pwc-archive/papers-with-abstracts huggingface dataset:
Two Methods For Wild Variational Inference
Authors: Qiang Liu, Yihao Feng
Date: 2016-11-30 00:00:00
Tasks: Variational Inference, Vocal Bursts Valence Prediction

Variational inference provides a powerful tool for approximate probabilistic
in- ference on complex, structured models. Typical variational inference
methods, however, require to use inference networks with computationally
tractable proba- bility density functions. This largely limits the design and
implementation of vari- ational inference methods. We consider wild variational
inference methods that do not require tractable density functions on the
inference networks, and hence can be applied in more challenging cases. As an
example of application, we treat stochastic gradient Langevin dynamics (SGLD)
as an inference network, and use our methods to automatically adjust the step
sizes of SGLD, yielding significant improvement over the hand-designed step
size schemes
Adaptive Sampling Strategies for Stochastic Optimization
Authors: Raghu Bollapragada, Richard Byrd, Jorge Nocedal
Date: 2017-10-30 00:00:00
Tasks: regression, Stochastic Optimization

In this paper, we propose a stochastic optimization method that adaptively
controls the sample size used in the computation of gradient approximations.
Unlike other variance reduction techniques that either require additional
storage or the regular computation of full gradients, the proposed method
reduces variance by increasing the sample size as needed. The decision to
increase the sample size is governed by an inner product test that ensures that
search directions are descent directions with high probability. We show that
the inner product test improves upon the well known norm test, and can be used
as a basis for an algorithm that is globally convergent on nonconvex functions
and enjoys a global linear rate of convergence on strongly convex functions.
Numerical experiments on logistic regression problems illustrate the
performance of the algorithm.
Log-DenseNet: How to Sparsify a DenseNet
Authors: Hanzhang Hu, Debadeepta Dey, Allison Del Giorno, Martial Hebert, J. Andrew Bagnell
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Semantic Segmentation

Skip connections are increasingly utilized by deep neural networks to improve
accuracy and cost-efficiency. In particular, the recent DenseNet is efficient
in computation and parameters, and achieves state-of-the-art predictions by
directly connecting each feature layer to all previous ones. However,
DenseNet's extreme connectivity pattern may hinder its scalability to high
depths, and in applications like fully convolutional networks, full DenseNet
connections are prohibitively expensive. This work first experimentally shows
that one key advantage of skip connections is to have short distances among
feature layers during backpropagation. Specifically, using a fixed number of
skip connections, the connection patterns with shorter backpropagation distance
among layers have more accurate predictions. Following this insight, we propose
a connection template, Log-DenseNet, which, in comparison to DenseNet, only
slightly increases the backpropagation distances among layers from 1 to ($1 +
\log_2 L$), but uses only $L\log_2 L$ total connections instead of $O(L^2)$.
Hence, Log-DenseNets are easier than DenseNets to implement and to scale. We
demonstrate the effectiveness of our design principle by showing better
performance than DenseNets on tabula rasa semantic segmentation, and
competitive results on visual recognition.
Approximation Algorithms for $\ell_0$-Low Rank Approximation
Authors: Karl Bringmann, Pavel Kolev, David P. Woodruff
Date: 2017-10-30 00:00:00

We study the $\ell_0$-Low Rank Approximation Problem, where the goal is,
given an $m \times n$ matrix $A$, to output a rank-$k$ matrix $A'$ for which
$\|A'-A\|_0$ is minimized. Here, for a matrix $B$, $\|B\|_0$ denotes the number
of its non-zero entries. This NP-hard variant of low rank approximation is
natural for problems with no underlying metric, and its goal is to minimize the
number of disagreeing data positions. We provide approximation algorithms which
significantly improve the running time and approximation factor of previous
work. For $k > 1$, we show how to find, in poly$(mn)$ time for every $k$, a
rank $O(k \log(n/k))$ matrix $A'$ for which $\|A'-A\|_0 \leq O(k^2 \log(n/k))
\mathrm{OPT}$. To the best of our knowledge, this is the first algorithm with
provable guarantees for the $\ell_0$-Low Rank Approximation Problem for $k >
1$, even for bicriteria algorithms. For the well-studied case when $k = 1$, we
give a $(2+\epsilon)$-approximation in {\it sublinear time}, which is
impossible for other variants of low rank approximation such as for the
Frobenius norm. We strengthen this for the well-studied case of binary matrices
to obtain a $(1+O(\psi))$-approximation in sublinear time, where $\psi =
\mathrm{OPT}/\lVert A\rVert_0$. For small $\psi$, our approximation factor is
$1+o(1)$.
Learning to Draw Samples with Amortized Stein Variational Gradient Descent
Authors: Yihao Feng, Dilin Wang, Qiang Liu
Date: 2017-07-20 00:00:00
Tasks: Bayesian Inference

