pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration
Authors: Alexandre Péré, Sébastien Forestier, Olivier Sigaud, Pierre-Yves Oudeyer
Date: 2018-03-02 00:00:00
Proceeding: ICLR 2018 1
Tasks: Diversity, Representation Learning

Intrinsically motivated goal exploration algorithms enable machines to
discover repertoires of policies that produce a diversity of effects in complex
environments. These exploration algorithms have been shown to allow real world
robots to acquire skills such as tool use in high-dimensional continuous state
and action spaces. However, they have so far assumed that self-generated goals
are sampled in a specifically engineered feature space, limiting their
autonomy. In this work, we propose to use deep representation learning
algorithms to learn an adequate goal space. This is a developmental 2-stage
approach: first, in a perceptual learning stage, deep learning algorithms use
passive raw sensor observations of world changes to learn a corresponding
latent space; then goal exploration happens in a second stage by sampling goals
in this latent space. We present experiments where a simulated robot arm
interacts with an object, and we show that exploration algorithms using such
learned representations can match the performance obtained using engineered
representations.
Learning DNFs under product distributions via μ-biased quantum Fourier sampling
Authors: Varun Kanade, Andrea Rocchetto, Simone Severini
Date: 2018-02-15 00:00:00

We show that DNF formulae can be quantum PAC-learned in polynomial time under product distributions using a quantum example oracle. The best classical algorithm (without access to membership queries) runs in superpolynomial time. Our result extends the work by Bshouty and Jackson (1998) that proved that DNF formulae are efficiently learnable under the uniform distribution using a quantum example oracle. Our proof is based on a new quantum algorithm that efficiently samples the coefficients of a {\mu}-biased Fourier transform.
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Authors: Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, Priya Goyal, Zachary DeVito, William S. Moses, Sven Verdoolaege, Andrew Adams, Albert Cohen
Date: 2018-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Management, Scene Understanding, Speech-to-Text, Vocal Bursts Intensity Prediction

Deep learning models with convolutional and recurrent networks are now
ubiquitous and analyze massive amounts of audio, image, video, text and graph
data, with applications in automatic translation, speech-to-text, scene
understanding, ranking user preferences, ad placement, etc. Competing
frameworks for building these networks such as TensorFlow, Chainer, CNTK,
Torch/PyTorch, Caffe1/2, MXNet and Theano, explore different tradeoffs between
usability and expressiveness, research or production orientation and supported
hardware. They operate on a DAG of computational operators, wrapping
high-performance libraries such as CUDNN (for NVIDIA GPUs) or NNPACK (for
various CPUs), and automate memory allocation, synchronization, distribution.
Custom operators are needed where the computation does not fit existing
high-performance library calls, usually at a high engineering cost. This is
frequently required when new operators are invented by researchers: such
operators suffer a severe performance penalty, which limits the pace of
innovation. Furthermore, even if there is an existing runtime call these
frameworks can use, it often doesn't offer optimal performance for a user's
particular network architecture and dataset, missing optimizations between
operators as well as optimizations that can be done knowing the size and shape
of data. Our contributions include (1) a language close to the mathematics of
deep learning called Tensor Comprehensions, (2) a polyhedral Just-In-Time
compiler to convert a mathematical description of a deep learning DAG into a
CUDA kernel with delegated memory management and synchronization, also
providing optimizations such as operator fusion and specialization for specific
sizes, (3) a compilation cache populated by an autotuner. [Abstract cutoff]
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
Authors: Junaid Ahmed Ansari, Sarthak Sharma, Anshuman Majumdar, J. Krishna Murthy, K. Madhava Krishna
Date: 2018-03-06 00:00:00
Tasks: Autonomous Driving, Monocular Reconstruction, Object Localization

Accurate localization of other traffic participants is a vital task in
autonomous driving systems. State-of-the-art systems employ a combination of
sensing modalities such as RGB cameras and LiDARs for localizing traffic
participants, but most such demonstrations have been confined to plain roads.
We demonstrate, to the best of our knowledge, the first results for monocular
object localization and shape estimation on surfaces that do not share the same
plane with the moving monocular camera. We approximate road surfaces by local
planar patches and use semantic cues from vehicles in the scene to initialize a
local bundle-adjustment like procedure that simultaneously estimates the pose
and shape of the vehicles, and the orientation of the local ground plane on
which the vehicle stands as well. We evaluate the proposed approach on the
KITTI and SYNTHIA-SF benchmarks, for a variety of road plane configurations.
The proposed approach significantly improves the state-of-the-art for monocular
object localization on arbitrarily-shaped roads.
Solving the L1 regularized least square problem via a box-constrained smooth minimization
Authors: Majid Mohammadi, Wout Hofman, Yao-Hua Tan, S. Hamid Mousavi
Date: 2017-04-11 00:00:00

