | null |
Constrained Bayesian Optimization with Noisy Experiments | stat.ML cs.LG stat.AP | Randomized experiments are the gold standard for evaluating the effects of
changes to real-world systems. Data in these tests may be difficult to collect
and outcomes may have high variance, resulting in potentially large measurement
error. Bayesian optimization is a promising technique for efficiently
optimizing multiple continuous parameters, but existing approaches degrade in
performance when the noise level is high, limiting its applicability to many
randomized experiments. We derive an expression for expected improvement under
greedy batch optimization with noisy observations and noisy constraints, and
develop a quasi-Monte Carlo approximation that allows it to be efficiently
optimized. Simulations with synthetic functions show that optimization
performance on noisy, constrained problems outperforms existing methods. We
further demonstrate the effectiveness of the method with two real-world
experiments conducted at Facebook: optimizing a ranking system, and optimizing
server compiler flags.
| Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy | null | 1706.07094 | null | null |
The energy landscape of a simple neural network | stat.ML cs.LG | We explore the energy landscape of a simple neural network. In particular, we
expand upon previous work demonstrating that the empirical complexity of fitted
neural networks is vastly less than a naive parameter count would suggest and
that this implicit regularization is actually beneficial for generalization
from fitted models.
| Anthony Collins Gamst and Alden Walker | null | 1706.07101 | null | null |
A hybrid supervised/unsupervised machine learning approach to solar
flare prediction | astro-ph.SR cs.LG | We introduce a hybrid approach to solar flare prediction, whereby a
supervised regularization method is used to realize feature importance and an
unsupervised clustering method is used to realize the binary flare/no-flare
decision. The approach is validated against NOAA SWPC data.
| Federico Benvenuto, Michele Piana, Cristina Campi, Anna Maria Massone | 10.3847/1538-4357/aaa23c | 1706.07103 | null | null |
"Parallel Training Considered Harmful?": Comparing series-parallel and
parallel feedforward network training | cs.SY cs.LG | Neural network models for dynamic systems can be trained either in parallel
or in series-parallel configurations. Influenced by early arguments, several
papers justify the choice of series-parallel rather than parallel configuration
claiming it has a lower computational cost, better stability properties during
training and provides more accurate results. Other published results, on the
other hand, defend parallel training as being more robust and capable of
yielding more accu- rate long-term predictions. The main contribution of this
paper is to present a study comparing both methods under the same unified
framework. We focus on three aspects: i) robustness of the estimation in the
presence of noise; ii) computational cost; and, iii) convergence. A unifying
mathematical framework and simulation studies show situations where each
training method provides better validation results, being parallel training
better in what is believed to be more realistic scenarios. An example using
measured data seems to reinforce such claim. We also show, with a novel
complexity analysis and numerical examples, that both methods have similar
computational cost, being series series-parallel training, however, more
amenable to parallelization. Some informal discussion about stability and
convergence properties is presented and explored in the examples.
| Ant\^onio H. Ribeiro and Luis A. Aguirre | 10.1016/j.neucom.2018.07.071 | 1706.07119 | null | null |
Generating Long-term Trajectories Using Deep Hierarchical Networks | cs.LG | We study the problem of modeling spatiotemporal trajectories over long time
horizons using expert demonstrations. For instance, in sports, agents often
choose action sequences with long-term goals in mind, such as achieving a
certain strategic position. Conventional policy learning approaches, such as
those based on Markov decision processes, generally fail at learning cohesive
long-term behavior in such high-dimensional state spaces, and are only
effective when myopic modeling lead to the desired behavior. The key difficulty
is that conventional approaches are "shallow" models that only learn a single
state-action policy. We instead propose a hierarchical policy class that
automatically reasons about both long-term and short-term goals, which we
instantiate as a hierarchical neural network. We showcase our approach in a
case study on learning to imitate demonstrated basketball trajectories, and
show that it generates significantly more realistic trajectories compared to
non-hierarchical baselines as judged by professional sports analysts.
| Stephan Zheng, Yisong Yue, Patrick Lucey | null | 1706.07138 | null | null |
Balanced Quantization: An Effective and Efficient Approach to Quantized
Neural Networks | cs.CV cs.LG | Quantized Neural Networks (QNNs), which use low bitwidth numbers for
representing parameters and performing computations, have been proposed to
reduce the computation complexity, storage size and memory usage. In QNNs,
parameters and activations are uniformly quantized, such that the
multiplications and additions can be accelerated by bitwise operations.
However, distributions of parameters in Neural Networks are often imbalanced,
such that the uniform quantization determined from extremal values may under
utilize available bitwidth. In this paper, we propose a novel quantization
method that can ensure the balance of distributions of quantized values. Our
method first recursively partitions the parameters by percentiles into balanced
bins, and then applies uniform quantization. We also introduce computationally
cheaper approximations of percentiles to reduce the computation overhead
introduced. Overall, our method improves the prediction accuracies of QNNs
without introducing extra computation during inference, has negligible impact
on training speed, and is applicable to both Convolutional Neural Networks and
Recurrent Neural Networks. Experiments on standard datasets including ImageNet
and Penn Treebank confirm the effectiveness of our method. On ImageNet, the
top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is
superior to the state-of-the-arts of QNNs.