We propose a simple algorithm to train stochastic neural networks to draw
samples from given target distributions for probabilistic inference. Our method
is based on iteratively adjusting the neural network parameters so that the
output changes along a Stein variational gradient direction (Liu & Wang, 2016)
that maximally decreases the KL divergence with the target distribution. Our
method works for any target distribution specified by their unnormalized
density function, and can train any black-box architectures that are
differentiable in terms of the parameters we want to adapt. We demonstrate our
method with a number of applications, including variational autoencoder (VAE)
with expressive encoders to model complex latent space structures, and
hyper-parameter learning of MCMC samplers that allows Bayesian inference to
adaptively improve itself when seeing more data.
Learning Robust Rewards with Adversarial Inverse Reinforcement Learning
Authors: Justin Fu, Katie Luo, Sergey Levine
Date: 2017-10-30 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning provides a powerful and general framework for decision
making and control, but its application in practice is often hindered by the
need for extensive feature and reward engineering. Deep reinforcement learning
methods can remove the need for explicit engineering of policy or value
features, but still require a manually specified reward function. Inverse
reinforcement learning holds the promise of automatic reward acquisition, but
has proven exceptionally difficult to apply to large, high-dimensional problems
with unknown dynamics. In this work, we propose adverserial inverse
reinforcement learning (AIRL), a practical and scalable inverse reinforcement
learning algorithm based on an adversarial reward learning formulation. We
demonstrate that AIRL is able to recover reward functions that are robust to
changes in dynamics, enabling us to learn policies even under significant
variation in the environment seen during training. Our experiments show that
AIRL greatly outperforms prior methods in these transfer settings.
Theoretical properties of the global optimizer of two layer neural network
Authors: Digvijay Boob, Guanghui Lan
Date: 2017-10-30 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, we study the problem of optimizing a two-layer artificial
neural network that best fits a training dataset. We look at this problem in
the setting where the number of parameters is greater than the number of
sampled points. We show that for a wide class of differentiable activation
functions (this class involves "almost" all functions which are not piecewise
linear), we have that first-order optimal solutions satisfy global optimality
provided the hidden layer is non-singular. Our results are easily extended to
hidden layers given by a flat matrix from that of a square matrix. Results are
applicable even if network has more than one hidden layer provided all hidden
layers satisfy non-singularity, all activations are from the given "good" class
of differentiable functions and optimization is only with respect to the last
hidden layer. We also study the smoothness properties of the objective function
and show that it is actually Lipschitz smooth, i.e., its gradients do not
change sharply. We use smoothness properties to guarantee asymptotic
convergence of O(1/number of iterations) to a first-order optimal solution. We
also show that our algorithm will maintain non-singularity of hidden layer for
any finite number of iterations.
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Authors: Christoph Wehmeyer, Frank Noé
Date: 2017-10-30 00:00:00
Tasks: Dimensionality Reduction

Inspired by the success of deep learning techniques in the physical and
chemical sciences, we apply a modification of an autoencoder type deep neural
network to the task of dimension reduction of molecular dynamics data. We can
show that our time-lagged autoencoder reliably finds low-dimensional embeddings
for high-dimensional feature spaces which capture the slow dynamics of the
underlying stochastic processes - beyond the capabilities of linear dimension
reduction techniques.
Federated Learning: Strategies for Improving Communication Efficiency
Authors: Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, Dave Bacon
Date: 2016-10-18 00:00:00
Proceeding: ICLR 2018 1
Tasks: Federated Learning, Quantization

Federated Learning is a machine learning setting where the goal is to train a
high-quality centralized model while training data remains distributed over a
large number of clients each with unreliable and relatively slow network
connections. We consider learning algorithms for this setting where on each
round, each client independently computes an update to the current model based
on its local data, and communicates this update to a central server, where the
client-side updates are aggregated to compute a new global model. The typical
clients in this setting are mobile phones, and communication efficiency is of
the utmost importance.
  In this paper, we propose two ways to reduce the uplink communication costs:
structured updates, where we directly learn an update from a restricted space
parametrized using a smaller number of variables, e.g. either low-rank or a
random mask; and sketched updates, where we learn a full model update and then
compress it using a combination of quantization, random rotations, and
subsampling before sending it to the server. Experiments on both convolutional
and recurrent networks show that the proposed methods can reduce the
communication cost by two orders of magnitude.
Deep Learning for Video Game Playing
Authors: Niels Justesen, Philip Bontrager, Julian Togelius, Sebastian Risi
Date: 2017-08-25 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Real-Time Strategy Games