In this paper, an equivalent smooth minimization for the L1 regularized least square problem is proposed. The proposed problem is a convex box-constrained smooth minimization which allows applying fast optimization methods to find its solution. Further, it is investigated that the property "the dual of dual is primal" holds for the L1 regularized least square problem. A solver for the smooth problem is proposed, and its affinity to the proximal gradient is shown. Finally, the experiments on L1 and total variation regularized problems are performed, and the corresponding results are reported.
Online Deep Learning: Growing RBM on the fly
Authors: Savitha Ramasamy, Kanagasabai Rajaraman, Pavitra Krishnaswamy, Vijay Chandrasekhar
Date: 2018-03-06 00:00:00
Tasks: Binary Classification, Deep Learning, General Classification

We propose a novel online learning algorithm for Restricted Boltzmann
Machines (RBM), namely, the Online Generative Discriminative Restricted
Boltzmann Machine (OGD-RBM), that provides the ability to build and adapt the
network architecture of RBM according to the statistics of streaming data. The
OGD-RBM is trained in two phases: (1) an online generative phase for
unsupervised feature representation at the hidden layer and (2) a
discriminative phase for classification. The online generative training begins
with zero neurons in the hidden layer, adds and updates the neurons to adapt to
statistics of streaming data in a single pass unsupervised manner, resulting in
a feature representation best suited to the data. The discriminative phase is
based on stochastic gradient descent and associates the represented features to
the class labels. We demonstrate the OGD-RBM on a set of multi-category and
binary classification problems for data sets having varying degrees of
class-imbalance. We first apply the OGD-RBM algorithm on the multi-class MNIST
dataset to characterize the network evolution. We demonstrate that the online
generative phase converges to a stable, concise network architecture, wherein
individual neurons are inherently discriminative to the class labels despite
unsupervised training. We then benchmark OGD-RBM performance to other machine
learning, neural network and ClassRBM techniques for credit scoring
applications using 3 public non-stationary two-class credit datasets with
varying degrees of class-imbalance. We report that OGD-RBM improves accuracy by
2.5-3% over batch learning techniques while requiring at least 24%-70% fewer
neurons and fewer training samples. This online generative training approach
can be extended greedily to multiple layers for training Deep Belief Networks
in non-stationary data mining applications without the need for a priori fixed
architectures.
Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Authors: Ruqayya Awan, Navid Alemi Koohbanani, Muhammad Shaban, Anna Lisowska, Nasir Rajpoot
Date: 2018-02-12 00:00:00
Tasks: Cancer Classification, Classification, Classification Of Breast Cancer Histology Images, General Classification

Convolutional neural networks (CNNs) have been recently used for a variety of
histology image analysis. However, availability of a large dataset is a major
prerequisite for training a CNN which limits its use by the computational
pathology community. In previous studies, CNNs have demonstrated their
potential in terms of feature generalizability and transferability accompanied
with better performance. Considering these traits of CNN, we propose a simple
yet effective method which leverages the strengths of CNN combined with the
advantages of including contextual information, particularly designed for a
small dataset. Our method consists of two main steps: first it uses the
activation features of CNN trained for a patch-based classification and then it
trains a separate classifier using features of overlapping patches to perform
image-based classification using the contextual information. The proposed
framework outperformed the state-of-the-art method for breast cancer
classification.
Accelerated Gradient Boosting
Authors: Gérard Biau, Benoît Cadre, Laurent Rouvìère
Date: 2018-03-06 00:00:00

Gradient tree boosting is a prediction algorithm that sequentially produces a
model in the form of linear combinations of decision trees, by solving an
infinite-dimensional optimization problem. We combine gradient boosting and
Nesterov's accelerated descent to design a new algorithm, which we call AGB
(for Accelerated Gradient Boosting). Substantial numerical evidence is provided
on both synthetic and real-life data sets to assess the excellent performance
of the method in a large variety of prediction problems. It is empirically
shown that AGB is much less sensitive to the shrinkage parameter and outputs
predictors that are considerably more sparse in the number of trees, while
retaining the exceptional performance of gradient boosting.
CNN training with graph-based sample preselection: application to handwritten character recognition
Authors: Frédéric Rayar, Masanori Goto, Seiichi Uchida
Date: 2017-12-06 00:00:00
Tasks: General Classification

In this paper, we present a study on sample preselection in large training
data set for CNN-based classification. To do so, we structure the input data
set in a network representation, namely the Relative Neighbourhood Graph, and
then extract some vectors of interest. The proposed preselection method is
evaluated in the context of handwritten character recognition, by using two
data sets, up to several hundred thousands of images. It is shown that the
graph-based preselection can reduce the training data set without degrading the
recognition accuracy of a non pretrained CNN shallow model.
John's Walk
Authors: Adam Gustafson, Hariharan Narayanan
Date: 2018-03-06 00:00:00