| Shuchang Zhou, Yuzhi Wang, He Wen, Qinyao He and Yuheng Zou | null | 1706.07145 | null | null |
A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional
Visual Environment | cs.LG cs.AI q-bio.NC stat.ML | Animals (especially humans) have an amazing ability to learn new tasks
quickly, and switch between them flexibly. How brains support this ability is
largely unknown, both neuroscientifically and algorithmically. One reasonable
supposition is that modules drawing on an underlying general-purpose sensory
representation are dynamically allocated on a per-task basis. Recent results
from neuroscience and artificial intelligence suggest the role of the general
purpose visual representation may be played by a deep convolutional neural
network, and give some clues how task modules based on such a representation
might be discovered and constructed. In this work, we investigate module
architectures in an embodied two-dimensional touchscreen environment, in which
an agent's learning must occur via interactions with an environment that emits
images and rewards, and accepts touches as input. This environment is designed
to capture the physical structure of the task environments that are commonly
deployed in visual neuroscience and psychophysics. We show that in this
context, very simple changes in the nonlinear activations used by such a module
can significantly influence how fast it is at learning visual tasks and how
suitable it is for switching to new tasks.
| Kevin T. Feigelis and Daniel L. K. Yamins | null | 1706.07147 | null | null |
Curvature-aware Manifold Learning | cs.LG | Traditional manifold learning algorithms assumed that the embedded manifold
is globally or locally isometric to Euclidean space. Under this assumption,
they divided manifold into a set of overlapping local patches which are locally
isometric to linear subsets of Euclidean space. By analyzing the global or
local isometry assumptions it can be shown that the learnt manifold is a flat
manifold with zero Riemannian curvature tensor. In general, manifolds may not
satisfy these hypotheses. One major limitation of traditional manifold learning
is that it does not consider the curvature information of manifold. In order to
remove these limitations, we present our curvature-aware manifold learning
algorithm called CAML. The purpose of our algorithm is to break the local
isometry assumption and to reduce the dimension of the general manifold which
is not isometric to Euclidean space. Thus, our method adds the curvature
information to the process of manifold learning. The experiments have shown
that our method CAML is more stable than other manifold learning algorithms by
comparing the neighborhood preserving ratios.
| Yangyang Li | null | 1706.07167 | null | null |
RelNet: End-to-End Modeling of Entities & Relations | cs.CL cs.LG | We introduce RelNet: a new model for relational reasoning. RelNet is a memory
augmented neural network which models entities as abstract memory slots and is
equipped with an additional relational memory which models relations between
all memory pairs. The model thus builds an abstract knowledge graph on the
entities and relations present in a document which can then be used to answer
questions about the document. It is trained end-to-end: only supervision to the
model is in the form of correct answers to the questions. We test the model on
the 20 bAbI question-answering tasks with 10k examples per task and find that
it solves all the tasks with a mean error of 0.3%, achieving 0% error on 11 of
the 20 tasks.
| Trapit Bansal, Arvind Neelakantan, Andrew McCallum | null | 1706.07179 | null | null |
Compressive Statistical Learning with Random Feature Moments | stat.ML cs.IT cs.LG math.IT math.ST stat.TH | We describe a general framework -- compressive statistical learning -- for
resource-efficient large-scale learning: the training collection is compressed
in one pass into a low-dimensional sketch (a vector of random empirical
generalized moments) that captures the information relevant to the considered
learning task. A near-minimizer of the risk is computed from the sketch through
the solution of a nonlinear least squares problem. We investigate sufficient
sketch sizes to control the generalization error of this procedure. The
framework is illustrated on compressive PCA, compressive clustering, and
compressive Gaussian mixture Modeling with fixed known variance. The latter two
are further developed in a companion paper.
| R\'emi Gribonval (PANAMA, DANTE), Gilles Blanchard (DATASHAPE, LMO),
Nicolas Keriven (PANAMA, GIPSA-GAIA), Yann Traonmilin (PANAMA, IMB) | null | 1706.0718 | null | null |
Gated-Attention Architectures for Task-Oriented Language Grounding | cs.LG cs.AI cs.CL cs.RO | To perform tasks specified by natural language instructions, autonomous
agents need to extract semantically meaningful representations of language and
map it to visual elements and actions in the environment. This problem is
called task-oriented language grounding. We propose an end-to-end trainable
neural architecture for task-oriented language grounding in 3D environments
which assumes no prior linguistic or perceptual knowledge and requires only raw
pixels from the environment and the natural language instruction as input. The
proposed model combines the image and text representations using a
Gated-Attention mechanism and learns a policy to execute the natural language
instruction using standard reinforcement and imitation learning methods. We
show the effectiveness of the proposed model on unseen instructions as well as
unseen maps, both quantitatively and qualitatively. We also introduce a novel
environment based on a 3D game engine to simulate the challenges of
task-oriented language grounding over a rich set of instructions and
environment states.
| Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar
Pasumarthi, Dheeraj Rajagopal, Ruslan Salakhutdinov | null | 1706.0723 | null | null |
Jointly Learning Word Embeddings and Latent Topics | cs.CL cs.IR cs.LG | Word embedding models such as Skip-gram learn a vector-space representation
for each word, based on the local word collocation patterns that are observed
in a text corpus. Latent topic models, on the other hand, take a more global
view, looking at the word distributions across the corpus to assign a topic to
each word occurrence. These two paradigms are complementary in how they
represent the meaning of word occurrences. While some previous works have
already looked at using word embeddings for improving the quality of latent
topics, and conversely, at using latent topics for improving word embeddings,
such "two-step" methods cannot capture the mutual interaction between the two
paradigms. In this paper, we propose STE, a framework which can learn word
embeddings and latent topics in a unified manner. STE naturally obtains
topic-specific word embeddings, and thus addresses the issue of polysemy. At
the same time, it also learns the term distributions of the topics, and the
topic distributions of the documents. Our experimental results demonstrate that
the STE model can indeed generate useful topic-specific word embeddings and
coherent latent topics in an effective and efficient way.