In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards.
Testing Ising Models
Authors: Constantinos Daskalakis, Nishanth Dikkala, Gautam Kamath
Date: 2016-12-09 00:00:00

Given samples from an unknown multivariate distribution $p$, is it possible to distinguish whether $p$ is the product of its marginals versus $p$ being far from every product distribution? Similarly, is it possible to distinguish whether $p$ equals a given distribution $q$ versus $p$ and $q$ being far from each other? These problems of testing independence and goodness-of-fit have received enormous attention in statistics, information theory, and theoretical computer science, with sample-optimal algorithms known in several interesting regimes of parameters. Unfortunately, it has also been understood that these problems become intractable in large dimensions, necessitating exponential sample complexity. Motivated by the exponential lower bounds for general distributions as well as the ubiquity of Markov Random Fields (MRFs) in the modeling of high-dimensional distributions, we initiate the study of distribution testing on structured multivariate distributions, and in particular the prototypical example of MRFs: the Ising Model. We demonstrate that, in this structured setting, we can avoid the curse of dimensionality, obtaining sample and time efficient testers for independence and goodness-of-fit. One of the key technical challenges we face along the way is bounding the variance of functions of the Ising model.
Online Learning of Combinatorial Objects via Extended Formulation
Authors: Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan
Date: 2016-09-17 00:00:00

The standard techniques for online learning of combinatorial objects perform
multiplicative updates followed by projections into the convex hull of all the
objects. However, this methodology can be expensive if the convex hull contains
many facets. For example, the convex hull of $n$-symbol Huffman trees is known
to have exponentially many facets (Maurras et al., 2010). We get around this
difficulty by exploiting extended formulations (Kaibel, 2011), which encode the
polytope of combinatorial objects in a higher dimensional "extended" space with
only polynomially many facets. We develop a general framework for converting
extended formulations into efficient online algorithms with good relative loss
bounds. We present applications of our framework to online learning of Huffman
trees and permutations. The regret bounds of the resulting algorithms are
within a factor of $O(\sqrt{\log(n)})$ of the state-of-the-art specialized
algorithms for permutations, and depending on the loss regimes, improve on or
match the state-of-the-art for Huffman trees. Our method is general and can be
applied to other combinatorial objects.
Optimal Threshold Design for Quanta Image Sensor
Authors: Omar A. Elgendy, Stanley H. Chan
Date: 2017-04-12 00:00:00

Quanta Image Sensor (QIS) is a binary imaging device envisioned to be the
next generation image sensor after CCD and CMOS. Equipped with a massive number
of single photon detectors, the sensor has a threshold $q$ above which the
number of arriving photons will trigger a binary response "1", or "0"
otherwise. Existing methods in the device literature typically assume that
$q=1$ uniformly. We argue that a spatially varying threshold can significantly
improve the signal-to-noise ratio of the reconstructed image. In this paper, we
present an optimal threshold design framework. We make two contributions.
First, we derive a set of oracle results to theoretically inform the maximally
achievable performance. We show that the oracle threshold should match exactly
with the underlying pixel intensity. Second, we show that around the oracle
threshold there exists a set of thresholds that give asymptotically unbiased
reconstructions. The asymptotic unbiasedness has a phase transition behavior
which allows us to develop a practical threshold update scheme using a
bisection method. Experimentally, the new threshold design method achieves
better rate of convergence than existing methods.
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Authors: Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt
Date: 2017-10-30 00:00:00
Tasks: Recommendation Systems

Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility.
Group-level Emotion Recognition using Transfer Learning from Face Identification
Authors: Alexandr G. Rassadin, Alexey S. Gruzdev, Andrey V. Savchenko
Date: 2017-09-06 00:00:00
Tasks: Emotion Recognition, Face Identification, Transfer Learning

In this paper, we describe our algorithmic approach, which was used for
submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017)
group-level emotion recognition sub-challenge. We extracted feature vectors of
detected faces using the Convolutional Neural Network trained for face
identification task, rather than traditional pre-training on emotion
recognition problems. In the final pipeline an ensemble of Random Forest
classifiers was learned to predict emotion score using available training set.
In case when the faces have not been detected, one member of our ensemble
extracts features from the whole image. During our experimental study, the
proposed approach showed the lowest error rate when compared to other explored
techniques. In particular, we achieved 75.4% accuracy on the validation data,
which is 20% higher than the handcrafted feature-based baseline. The source
code using Keras framework is publicly available.
Critical Points of Neural Networks: Analytical Forms and Landscape Properties
Authors: Yi Zhou, Yingbin Liang
Date: 2017-10-30 00:00:00

