pwc-archive/papers-with-abstracts huggingface dataset:
DCM Bandits: Learning to Rank with Multiple Clicks
Authors: Sumeet Katariya, Branislav Kveton, Csaba Szepesvári, Zheng Wen
Date: 2016-02-09 00:00:00
Tasks: Learning-To-Rank

A search engine recommends to the user a list of web pages. The user examines
this list, from the first page to the last, and clicks on all attractive pages
until the user is satisfied. This behavior of the user can be described by the
dependent click model (DCM). We propose DCM bandits, an online learning variant
of the DCM where the goal is to maximize the probability of recommending
satisfactory items, such as web pages. The main challenge of our learning
problem is that we do not observe which attractive item is satisfactory. We
propose a computationally-efficient learning algorithm for solving our problem,
dcmKL-UCB; derive gap-dependent upper bounds on its regret under reasonable
assumptions; and also prove a matching lower bound up to logarithmic factors.
We evaluate our algorithm on synthetic and real-world problems, and show that
it performs well even when our model is misspecified. This work presents the
first practical and regret-optimal online algorithm for learning to rank with
multiple clicks in a cascade-like click model.
Texture Synthesis Using Shallow Convolutional Networks with Random Filters
Authors: Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge
Date: 2016-05-31 00:00:00
Tasks: Texture Synthesis

Here we demonstrate that the feature space of random shallow convolutional
neural networks (CNNs) can serve as a surprisingly good model of natural
textures. Patches from the same texture are consistently classified as being
more similar then patches from different textures. Samples synthesized from the
model capture spatial correlations on scales much larger then the receptive
field size, and sometimes even rival or surpass the perceptual quality of state
of the art texture models (but show less variability). The current state of the
art in parametric texture synthesis relies on the multi-layer feature space of
deep CNNs that were trained on natural images. Our finding suggests that such
optimized multi-layer feature spaces are not imperative for texture modeling.
Instead, much simpler shallow and convolutional networks can serve as the basis
for novel texture synthesis algorithms.
TensorFlow: A system for large-scale machine learning
Authors: Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng
Date: 2016-05-27 00:00:00
Tasks: BIG-bench Machine Learning, Management

TensorFlow is a machine learning system that operates at large scale and in
heterogeneous environments. TensorFlow uses dataflow graphs to represent
computation, shared state, and the operations that mutate that state. It maps
the nodes of a dataflow graph across many machines in a cluster, and within a
machine across multiple computational devices, including multicore CPUs,
general-purpose GPUs, and custom designed ASICs known as Tensor Processing
Units (TPUs). This architecture gives flexibility to the application developer:
whereas in previous "parameter server" designs the management of shared state
is built into the system, TensorFlow enables developers to experiment with
novel optimizations and training algorithms. TensorFlow supports a variety of
applications, with particularly strong support for training and inference on
deep neural networks. Several Google services use TensorFlow in production, we
have released it as an open-source project, and it has become widely used for
machine learning research. In this paper, we describe the TensorFlow dataflow
model in contrast to existing systems, and demonstrate the compelling
performance that TensorFlow achieves for several real-world applications.
Technical Report: Directed Controller Synthesis of Discrete Event Systems
Authors: Daniel Ciolek, Victor Braberman, Nicolás D'Ippolito, Sebastián Uchitel
Date: 2016-05-31 00:00:00

This paper presents a Directed Controller Synthesis (DCS) technique for
discrete event systems. The DCS method explores the solution space for reactive
controllers guided by a domain-independent heuristic. The heuristic is derived
from an efficient abstraction of the environment based on the componentized way
in which complex environments are described. Then by building the composition
of the components on-the-fly DCS obtains a solution by exploring a reduced
portion of the state space. This work focuses on untimed discrete event systems
with safety and co-safety (i.e. reachability) goals. An evaluation for the
technique is presented comparing it to other well-known approaches to
controller synthesis (based on symbolic representation and compositional
analyses).
Collaborative Filtering Bandits
Authors: Shuai Li, Alexandros Karatzoglou, Claudio Gentile
Date: 2015-02-11 00:00:00
Tasks: Clustering, Collaborative Filtering, News Recommendation

Classical collaborative filtering, and content-based filtering methods try to
learn a static recommendation model given training data. These approaches are
far from ideal in highly dynamic recommendation domains such as news
recommendation and computational advertisement, where the set of items and
users is very fluid. In this work, we investigate an adaptive clustering
technique for content recommendation based on exploration-exploitation
strategies in contextual multi-armed bandit settings. Our algorithm takes into
account the collaborative effects that arise due to the interaction of the
users with the items, by dynamically grouping users based on the items under
consideration and, at the same time, grouping items based on the similarity of
the clusterings induced over the users. The resulting algorithm thus takes
advantage of preference patterns in the data in a way akin to collaborative
filtering methods. We provide an empirical analysis on medium-size real-world
datasets, showing scalability and increased prediction performance (as measured
by click-through rate) over state-of-the-art methods for clustering bandits. We
also provide a regret analysis within a standard linear stochastic noise
setting.
Fast Zero-Shot Image Tagging
Authors: Yang Zhang, Boqing Gong, Mubarak Shah
Date: 2016-05-31 00:00:00
Proceeding: CVPR 2016 6
Tasks: Multi-label zero-shot learning, Zero-Shot Learning