| Bei Shi, Wai Lam, Shoaib Jameel, Steven Schockaert, Kwun Ping Lai | 10.1145/3077136.3080806 | 1706.07276 | null | null |
An approach to reachability analysis for feed-forward ReLU neural
networks | cs.AI cs.LG cs.LO | We study the reachability problem for systems implemented as feed-forward
neural networks whose activation function is implemented via ReLU functions. We
draw a correspondence between establishing whether some arbitrary output can
ever be outputed by a neural system and linear problems characterising a neural
system of interest. We present a methodology to solve cases of practical
interest by means of a state-of-the-art linear programs solver. We evaluate the
technique presented by discussing the experimental results obtained by
analysing reachability properties for a number of benchmarks in the literature.
| Alessio Lomuscio, Lalit Maganti | null | 1706.07351 | null | null |
Pixels to Graphs by Associative Embedding | cs.CV cs.LG | Graphs are a useful abstraction of image content. Not only can graphs
represent details about individual objects in a scene but they can capture the
interactions between pairs of objects. We present a method for training a
convolutional neural network such that it takes in an input image and produces
a full graph definition. This is done end-to-end in a single stage with the use
of associative embeddings. The network learns to simultaneously identify all of
the elements that make up a graph and piece them together. We benchmark on the
Visual Genome dataset, and demonstrate state-of-the-art performance on the
challenging task of scene graph generation.
| Alejandro Newell, Jia Deng | null | 1706.07365 | null | null |
Universal Sampling Rate Distortion | cs.IT cs.LG math.IT | We examine the coordinated and universal rate-efficient sampling of a subset
of correlated discrete memoryless sources followed by lossy compression of the
sampled sources. The goal is to reconstruct a predesignated subset of sources
within a specified level of distortion. The combined sampling mechanism and
rate distortion code are universal in that they are devised to perform robustly
without exact knowledge of the underlying joint probability distribution of the
sources. In Bayesian as well as nonBayesian settings, single-letter
characterizations are provided for the universal sampling rate distortion
function for fixed-set sampling, independent random sampling and memoryless
random sampling. It is illustrated how these sampling mechanisms are
successively better. Our achievability proofs bring forth new schemes for joint
source distribution-learning and lossy compression.
| Vinay Praneeth Boda and Prakash Narayan | null | 1706.07409 | null | null |
Deep Transfer Learning: A new deep learning glitch classification method
for advanced LIGO | gr-qc astro-ph.IM cs.CV cs.LG cs.NE | The exquisite sensitivity of the advanced LIGO detectors has enabled the
detection of multiple gravitational wave signals. The sophisticated design of
these detectors mitigates the effect of most types of noise. However, advanced
LIGO data streams are contaminated by numerous artifacts known as glitches:
non-Gaussian noise transients with complex morphologies. Given their high rate
of occurrence, glitches can lead to false coincident detections, obscure and
even mimic gravitational wave signals. Therefore, successfully characterizing
and removing glitches from advanced LIGO data is of utmost importance. Here, we
present the first application of Deep Transfer Learning for glitch
classification, showing that knowledge from deep learning algorithms trained
for real-world object recognition can be transferred for classifying glitches
in time-series based on their spectrogram images. Using the Gravity Spy
dataset, containing hand-labeled, multi-duration spectrograms obtained from
real LIGO data, we demonstrate that this method enables optimal use of very
deep convolutional neural networks for classification given small training
datasets, significantly reduces the time for training the networks, and
achieves state-of-the-art accuracy above 98.8%, with perfect precision-recall
on 8 out of 22 classes. Furthermore, new types of glitches can be classified
accurately given few labeled examples with this technique. Once trained via
transfer learning, we show that the convolutional neural networks can be
truncated and used as excellent feature extractors for unsupervised clustering
methods to identify new classes based on their morphology, without any labeled
examples. Therefore, this provides a new framework for dynamic glitch
classification for gravitational wave detectors, which are expected to
encounter new types of noise as they undergo gradual improvements to attain
design sensitivity.
| Daniel George, Hongyu Shen, E. A. Huerta | 10.1103/PhysRevD.97.101501 | 1706.07446 | null | null |
Revised Note on Learning Algorithms for Quadratic Assignment with Graph
Neural Networks | stat.ML cs.LG | Inverse problems correspond to a certain type of optimization problems
formulated over appropriate input distributions. Recently, there has been a
growing interest in understanding the computational hardness of these
optimization problems, not only in the worst case, but in an average-complexity
sense under this same input distribution.
In this revised note, we are interested in studying another aspect of
hardness, related to the ability to learn how to solve a problem by simply
observing a collection of previously solved instances. These 'planted
solutions' are used to supervise the training of an appropriate predictive
model that parametrizes a broad class of algorithms, with the hope that the
resulting model will provide good accuracy-complexity tradeoffs in the average
sense.