We present an affine-invariant random walk for drawing uniform random samples from a convex body $\mathcal{K} \subset \mathbb{R}^n$ that uses maximum volume inscribed ellipsoids, known as John's ellipsoids, for the proposal distribution. Our algorithm makes steps using uniform sampling from the John's ellipsoid of the symmetrization of $\mathcal{K}$ at the current point. We show that from a warm start, the random walk mixes in $\widetilde{O}(n^7)$ steps where the log factors depend only on constants associated with the warm start and desired total variation distance to uniformity. We also prove polynomial mixing bounds starting from any fixed point $x$ such that for any chord $pq$ of $\mathcal{K}$ containing $x$, $\left|\log \frac{|p-x|}{|q-x|}\right|$ is bounded above by a polynomial in $n$.
Exact partial information decompositions for Gaussian systems based on dependency constraints
Authors: James W. Kay, Robin A. A. Ince
Date: 2018-03-06 00:00:00

The Partial Information Decomposition (PID) [arXiv:1004.2515] provides a
theoretical framework to characterize and quantify the structure of
multivariate information sharing. A new method (Idep) has recently been
proposed for computing a two-predictor PID over discrete spaces.
[arXiv:1709.06653] A lattice of maximum entropy probability models is
constructed based on marginal dependency constraints, and the unique
information that a particular predictor has about the target is defined as the
minimum increase in joint predictor-target mutual information when that
particular predictor-target marginal dependency is constrained. Here, we apply
the Idep approach to Gaussian systems, for which the marginally constrained
maximum entropy models are Gaussian graphical models. Closed form solutions for
the Idep PID are derived for both univariate and multivariate Gaussian systems.
Numerical and graphical illustrations are provided, together with practical and
theoretical comparisons of the Idep PID with the minimum mutual information PID
(Immi). [arXiv:1411.2832] In particular, it is proved that the Immi method
generally produces larger estimates of redundancy and synergy than does the
Idep method. In discussion of the practical examples, the PIDs are complemented
by the use of deviance tests for the comparison of Gaussian graphical models.
Automated Detection of Acute Leukemia using K-mean Clustering Algorithm
Authors: Sachin Kumar, Sumita Mishra, Pallavi Asthana, Pragya
Date: 2018-03-06 00:00:00
Tasks: Clustering, Diagnostic

Leukemia is a hematologic cancer which develops in blood tissue and triggers
rapid production of immature and abnormal shaped white blood cells. Based on
statistics it is found that the leukemia is one of the leading causes of death
in men and women alike. Microscopic examination of blood sample or bone marrow
smear is the most effective technique for diagnosis of leukemia. Pathologists
analyze microscopic samples to make diagnostic assessments on the basis of
characteristic cell features. Recently, computerized methods for cancer
detection have been explored towards minimizing human intervention and
providing accurate clinical information. This paper presents an algorithm for
automated image based acute leukemia detection systems. The method implemented
uses basic enhancement, morphology, filtering and segmenting technique to
extract region of interest using k-means clustering algorithm. The proposed
algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor
(KNN) and Naive Bayes Classifier on the data-set of 60 samples.
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
Authors: Gabriel Grand, Idan Asher Blank, Francisco Pereira, Evelina Fedorenko
Date: 2018-02-05 00:00:00
Tasks: Word Embeddings

The words of a language reflect the structure of the human mind, allowing us
to transmit thoughts between individuals. However, language can represent only
a subset of our rich and detailed cognitive architecture. Here, we ask what
kinds of common knowledge (semantic memory) are captured by word meanings
(lexical semantics). We examine a prominent computational model that represents
words as vectors in a multidimensional space, such that proximity between
word-vectors approximates semantic relatedness. Because related words appear in
similar contexts, such spaces - called "word embeddings" - can be learned from
patterns of lexical co-occurrences in natural language. Despite their
popularity, a fundamental concern about word embeddings is that they appear to
be semantically "rigid": inter-word proximity captures only overall similarity,
yet human judgments about object similarities are highly context-dependent and
involve multiple, distinct semantic features. For example, dolphins and
alligators appear similar in size, but differ in intelligence and
aggressiveness. Could such context-dependent relationships be recovered from
word embeddings? To address this issue, we introduce a powerful, domain-general
solution: "semantic projection" of word-vectors onto lines that represent
various object features, like size (the line extending from the word "small" to
"big"), intelligence (from "dumb" to "smart"), or danger (from "safe" to
"dangerous"). This method, which is intuitively analogous to placing objects
"on a mental scale" between two extremes, recovers human judgments across a
range of object categories and properties. We thus show that word embeddings
inherit a wealth of common knowledge from word co-occurrence statistics and can
be flexibly manipulated to express context-dependent meanings.
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Authors: Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse
Date: 2018-03-06 00:00:00
Proceeding: ICLR 2018 1