The well-known word analogy experiments show that the recent word vectors
capture fine-grained linguistic regularities in words by linear vector offsets,
but it is unclear how well the simple vector offsets can encode visual
regularities over words. We study a particular image-word relevance relation in
this paper. Our results show that the word vectors of relevant tags for a given
image rank ahead of the irrelevant tags, along a principal direction in the
word vector space. Inspired by this observation, we propose to solve image
tagging by estimating the principal direction for an image. Particularly, we
exploit linear mappings and nonlinear deep neural networks to approximate the
principal direction from an input image. We arrive at a quite versatile tagging
model. It runs fast given a test image, in constant time w.r.t.\ the training
set size. It not only gives superior performance for the conventional tagging
task on the NUS-WIDE dataset, but also outperforms competitive baselines on
annotating images with previously unseen tags
Towards ontology driven learning of visual concept detectors
Authors: Sanchit Arora, Chuck Cho, Paul Fitzpatrick, Francois Scharffe
Date: 2016-05-31 00:00:00
Tasks: Active Learning, Object Recognition

The maturity of deep learning techniques has led in recent years to a
breakthrough in object recognition in visual media. While for some specific
benchmarks, neural techniques seem to match if not outperform human judgement,
challenges are still open for detecting arbitrary concepts in arbitrary videos.
In this paper, we propose a system that combines neural techniques, a large
scale visual concepts ontology, and an active learning loop, to provide on the
fly model learning of arbitrary concepts. We give an overview of the system as
a whole, and focus on the central role of the ontology for guiding and
bootstrapping the learning of new concepts, improving the recall of concept
detection, and, on the user end, providing semantic search on a library of
annotated videos.
Information Theoretically Aided Reinforcement Learning for Embodied Agents
Authors: Guido Montufar, Keyan Ghazi-Zahedi, Nihat Ay
Date: 2016-05-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning for embodied agents is a challenging problem. The
accumulated reward to be optimized is often a very rugged function, and
gradient methods are impaired by many local optimizers. We demonstrate, in an
experimental setting, that incorporating an intrinsic reward can smoothen the
optimization landscape while preserving the global optimizers of interest. We
show that policy gradient optimization for locomotion in a complex morphology
is significantly improved when supplementing the extrinsic reward by an
intrinsic reward defined in terms of the mutual information of time consecutive
sensor readings.
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
Authors: Muhammad Imran, Prasenjit Mitra, Carlos Castillo
Date: 2016-05-19 00:00:00
Proceeding: LREC 2016 5
Tasks: Disaster Response, Humanitarian, Word Embeddings

Microblogging platforms such as Twitter provide active communication channels
during mass convergence and emergency events such as earthquakes, typhoons.
During the sudden onset of a crisis situation, affected people post useful
information on Twitter that can be used for situational awareness and other
humanitarian disaster response efforts, if processed timely and effectively.
Processing social media information pose multiple challenges such as parsing
noisy, brief and informal messages, learning information categories from the
incoming stream of messages and classifying them into different classes among
others. One of the basic necessities of many of these tasks is the availability
of data, in particular human-annotated data. In this paper, we present
human-annotated Twitter corpora collected during 19 different crises that took
place between 2013 and 2015. To demonstrate the utility of the annotations, we
train machine learning classifiers. Moreover, we publish first largest word2vec
word embeddings trained on 52 million crisis-related tweets. To deal with
tweets language issues, we present human-annotated normalized lexical resources
for different lexical variations.
Contextual LSTM (CLSTM) models for Large scale NLP tasks
Authors: Shalini Ghosh, Oriol Vinyals, Brian Strope, Scott Roy, Tom Dean, Larry Heck
Date: 2016-02-19 00:00:00
Tasks: Articles, Paraphrase Generation, Prediction, Question Answering, Sentence, Sentence Completion