We illustrate this setup on the Quadratic Assignment Problem, a fundamental
problem in Network Science. We observe that data-driven models based on Graph
Neural Networks offer intriguingly good performance, even in regimes where
standard relaxation based techniques appear to suffer.
| Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna | null | 1706.0745 | null | null |
Personalization in Goal-Oriented Dialog | cs.CL cs.LG | The main goal of modeling human conversation is to create agents which can
interact with people in both open-ended and goal-oriented scenarios. End-to-end
trained neural dialog systems are an important line of research for such
generalized dialog models as they do not resort to any situation-specific
handcrafting of rules. However, incorporating personalization into such systems
is a largely unexplored topic as there are no existing corpora to facilitate
such work. In this paper, we present a new dataset of goal-oriented dialogs
which are influenced by speaker profiles attached to them. We analyze the
shortcomings of an existing end-to-end dialog system based on Memory Networks
and propose modifications to the architecture which enable personalization. We
also investigate personalization in dialog as a multi-task learning problem,
and show that a single model which shares features among various profiles
outperforms separate models for each profile.
| Chaitanya K. Joshi, Fei Mi and Boi Faltings | null | 1706.07503 | null | null |
Clustering with Noisy Queries | stat.ML cs.DS cs.IT cs.LG math.IT | In this paper, we initiate a rigorous theoretical study of clustering with
noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to
recover the true clustering by asking minimum number of pairwise queries to an
oracle. Oracle can answer queries of the form : "do elements $u$ and $v$ belong
to the same cluster?" -- the queries can be asked interactively (adaptive
queries), or non-adaptively up-front, but its answer can be erroneous with
probability $p$. In this paper, we provide the first information theoretic
lower bound on the number of queries for clustering with noisy oracle in both
situations. We design novel algorithms that closely match this query complexity
lower bound, even when the number of clusters is unknown. Moreover, we design
computationally efficient algorithms both for the adaptive and non-adaptive
settings. The problem captures/generalizes multiple application scenarios. It
is directly motivated by the growing body of work that use crowdsourcing for
{\em entity resolution}, a fundamental and challenging data mining task aimed
to identify all records in a database referring to the same entity. Here crowd
represents the noisy oracle, and the number of queries directly relates to the
cost of crowdsourcing. Another application comes from the problem of {\em sign
edge prediction} in social network, where social interactions can be both
positive and negative, and one must identify the sign of all pair-wise
interactions by querying a few pairs. Furthermore, clustering with noisy oracle
is intimately connected to correlation clustering, leading to improvement
therein. Finally, it introduces a new direction of study in the popular {\em
stochastic block model} where one has an incomplete stochastic block model
matrix to recover the clusters.
| Arya Mazumdar, Barna Saha | null | 1706.0751 | null | null |
Efficient Approximate Solutions to Mutual Information Based Global
Feature Selection | cs.LG stat.ML | Mutual Information (MI) is often used for feature selection when developing
classifier models. Estimating the MI for a subset of features is often
intractable. We demonstrate, that under the assumptions of conditional
independence, MI between a subset of features can be expressed as the
Conditional Mutual Information (CMI) between pairs of features. But selecting
features with the highest CMI turns out to be a hard combinatorial problem. In
this work, we have applied two unique global methods, Truncated Power Method
(TPower) and Low Rank Bilinear Approximation (LowRank), to solve the feature
selection problem. These algorithms provide very good approximations to the
NP-hard CMI based feature selection problem. We experimentally demonstrate the
effectiveness of these procedures across multiple datasets and compare them
with existing MI based global and iterative feature selection procedures.
| Hemanth Venkateswara, Prasanth Lade, Binbin Lin, Jieping Ye,
Sethuraman Panchanathan | null | 1706.07535 | null | null |
Toward Goal-Driven Neural Network Models for the Rodent
Whisker-Trigeminal System | q-bio.NC cs.LG | In large part, rodents see the world through their whiskers, a powerful
tactile sense enabled by a series of brain areas that form the
whisker-trigeminal system. Raw sensory data arrives in the form of mechanical
input to the exquisitely sensitive, actively-controllable whisker array, and is
processed through a sequence of neural circuits, eventually arriving in
cortical regions that communicate with decision-making and memory areas.
Although a long history of experimental studies has characterized many aspects
of these processing stages, the computational operations of the
whisker-trigeminal system remain largely unknown. In the present work, we take
a goal-driven deep neural network (DNN) approach to modeling these
computations. First, we construct a biophysically-realistic model of the rat
whisker array. We then generate a large dataset of whisker sweeps across a wide
variety of 3D objects in highly-varying poses, angles, and speeds. Next, we
train DNNs from several distinct architectural families to solve a shape
recognition task in this dataset. Each architectural family represents a
structurally-distinct hypothesis for processing in the whisker-trigeminal
system, corresponding to different ways in which spatial and temporal
information can be integrated. We find that most networks perform poorly on the
challenging shape recognition task, but that specific architectures from
several families can achieve reasonable performance levels. Finally, we show
that Representational Dissimilarity Matrices (RDMs), a tool for comparing
population codes between neural systems, can separate these higher-performing
networks with data of a type that could plausibly be collected in a
neurophysiological or imaging experiment. Our results are a proof-of-concept
that goal-driven DNN networks of the whisker-trigeminal system are potentially
within reach.
| Chengxu Zhuang, Jonas Kubilius, Mitra Hartmann, Daniel Yamins | null | 1706.07555 | null | null |
A-NICE-MC: Adversarial Training for MCMC | stat.ML cs.AI cs.LG | Existing Markov Chain Monte Carlo (MCMC) methods are either based on
general-purpose and domain-agnostic schemes which can lead to slow convergence,
or hand-crafting of problem-specific proposals by an expert. We propose
A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to
produce samples with desired properties. First, we propose an efficient
likelihood-free adversarial training method to train a Markov chain and mimic a
given data distribution. Then, we leverage flexible volume preserving flows to
obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to
train efficient Markov chains to sample from a prescribed posterior
distribution by iteratively improving the quality of both the model and the
samples. A-NICE-MC provides the first framework to automatically design
efficient domain-specific MCMC proposals. Empirical results demonstrate that
A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of
deep neural networks, and is able to significantly outperform competing methods
such as Hamiltonian Monte Carlo.