Due to the success of deep learning to solving a variety of challenging
machine learning tasks, there is a rising interest in understanding loss
functions for training neural networks from a theoretical aspect. Particularly,
the properties of critical points and the landscape around them are of
importance to determine the convergence performance of optimization algorithms.
In this paper, we provide full (necessary and sufficient) characterization of
the analytical forms for the critical points (as well as global minimizers) of
the square loss functions for various neural networks. We show that the
analytical forms of the critical points characterize the values of the
corresponding loss functions as well as the necessary and sufficient conditions
to achieve global minimum. Furthermore, we exploit the analytical forms of the
critical points to characterize the landscape properties for the loss functions
of these neural networks. One particular conclusion is that: The loss function
of linear networks has no spurious local minimum, while the loss function of
one-hidden-layer nonlinear networks with ReLU activation function does have
local minimum that is not global minimum.
Improve SAT-solving with Machine Learning
Authors: Haoze Wu
Date: 2017-10-30 00:00:00
Tasks: BIG-bench Machine Learning, Multi-Stage Campaigning Optimization, regression

In this project, we aimed to improve the runtime of Minisat, a
Conflict-Driven Clause Learning (CDCL) solver that solves the Propositional
Boolean Satisfiability (SAT) problem. We first used a logistic regression model
to predict the satisfiability of propositional boolean formulae after fixing
the values of a certain fraction of the variables in each formula. We then
applied the logistic model and added a preprocessing period to Minisat to
determine the preferable initial value (either true or false) of each boolean
variable using a Monte-Carlo approach. Concretely, for each Monte-Carlo trial,
we fixed the values of a certain ratio of randomly selected variables, and
calculated the confidence that the resulting sub-formula is satisfiable with
our logistic regression model. The initial value of each variable was set based
on the mean confidence scores of the trials that started from the literals of
that variable. We were particularly interested in setting the initial values of
the backbone variables correctly, which are variables that have the same value
in all solutions of a SAT formula. Our Monte-Carlo method was able to set 78%
of the backbones correctly. Excluding the preprocessing time, compared with the
default setting of Minisat, the runtime of Minisat for satisfiable formulae
decreased by 23%. However, our method did not outperform vanilla Minisat in
runtime, as the decrease in the conflicts was outweighed by the long runtime of
the preprocessing period.
Deep word embeddings for visual speech recognition
Authors: Themos Stafylakis, Georgios Tzimiropoulos
Date: 2017-10-30 00:00:00
Tasks: Lipreading, speech-recognition, Speech Recognition, Visual Speech Recognition, Word Embeddings

In this paper we present a deep learning architecture for extracting word
embeddings for visual speech recognition. The embeddings summarize the
information of the mouth region that is relevant to the problem of word
recognition, while suppressing other types of variability such as speaker, pose
and illumination. The system is comprised of a spatiotemporal convolutional
layer, a Residual Network and bidirectional LSTMs and is trained on the
Lipreading in-the-wild database. We first show that the proposed architecture
goes beyond state-of-the-art on closed-set word identification, by attaining
11.92% error rate on a vocabulary of 500 words. We then examine the capacity of
the embeddings in modelling words unseen during training. We deploy
Probabilistic Linear Discriminant Analysis (PLDA) to model the embeddings and
perform low-shot learning experiments on words unseen during training. The
experiments demonstrate that word-level visual speech recognition is feasible
even in cases where the target words are not included in the training set.
Gene Ontology (GO) Prediction using Machine Learning Methods
Authors: Haoze Wu, Yangyu Zhou
Date: 2017-10-30 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

We applied machine learning to predict whether a gene is involved in axon regeneration. We extracted 31 features from different databases and trained five machine learning models. Our optimal model, a Random Forest Classifier with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than the baseline score. We concluded that our models have some predictive capability. Similar methodology and features could be applied to predict other Gene Ontology (GO) terms.
Improved Adversarial Systems for 3D Object Generation and Reconstruction
Authors: Edward Smith, David Meger
Date: 2017-07-29 00:00:00
Tasks: Object

This paper describes a new approach for training generative adversarial
networks (GAN) to understand the detailed 3D shape of objects. While GANs have
been used in this domain previously, they are notoriously hard to train,
especially for the complex joint data distribution over 3D objects of many
categories and orientations. Our method extends previous work by employing the
Wasserstein distance normalized with gradient penalization as a training
objective. This enables improved generation from the joint object shape
distribution. Our system can also reconstruct 3D shape from 2D images and
perform shape completion from occluded 2.5D range scans. We achieve notable
quantitative improvements in comparison to existing baselines