Documents exhibit sequential structure at multiple levels of abstraction
(e.g., sentences, paragraphs, sections). These abstractions constitute a
natural hierarchy for representing the context in which to infer the meaning of
words and larger fragments of text. In this paper, we present CLSTM (Contextual
LSTM), an extension of the recurrent neural network LSTM (Long-Short Term
Memory) model, where we incorporate contextual features (e.g., topics) into the
model. We evaluate CLSTM on three specific NLP tasks: word prediction, next
sentence selection, and sentence topic prediction. Results from experiments run
on two corpora, English documents in Wikipedia and a subset of articles from a
recent snapshot of English Google News, indicate that using both words and
topics as features improves performance of the CLSTM models over baseline LSTM
models for these tasks. For example on the next sentence selection task, we get
relative accuracy improvements of 21% for the Wikipedia dataset and 18% for the
Google News dataset. This clearly demonstrates the significant benefit of using
context appropriately in natural language (NL) tasks. This has implications for
a wide variety of NL applications like question answering, sentence completion,
paraphrase generation, and next utterance prediction in dialog systems.
CYCLADES: Conflict-free Asynchronous Machine Learning
Authors: Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael. I. Jordan, Kannan Ramchandran, Chris Re, Benjamin Recht
Date: 2016-05-31 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: BIG-bench Machine Learning, Stochastic Optimization

We present CYCLADES, a general framework for parallelizing stochastic
optimization algorithms in a shared memory setting. CYCLADES is asynchronous
during shared model updates, and requires no memory locking mechanisms, similar
to HOGWILD!-type algorithms. Unlike HOGWILD!, CYCLADES introduces no conflicts
during the parallel execution, and offers a black-box analysis for provable
speedups across a large family of algorithms. Due to its inherent conflict-free
nature and cache locality, our multi-core implementation of CYCLADES
consistently outperforms HOGWILD!-type algorithms on sufficiently sparse
datasets, leading to up to 40% speedup gains compared to the HOGWILD!
implementation of SGD, and up to 5x gains over asynchronous implementations of
variance reduction algorithms.
Semi-supervised Zero-Shot Learning by a Clustering-based Approach
Authors: Seyed Mohsen Shojaee, Mahdieh Soleymani Baghshah
Date: 2016-05-29 00:00:00
Tasks: Clustering, Object Recognition, Zero-Shot Learning

In some of object recognition problems, labeled data may not be available for
all categories. Zero-shot learning utilizes auxiliary information (also called
signatures) describing each category in order to find a classifier that can
recognize samples from categories with no labeled instance. In this paper, we
propose a novel semi-supervised zero-shot learning method that works on an
embedding space corresponding to abstract deep visual features. We seek a
linear transformation on signatures to map them onto the visual features, such
that the mapped signatures of the seen classes are close to labeled samples of
the corresponding classes and unlabeled data are also close to the mapped
signatures of one of the unseen classes.
  We use the idea that the rich deep visual features provide a representation
space in which samples of each class are usually condensed in a cluster. The
effectiveness of the proposed method is demonstrated through extensive
experiments on four public benchmarks improving the state-of-the-art prediction
accuracy on three of them.
Mixture Proportion Estimation via Kernel Embedding of Distributions
Authors: Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari
Date: 2016-03-08 00:00:00
Tasks: Anomaly Detection, Weakly-supervised Learning

Mixture proportion estimation (MPE) is the problem of estimating the weight
of a component distribution in a mixture, given samples from the mixture and
component. This problem constitutes a key part in many "weakly supervised
learning" problems like learning with positive and unlabelled samples, learning
with label noise, anomaly detection and crowdsourcing. While there have been
several methods proposed to solve this problem, to the best of our knowledge no
efficient algorithm with a proven convergence rate towards the true proportion
exists for this problem. We fill this gap by constructing a provably correct
algorithm for MPE, and derive convergence rates under certain assumptions on
the distribution. Our method is based on embedding distributions onto an RKHS,
and implementing it only requires solving a simple convex quadratic programming
problem a few times. We run our algorithm on several standard classification
datasets, and demonstrate that it performs comparably to or better than other
algorithms on most datasets.
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Authors: Tejas D. Kulkarni, Karthik R. Narasimhan, Ardavan Saeedi, Joshua B. Tenenbaum
Date: 2016-04-20 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Deep Reinforcement Learning, Montezuma's Revenge, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning goal-directed behavior in environments with sparse feedback is a
major challenge for reinforcement learning algorithms. The primary difficulty
arises due to insufficient exploration, resulting in an agent being unable to
learn robust value functions. Intrinsically motivated agents can explore new
behavior for its own sake rather than to directly solve problems. Such
intrinsic behaviors could eventually help the agent solve tasks posed by the
environment. We present hierarchical-DQN (h-DQN), a framework to integrate
hierarchical value functions, operating at different temporal scales, with
intrinsically motivated deep reinforcement learning. A top-level value function
learns a policy over intrinsic goals, and a lower-level function learns a
policy over atomic actions to satisfy the given goals. h-DQN allows for
flexible goal specifications, such as functions over entities and relations.
This provides an efficient space for exploration in complicated environments.
We demonstrate the strength of our approach on two problems with very sparse,
delayed feedback: (1) a complex discrete stochastic decision process, and (2)
the classic ATARI game `Montezuma's Revenge'.
Average-case Hardness of RIP Certification
Authors: Tengyao Wang, Quentin Berthet, Yaniv Plan
Date: 2016-05-31 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: compressed sensing