| Jiaming Song and Shengjia Zhao and Stefano Ermon | null | 1706.07561 | null | null |
How Much Data is Enough? A Statistical Approach with Case Study on
Longitudinal Driving Behavior | cs.LG | Big data has shown its uniquely powerful ability to reveal, model, and
understand driver behaviors. The amount of data affects the experiment cost and
conclusions in the analysis. Insufficient data may lead to inaccurate models
while excessive data waste resources. For projects that cost millions of
dollars, it is critical to determine the right amount of data needed. However,
how to decide the appropriate amount has not been fully studied in the realm of
driver behaviors. This paper systematically investigates this issue to estimate
how much naturalistic driving data (NDD) is needed for understanding driver
behaviors from a statistical point of view. A general assessment method is
proposed using a Gaussian kernel density estimation to catch the underlying
characteristics of driver behaviors. We then apply the Kullback-Liebler
divergence method to measure the similarity between density functions with
differing amounts of NDD. A max-minimum approach is used to compute the
appropriate amount of NDD. To validate our proposed method, we investigated the
car-following case using NDD collected from the University of Michigan Safety
Pilot Model Deployment (SPMD) program. We demonstrate that from a statistical
perspective, the proposed approach can provide an appropriate amount of NDD
capable of capturing most features of the normal car-following behavior, which
is consistent with the experiment settings in many literatures.
| Wenshuo Wang, Chang Liu, Ding Zhao | null | 1706.07637 | null | null |
A Variance Maximization Criterion for Active Learning | stat.ML cs.LG | Active learning aims to train a classifier as fast as possible with as few
labels as possible. The core element in virtually any active learning strategy
is the criterion that measures the usefulness of the unlabeled data based on
which new points to be labeled are picked. We propose a novel approach which we
refer to as maximizing variance for active learning or MVAL for short. MVAL
measures the value of unlabeled instances by evaluating the rate of change of
output variables caused by changes in the next sample to be queried and its
potential labelling. In a sense, this criterion measures how unstable the
classifier's output is for the unlabeled data points under perturbations of the
training data. MVAL maintains, what we refer to as, retraining information
matrices to keep track of these output scores and exploits two kinds of
variance to measure the informativeness and representativeness, respectively.
By fusing these variances, MVAL is able to select the instances which are both
informative and representative. We employ our technique both in combination
with logistic regression and support vector machines and demonstrate that MVAL
achieves state-of-the-art performance in experiments on a large number of
standard benchmark datasets.
| Yazhou Yang, Marco Loog | 10.1016/j.patcog.2018.01.017 | 1706.07642 | null | null |
Testing Piecewise Functions | cs.DS cs.LG | This work explores the query complexity of property testing for general
piecewise functions on the real line, in the active and passive property
testing settings. The results are proven under an abstract zero-measure
crossings condition, which has as special cases piecewise constant functions
and piecewise polynomial functions. We find that, in the active testing
setting, the query complexity of testing general piecewise functions is
independent of the number of pieces. We also identify the optimal dependence on
the number of pieces in the query complexity of passive testing in the special
case of piecewise constant functions.
| Steve Hanneke, Liu Yang | null | 1706.07669 | null | null |
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with
Automatic Multilayer Perceptron for Diabetes Prediction | cs.LG | With advanced data analytical techniques, efforts for more accurate decision
support systems for disease prediction are on rise. Surveys by World Health
Organization (WHO) indicate a great increase in number of diabetic patients and
related deaths each year. Early diagnosis of diabetes is a major concern among
researchers and practitioners. The paper presents an application of
\textit{Automatic Multilayer Perceptron }which\textit{ }is combined with an
outlier detection method \textit{Enhanced Class Outlier Detection using
distance based algorithm }to create a prediction framework named as Enhanced
Class Outlier with Automatic Multi layer Perceptron (ECO-AMLP). A series of
experiments are performed on publicly available Pima Indian Diabetes Dataset to
compare ECO-AMLP with other individual classifiers as well as ensemble based
methods. The outlier technique used in our framework gave better results as
compared to other pre-processing and classification techniques. Finally, the
results are compared with other state-of-the-art methods reported in literature
for diabetes prediction on PIDD and achieved accuracy of 88.7\% bests all other
reported studies.
| Maham Jahangir, Hammad Afzal, Mehreen Ahmed, Khawar Khurshid, Raheel
Nawaz | null | 1706.07679 | null | null |
Query Complexity of Clustering with Side Information | stat.ML cs.DS cs.IT cs.LG math.IT | Suppose, we are given a set of $n$ elements to be clustered into $k$
(unknown) clusters, and an oracle/expert labeler that can interactively answer
pair-wise queries of the form, "do two elements $u$ and $v$ belong to the same
cluster?". The goal is to recover the optimum clustering by asking the minimum
number of queries. In this paper, we initiate a rigorous theoretical study of
this basic problem of query complexity of interactive clustering, and provide
strong information theoretic lower bounds, as well as nearly matching upper
bounds. Most clustering problems come with a similarity matrix, which is used
by an automated process to cluster similar points together. Our main
contribution in this paper is to show the dramatic power of side information
aka similarity matrix on reducing the query complexity of clustering. A
similarity matrix represents noisy pair-wise relationships such as one computed
by some function on attributes of the elements. A natural noisy model is where
similarity values are drawn independently from some arbitrary probability
distribution $f_+$ when the underlying pair of elements belong to the same
cluster, and from some $f_-$ otherwise. We show that given such a similarity
matrix, the query complexity reduces drastically from $\Theta(nk)$ (no
similarity matrix) to $O(\frac{k^2\log{n}}{\cH^2(f_+\|f_-)})$ where $\cH^2$
denotes the squared Hellinger divergence. Moreover, this is also
information-theoretic optimal within an $O(\log{n})$ factor. Our algorithms are
all efficient, and parameter free, i.e., they work without any knowledge of $k,
f_+$ and $f_-$, and only depend logarithmically with $n$. Along the way, our
work also reveals intriguing connection to popular community detection models
such as the {\em stochastic block model}, significantly generalizes them, and
opens up many venues for interesting future research.