Careful tuning of the learning rate, or even schedules thereof, can be
crucial to effective neural net training. There has been much recent interest
in gradient-based meta-optimization, where one tunes hyperparameters, or even
learns an optimizer, in order to minimize the expected loss when the training
procedure is unrolled. But because the training procedure must be unrolled
thousands of times, the meta-objective must be defined with an
orders-of-magnitude shorter time horizon than is typical for neural net
training. We show that such short-horizon meta-objectives cause a serious bias
towards small step sizes, an effect we term short-horizon bias. We introduce a
toy problem, a noisy quadratic cost function, on which we analyze short-horizon
bias by deriving and comparing the optimal schedules for short and long time
horizons. We then run meta-optimization experiments (both offline and online)
on standard benchmark datasets, showing that meta-optimization chooses too
small a learning rate by multiple orders of magnitude, even when run with a
moderately long time horizon (100 steps) typical of work in the area. We
believe short-horizon bias is a fundamental problem that needs to be addressed
if meta-optimization is to scale to practical neural net training regimes.
Intent-aware Multi-agent Reinforcement Learning
Authors: Siyuan Qi, Song-Chun Zhu
Date: 2018-03-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes an intent-aware multi-agent planning framework as well as
a learning algorithm. Under this framework, an agent plans in the goal space to
maximize the expected utility. The planning process takes the belief of other
agents' intents into consideration. Instead of formulating the learning problem
as a partially observable Markov decision process (POMDP), we propose a simple
but effective linear function approximation of the utility function. It is
based on the observation that for humans, other people's intents will pose an
influence on our utility for a goal. The proposed framework has several major
advantages: i) it is computationally feasible and guaranteed to converge. ii)
It can easily integrate existing intent prediction and low-level planning
algorithms. iii) It does not suffer from sparse feedbacks in the action space.
We experiment our algorithm in a real-world problem that is non-episodic, and
the number of agents and goals can vary over time. Our algorithm is trained in
a scene in which aerial robots and humans interact, and tested in a novel scene
with a different environment. Experimental results show that our algorithm
achieves the best performance and human-like behaviors emerge during the
dynamic process.
Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation
Authors: Kapil Katyal, Katie Popek, Chris Paxton, Joseph Moore, Kevin Wolfe, Philippe Burlina, Gregory D. Hager
Date: 2018-03-06 00:00:00
Tasks: Navigate, SSIM

Fast, collision-free motion through unknown environments remains a
challenging problem for robotic systems. In these situations, the robot's
ability to reason about its future motion is often severely limited by sensor
field of view (FOV). By contrast, biological systems routinely make decisions
by taking into consideration what might exist beyond their FOV based on prior
experience. In this paper, we present an approach for predicting occupancy map
representations of sensor data for future robot motions using deep neural
networks. We evaluate several deep network architectures, including purely
generative and adversarial models. Testing on both simulated and real
environments we demonstrated performance both qualitatively and quantitatively,
with SSIM similarity measure up to 0.899. We showed that it is possible to make
predictions about occupied space beyond the physical robot's FOV from simulated
training data. In the future, this method will allow robots to navigate through
unknown environments in a faster, safer manner.
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
Authors: Hussein Hazimeh, Rahul Mazumder
Date: 2018-03-05 00:00:00
Tasks: Combinatorial Optimization, Sparse Learning, Variable Selection

The $L_0$-regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. Recent work has shown that modern mixed integer optimization (MIO) solvers can be used to address small to moderate instances of this problem. In spite of the usefulness of $L_0$-based estimators and generic MIO solvers, there is a steep computational price to pay when compared to popular sparse learning algorithms (e.g., based on $L_1$ regularization). In this paper, we aim to push the frontiers of computation for a family of $L_0$-regularized problems with additional convex penalties. We propose a new hierarchy of necessary optimality conditions for these problems. We develop fast algorithms, based on coordinate descent and local combinatorial optimization, that are guaranteed to converge to solutions satisfying these optimality conditions. From a statistical viewpoint, an interesting story emerges. When the signal strength is high, our combinatorial optimization algorithms have an edge in challenging statistical settings. When the signal is lower, pure $L_0$ benefits from additional convex regularization. We empirically demonstrate that our family of $L_0$-based estimators can outperform the state-of-the-art sparse learning algorithms in terms of a combination of prediction, estimation, and variable selection metrics under various regimes (e.g., different signal strengths, feature correlations, number of samples and features). Our new open-source sparse learning toolkit L0Learn (available on CRAN and Github) reaches up to a three-fold speedup (with $p$ up to $10^6$) when compared to competing toolkits such as glmnet and ncvreg.