The restricted isometry property (RIP) for design matrices gives guarantees
for optimal recovery in sparse linear models. It is of high interest in
compressed sensing and statistical learning. This property is particularly
important for computationally efficient recovery methods. As a consequence,
even though it is in general NP-hard to check that RIP holds, there have been
substantial efforts to find tractable proxies for it. These would allow the
construction of RIP matrices and the polynomial-time verification of RIP given
an arbitrary matrix. We consider the framework of average-case certifiers, that
never wrongly declare that a matrix is RIP, while being often correct for
random instances. While there are such functions which are tractable in a
suboptimal parameter regime, we show that this is a computationally hard task
in any better regime. Our results are based on a new, weaker assumption on the
problem of detecting dense subgraphs.
Horizontally Scalable Submodular Maximization
Authors: Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
Date: 2016-05-31 00:00:00

A variety of large-scale machine learning problems can be cast as instances
of constrained submodular maximization. Existing approaches for distributed
submodular maximization have a critical drawback: The capacity - number of
instances that can fit in memory - must grow with the data set size. In
practice, while one can provision many machines, the capacity of each machine
is limited by physical constraints. We propose a truly scalable approach for
distributed submodular maximization under fixed capacity. The proposed
framework applies to a broad class of algorithms and constraints and provides
theoretical guarantees on the approximation factor for any available capacity.
We empirically evaluate the proposed algorithm on a variety of data sets and
demonstrate that it achieves performance competitive with the centralized
greedy solution.
The use of deep learning in image segmentation, classification and detection
Authors: M. S. Badea, I. I. Felea, L. M. Florea, C. Vertan
Date: 2016-05-31 00:00:00
Tasks: Computational Efficiency, General Classification, Image Segmentation, Semantic Segmentation

Recent years have shown that deep learned neural networks are a valuable tool
in the field of computer vision. This paper addresses the use of two different
kinds of network architectures, namely LeNet and Network in Network (NiN). They
will be compared in terms of both performance and computational efficiency by
addressing the classification and detection problems. In this paper, multiple
databases will be used to test the networks. One of them contains images
depicting burn wounds from pediatric cases, another one contains an extensive
number of art images and other facial databases were used for facial keypoints
detection.
Coordinate Descent Methods for Symmetric Nonnegative Matrix Factorization
Authors: Arnaud Vandaele, Nicolas Gillis, Qi Lei, Kai Zhong, Inderjit Dhillon
Date: 2015-09-04 00:00:00
Tasks: Clustering

Given a symmetric nonnegative matrix $A$, symmetric nonnegative matrix
factorization (symNMF) is the problem of finding a nonnegative matrix $H$,
usually with much fewer columns than $A$, such that $A \approx HH^T$. SymNMF
can be used for data analysis and in particular for various clustering tasks.
In this paper, we propose simple and very efficient coordinate descent schemes
to solve this problem, and that can handle large and sparse input matrices. The
effectiveness of our methods is illustrated on synthetic and real-world data
sets, and we show that they perform favorably compared to recent
state-of-the-art methods.
Manipulated Object Proposal: A Discriminative Object Extraction and Feature Fusion Framework for First-Person Daily Activity Recognition
Authors: Changzhi Luo, Bingbing Ni, Jun Yuan, Jian-Feng Wang, Shuicheng Yan, Meng Wang
Date: 2015-09-02 00:00:00
Tasks: Action Recognition, Activity Recognition, Object, Object Proposal Generation, Temporal Action Localization

Detecting and recognizing objects interacting with humans lie in the center
of first-person (egocentric) daily activity recognition. However, due to noisy
camera motion and frequent changes in viewpoint and scale, most of the previous
egocentric action recognition methods fail to capture and model highly
discriminative object features. In this work, we propose a novel pipeline for
first-person daily activity recognition, aiming at more discriminative object
feature representation and object-motion feature fusion. Our object feature
extraction and representation pipeline is inspired by the recent success of
object hypotheses and deep convolutional neural network based detection
frameworks. Our key contribution is a simple yet effective manipulated object
proposal generation scheme. This scheme leverages motion cues such as motion
boundary and motion magnitude (in contrast, camera motion is usually considered
as "noise" for most previous methods) to generate a more compact and
discriminative set of object proposals, which are more closely related to the
objects which are being manipulated. Then, we learn more discriminative object
detectors from these manipulated object proposals based on region-based
convolutional neural network (R-CNN). Meanwhile, we develop a network based
feature fusion scheme which better combines object and motion features. We show
in experiments that the proposed framework significantly outperforms the
state-of-the-art recognition performance on a challenging first-person daily
activity benchmark.