| Arya Mazumdar, Barna Saha | null | 1706.07719 | null | null |
Loom: Exploiting Weight and Activation Precisions to Accelerate
Convolutional Neural Networks | cs.DC cs.AR cs.LG | Loom (LM), a hardware inference accelerator for Convolutional Neural Networks
(CNNs) is presented. In LM every bit of data precision that can be saved
translates to proportional performance gains. Specifically, for convolutional
layers LM's execution time scales inversely proportionally with the precisions
of both weights and activations. For fully-connected layers LM's performance
scales inversely proportionally with the precision of the weights. LM targets
area- and bandwidth-constrained System-on-a-Chip designs such as those found on
mobile devices that cannot afford the multi-megabyte buffers that would be
needed to store each layer on-chip. Accordingly, given a data bandwidth budget,
LM boosts energy efficiency and performance over an equivalent bit-parallel
accelerator. For both weights and activations LM can exploit profile-derived
perlayer precisions. However, at runtime LM further trims activation precisions
at a much smaller than a layer granularity. Moreover, it can naturally exploit
weight precision variability at a smaller granularity than a layer. On average,
across several image classification CNNs and for a configuration that can
perform the equivalent of 128 16b x 16b multiply-accumulate operations per
cycle LM outperforms a state-of-the-art bit-parallel accelerator [1] by 4.38x
without any loss in accuracy while being 3.54x more energy efficient. LM can
trade-off accuracy for additional improvements in execution performance and
energy efficiency and compares favorably to an accelerator that targeted only
activation precisions. We also study 2- and 4-bit LM variants and find the the
2-bit per cycle variant is the most energy efficient.
| Sayeh Sharify, Alberto Delmas Lascorz, Kevin Siu, Patrick Judd,
Andreas Moshovos | null | 1706.07853 | null | null |
Preserving Intermediate Objectives: One Simple Trick to Improve Learning
for Hierarchical Models | cs.LG | Hierarchical models are utilized in a wide variety of problems which are
characterized by task hierarchies, where predictions on smaller subtasks are
useful for trying to predict a final task. Typically, neural networks are first
trained for the subtasks, and the predictions of these networks are
subsequently used as additional features when training a model and doing
inference for a final task. In this work, we focus on improving learning for
such hierarchical models and demonstrate our method on the task of speaker
trait prediction. Speaker trait prediction aims to computationally identify
which personality traits a speaker might be perceived to have, and has been of
great interest to both the Artificial Intelligence and Social Science
communities. Persuasiveness prediction in particular has been of interest, as
persuasive speakers have a large amount of influence on our thoughts, opinions
and beliefs. In this work, we examine how leveraging the relationship between
related speaker traits in a hierarchical structure can help improve our ability
to predict how persuasive a speaker is. We present a novel algorithm that
allows us to backpropagate through this hierarchy. This hierarchical model
achieves a 25% relative error reduction in classification accuracy over current
state-of-the art methods on the publicly available POM dataset.
| Abhilasha Ravichander, Shruti Rijhwani, Rajat Kulshreshtha, Chirag
Nagpal, Tadas Baltru\v{s}aitis, Louis-Philippe Morency | null | 1706.07867 | null | null |
Collaborative Deep Learning in Fixed Topology Networks | stat.ML cs.LG | There is significant recent interest to parallelize deep learning algorithms
in order to handle the enormous growth in data and model sizes. While most
advances focus on model parallelization and engaging multiple computing agents
via using a central parameter server, aspect of data parallelization along with
decentralized computation has not been explored sufficiently. In this context,
this paper presents a new consensus-based distributed SGD (CDSGD) (and its
momentum variant, CDMSGD) algorithm for collaborative deep learning over fixed
topology networks that enables data parallelization as well as decentralized
computation. Such a framework can be extremely useful for learning agents with
access to only local/private data in a communication constrained environment.
We analyze the convergence properties of the proposed algorithm with strongly
convex and nonconvex objective functions with fixed and diminishing step sizes
using concepts of Lyapunov function construction. We demonstrate the efficacy
of our algorithms in comparison with the baseline centralized SGD and the
recently proposed federated averaging algorithm (that also enables data
parallelism) based on benchmark datasets such as MNIST, CIFAR-10 and CIFAR-100.
| Zhanhong Jiang, Aditya Balu, Chinmay Hegde and Soumik Sarkar | null | 1706.0788 | null | null |
On Sampling Strategies for Neural Network-based Collaborative Filtering | cs.LG cs.IR cs.SI stat.ML | Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive computational costs, making it challenging to scale and improve
upon. In this paper, we propose a general neural network-based recommendation
framework, which subsumes several existing state-of-the-art recommendation
algorithms, and address the efficiency issue by investigating sampling
strategies in the stochastic gradient descent training for the framework. We
tackle this issue by first establishing a connection between the loss functions
and the user-item interaction bipartite graph, where the loss function terms
are defined on links while major computation burdens are located at nodes. We
call this type of loss functions "graph-based" loss functions, for which varied
mini-batch sampling strategies can have different computational costs. Based on
the insight, three novel sampling strategies are proposed, which can
significantly improve the training efficiency of the proposed framework (up to
$\times 30$ times speedup in our experiments), as well as improving the
recommendation performance. Theoretical analysis is also provided for both the
computational cost and the convergence. We believe the study of sampling
strategies have further implications on general graph-based loss functions, and
would also enable more research under the neural network-based recommendation
framework.
| Ting Chen, Yizhou Sun, Yue Shi, Liangjie Hong | null | 1706.07881 | null | null |
Reservoir Computing on the Hypersphere | cs.LG physics.data-an stat.ML | Reservoir Computing (RC) refers to a Recurrent Neural Networks (RNNs)
framework, frequently used for sequence learning and time series prediction.
The RC system consists of a random fixed-weight RNN (the input-hidden reservoir
layer) and a classifier (the hidden-output readout layer). Here we focus on the
sequence learning problem, and we explore a different approach to RC. More
specifically, we remove the non-linear neural activation function, and we
consider an orthogonal reservoir acting on normalized states on the unit
hypersphere. Surprisingly, our numerical results show that the system's memory
capacity exceeds the dimensionality of the reservoir, which is the upper bound
for the typical RC approach based on Echo State Networks (ESNs). We also show
how the proposed system can be applied to symmetric cryptography problems, and
we include a numerical implementation.
| M. Andrecut | 10.1142/S0129183117500954 | 1706.07896 | null | null |
Semi-supervised Text Categorization Using Recursive K-means Clustering | cs.LG cs.CL cs.IR | In this paper, we present a semi-supervised learning algorithm for
classification of text documents. A method of labeling unlabeled text documents
is presented. The presented method is based on the principle of divide and
conquer strategy. It uses recursive K-means algorithm for partitioning both
labeled and unlabeled data collection. The K-means algorithm is applied
recursively on each partition till a desired level partition is achieved such
that each partition contains labeled documents of a single class. Once the
desired clusters are obtained, the respective cluster centroids are considered
as representatives of the clusters and the nearest neighbor rule is used for
classifying an unknown text document. Series of experiments have been conducted
to bring out the superiority of the proposed model over other recent state of
the art models on 20Newsgroups dataset.
| Harsha S. Gowda, Mahamad Suhil, D.S. Guru, and Lavanya Narayana Raju | 10.1007/978-981-10-4859-3_20 | 1706.07913 | null | null |
A Variational EM Method for Pole-Zero Modeling of Speech with Mixed
Block Sparse and Gaussian Excitation | cs.SD cs.LG | The modeling of speech can be used for speech synthesis and speech
recognition. We present a speech analysis method based on pole-zero modeling of
speech with mixed block sparse and Gaussian excitation. By using a pole-zero
model, instead of the all-pole model, a better spectral fitting can be
expected. Moreover, motivated by the block sparse glottal flow excitation
during voiced speech and the white noise excitation for unvoiced speech, we
model the excitation sequence as a combination of block sparse signals and
white noise. A variational EM (VEM) method is proposed for estimating the
posterior PDFs of the block sparse residuals and point estimates of mod- elling
parameters within a sparse Bayesian learning framework. Compared to
conventional pole-zero and all-pole based methods, experimental results show
that the proposed method has lower spectral distortion and good performance in
reconstructing of the block sparse excitation.
| Liming Shi, Jesper Kj{\ae}r Nielsen, Jesper Rindom Jensen and Mads
Gr{\ae}sb{\o}ll Christensen | null | 1706.07927 | null | null |
Methods for Interpreting and Understanding Deep Neural Networks | cs.LG stat.ML | This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.
| Gr\'egoire Montavon, Wojciech Samek, Klaus-Robert M\"uller | 10.1016/j.dsp.2017.10.011 | 1706.07979 | null | null |
Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of
learning relational order via reinforcement learning procedure? | cs.AI cs.LG stat.ML | In this article, we extend the conventional framework of
convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
among abitrary number of time related input maps by constructing a layer of
multiplicative units, which capture the relations among inputs. In many cases,
more than two maps are strongly related, so it is wise to make multiplicative
unit learn relations among more input maps, in other words, to find the optimal
relational-order of each unit. In order to enable our machine to learn
relational order, we developed a reinforcement-learning method whose optimality
is proven to train the network.
| Zizhuang Wang | null | 1706.08001 | null | null |
Target contrastive pessimistic risk for robust domain adaptation | stat.ML cs.LG | In domain adaptation, classifiers with information from a source domain adapt
to generalize to a target domain. However, an adaptive classifier can perform
worse than a non-adaptive classifier due to invalid assumptions, increased
sensitivity to estimation errors or model misspecification. Our goal is to
develop a domain-adaptive classifier that is robust in the sense that it does
not rely on restrictive assumptions on how the source and target domains relate
to each other and that it does not perform worse than the non-adaptive
classifier. We formulate a conservative parameter estimator that only deviates
from the source classifier when a lower risk is guaranteed for all possible
labellings of the given target samples. We derive the classical least-squares
and discriminant analysis cases and show that these perform on par with
state-of-the-art domain adaptive classifiers in sample selection bias settings,
while outperforming them in more general domain adaptation settings.
| Wouter M. Kouw, Marco Loog | 10.1016/j.patrec.2021.05.005 | 1706.08082 | null | null |
Compressed Factorization: Fast and Accurate Low-Rank Factorization of
Compressively-Sensed Data | cs.LG cs.AI stat.ML | What learning algorithms can be run directly on compressively-sensed data? In
this work, we consider the question of accurately and efficiently computing
low-rank matrix or tensor factorizations given data compressed via random
projections. We examine the approach of first performing factorization in the
compressed domain, and then reconstructing the original high-dimensional
factors from the recovered (compressed) factors. In both the matrix and tensor
settings, we establish conditions under which this natural approach will
provably recover the original factors. While it is well-known that random
projections preserve a number of geometric properties of a dataset, our work
can be viewed as showing that they can also preserve certain solutions of
non-convex, NP-Hard problems like non-negative matrix factorization. We support
these theoretical results with experiments on synthetic data and demonstrate
the practical applicability of compressed factorization on real-world gene
expression and EEG time series datasets.
| Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant | null | 1706.08146 | null | null |
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context | cs.CL cs.LG | Word embeddings, which represent a word as a point in a vector space, have
become ubiquitous to several NLP tasks. A recent line of work uses bilingual
(two languages) corpora to learn a different vector for each sense of a word,
by exploiting crosslingual signals to aid sense identification. We present a
multi-view Bayesian non-parametric algorithm which improves multi-sense word
embeddings by (a) using multilingual (i.e., more than two languages) corpora to
significantly improve sense embeddings beyond what one achieves with bilingual
information, and (b) uses a principled approach to learn a variable number of
senses per word, in a data-driven manner. Ours is the first approach with the
ability to leverage multilingual corpora efficiently for multi-sense
representation learning. Experiments show that multilingual training
significantly improves performance over monolingual and bilingual training, by
allowing us to combine different parallel corpora to leverage multilingual
context. Multilingual training yields comparable performance to a state of the
art mono-lingual model trained on five times more training data.
| Shyam Upadhyay and Kai-Wei Chang and Matt Taddy and Adam Kalai and
James Zou | null | 1706.0816 | null | null |
An Effective Way to Improve YouTube-8M Classification Accuracy in Google
Cloud Platform | stat.ML cs.LG | Large-scale datasets have played a significant role in progress of neural
network and deep learning areas. YouTube-8M is such a benchmark dataset for
general multi-label video classification. It was created from over 7 million
YouTube videos (450,000 hours of video) and includes video labels from a
vocabulary of 4716 classes (3.4 labels/video on average). It also comes with
pre-extracted audio & visual features from every second of video (3.2 billion
feature vectors in total). Google cloud recently released the datasets and
organized 'Google Cloud & YouTube-8M Video Understanding Challenge' on Kaggle.
Competitors are challenged to develop classification algorithms that assign
video-level labels using the new and improved Youtube-8M V2 dataset. Inspired
by the competition, we started exploration of audio understanding and
classification using deep learning algorithms and ensemble methods. We built
several baseline predictions according to the benchmark paper and public github
tensorflow code. Furthermore, we improved global prediction accuracy (GAP) from
base level 77% to 80.7% through approaches of ensemble.
| Zhenzhen Zhong, Shujiao Huang, Cheng Zhan, Licheng Zhang, Zhiwei Xiao,
Chang-Chun Wang, Pei Yang | null | 1706.08217 | null | null |
Do GANs actually learn the distribution? An empirical study | cs.LG | Do GANS (Generative Adversarial Nets) actually learn the target distribution?
The foundational paper of (Goodfellow et al 2014) suggested they do, if they
were given sufficiently large deep nets, sample size, and computation time. A
recent theoretical analysis in Arora et al (to appear at ICML 2017) raised
doubts whether the same holds when discriminator has finite size. It showed
that the training objective can approach its optimum value even if the
generated distribution has very low support ---in other words, the training
objective is unable to prevent mode collapse. The current note reports
experiments suggesting that such problems are not merely theoretical. It
presents empirical evidence that well-known GANs approaches do learn
distributions of fairly low support, and thus presumably are not learning the
target distribution. The main technical contribution is a new proposed test,
based upon the famous birthday paradox, for estimating the support size of the
generated distribution.
| Sanjeev Arora, Yi Zhang | null | 1706.08224 | null | null |
Multi-Label Learning with Label Enhancement | cs.LG cs.CV | The task of multi-label learning is to predict a set of relevant labels for
the unseen instance. Traditional multi-label learning algorithms treat each
class label as a logical indicator of whether the corresponding label is
relevant or irrelevant to the instance, i.e., +1 represents relevant to the
instance and -1 represents irrelevant to the instance. Such label represented
by -1 or +1 is called logical label. Logical label cannot reflect different
label importance. However, for real-world multi-label learning problems, the
importance of each possible label is generally different. For the real
applications, it is difficult to obtain the label importance information
directly. Thus we need a method to reconstruct the essential label importance
from the logical multilabel data. To solve this problem, we assume that each
multi-label instance is described by a vector of latent real-valued labels,
which can reflect the importance of the corresponding labels. Such label is
called numerical label. The process of reconstructing the numerical labels from
the logical multi-label data via utilizing the logical label information and
the topological structure in the feature space is called Label Enhancement. In
this paper, we propose a novel multi-label learning framework called LEMLL,
i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the
numerical labels and label enhancement into a unified framework. Extensive
comparative studies validate that the performance of multi-label learning can
be improved significantly with label enhancement and LEMLL can effectively
reconstruct latent label importance information from logical multi-label data.
| Ruifeng Shao, Ning Xu, Xin Geng | null | 1706.08323 | null | null |
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