pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits
Authors: Prashant Singh, Andreas Hellander
Date: 2018-05-22 00:00:00
Tasks: Feature Engineering, Multi-Armed Bandits, Time Series, Time Series Analysis

Approximate Bayesian computation is an established and popular method for
likelihood-free inference with applications in many disciplines. The
effectiveness of the method depends critically on the availability of well
performing summary statistics. Summary statistic selection relies heavily on
domain knowledge and carefully engineered features, and can be a laborious time
consuming process. Since the method is sensitive to data dimensionality, the
process of selecting summary statistics must balance the need to include
informative statistics and the dimensionality of the feature vector. This paper
proposes to treat the problem of dynamically selecting an appropriate summary
statistic from a given pool of candidate summary statistics as a multi-armed
bandit problem. This allows approximate Bayesian computation rejection sampling
to dynamically focus on a distribution over well performing summary statistics
as opposed to a fixed set of statistics. The proposed method is unique in that
it does not require any pre-processing and is scalable to a large number of
candidate statistics. This enables efficient use of a large library of possible
time series summary statistics without prior feature engineering. The proposed
approach is compared to state-of-the-art methods for summary statistics
selection using a challenging test problem from the systems biology literature.
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only
Authors: Yi-Chen Chen, Chia-Hao Shen, Sung-Feng Huang, Hung-Yi Lee
Date: 2018-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Automatic speech recognition (ASR) has been widely researched with supervised
approaches, while many low-resourced languages lack audio-text aligned data,
and supervised methods cannot be applied on them.
  In this work, we propose a framework to achieve unsupervised ASR on a read
English speech dataset, where audio and text are unaligned. In the first stage,
each word-level audio segment in the utterances is represented by a vector
representation extracted by a sequence-of-sequence autoencoder, in which
phonetic information and speaker information are disentangled.
  Secondly, semantic embeddings of audio segments are trained from the vector
representations using a skip-gram model. Last but not the least, an
unsupervised method is utilized to transform semantic embeddings of audio
segments to text embedding space, and finally the transformed embeddings are
mapped to words.
  With the above framework, we are towards unsupervised ASR trained by
unaligned text and speech only.
Cost-aware Cascading Bandits
Authors: Ruida Zhou, Chao Gan, Jing Yan, Cong Shen
Date: 2018-05-22 00:00:00

In this paper, we propose a cost-aware cascading bandits model, a new variant
of multi-armed ban- dits with cascading feedback, by considering the random
cost of pulling arms. In each step, the learning agent chooses an ordered list
of items and examines them sequentially, until certain stopping condition is
satisfied. Our objective is then to max- imize the expected net reward in each
step, i.e., the reward obtained in each step minus the total cost in- curred in
examining the items, by deciding the or- dered list of items, as well as when
to stop examina- tion. We study both the offline and online settings, depending
on whether the state and cost statistics of the items are known beforehand. For
the of- fline setting, we show that the Unit Cost Ranking with Threshold 1
(UCR-T1) policy is optimal. For the online setting, we propose a Cost-aware
Cas- cading Upper Confidence Bound (CC-UCB) algo- rithm, and show that the
cumulative regret scales in O(log T ). We also provide a lower bound for all
{\alpha}-consistent policies, which scales in {\Omega}(log T ) and matches our
upper bound. The performance of the CC-UCB algorithm is evaluated with both
synthetic and real-world data.
Crossmodal Attentive Skill Learner
Authors: Shayegan Omidshafiei, Dong-Ki Kim, Jason Pazis, Jonathan P. How
Date: 2017-11-28 00:00:00
Tasks: Atari Games, CPU, GPU, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated
with the recently-introduced Asynchronous Advantage Option-Critic (A2OC)
architecture [Harb et al., 2017] to enable hierarchical reinforcement learning
across multiple sensory inputs. We provide concrete examples where the approach
not only improves performance in a single task, but accelerates transfer to new
tasks. We demonstrate the attention mechanism anticipates and identifies useful
latent features, while filtering irrelevant sensor modalities during execution.
We modify the Arcade Learning Environment [Bellemare et al., 2013] to support
audio queries, and conduct evaluations of crossmodal learning in the Atari 2600
game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017],
we open-source a fast hybrid CPU-GPU implementation of CASL.
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit
Authors: Seyed H. F. Langroudi, Tej Pandit, Dhireesha Kudithipudi
Date: 2018-05-22 00:00:00
Tasks: Deep Learning, Quantization

Performing the inference step of deep learning in resource constrained
environments, such as embedded devices, is challenging. Success requires
optimization at both software and hardware levels. Low precision arithmetic and
specifically low precision fixed-point number systems have become the standard
for performing deep learning inference. However, representing non-uniform data
and distributed parameters (e.g. weights) by using uniformly distributed
fixed-point values is still a major drawback when using this number system.
Recently, the posit number system was proposed, which represents numbers in a
non-uniform manner. Therefore, in this paper we are motivated to explore using
the posit number system to represent the weights of Deep Convolutional Neural
Networks. However, we do not apply any quantization techniques and hence the
network weights do not require re-training. The results of this exploration
show that using the posit number system outperformed the fixed point number
system in terms of accuracy and memory utilization.
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Authors: Mark McLeod, Michael A. Osborne, Stephen J. Roberts
Date: 2018-05-22 00:00:00
Proceeding: ICML 2018 7
Tasks: Bayesian Optimization

We develop the first Bayesian Optimization algorithm, BLOSSOM, which selects
between multiple alternative acquisition functions and traditional local
optimization at each step. This is combined with a novel stopping condition
based on expected regret. This pairing allows us to obtain the best
characteristics of both local and Bayesian optimization, making efficient use
of function evaluations while yielding superior convergence to the global
minimum on a selection of optimization problems, and also halting optimization
once a principled and intuitive stopping condition has been fulfilled.
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Authors: Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How
Date: 2018-05-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Collective human knowledge has clearly benefited from the fact that
innovations by individuals are taught to others through communication. Similar
to human social groups, agents in distributed learning systems would likely
benefit from communication to share knowledge and teach skills. The problem of
teaching to improve agent learning has been investigated by prior works, but
these approaches make assumptions that prevent application of teaching to
general multiagent problems, or require domain expertise for problems they can
apply to. This learning to teach problem has inherent complexities related to
measuring long-term impacts of teaching that compound the standard multiagent
coordination challenges. In contrast to existing works, this paper presents the
first general framework and algorithm for intelligent agents to learn to teach
in a multiagent environment. Our algorithm, Learning to Coordinate and Teach
Reinforcement (LeCTR), addresses peer-to-peer teaching in cooperative
multiagent reinforcement learning. Each agent in our approach learns both when
and what to advise, then uses the received advice to improve local learning.
Importantly, these roles are not fixed; these agents learn to assume the role
of student and/or teacher at the appropriate moments, requesting and providing
advice in order to improve teamwide performance and learning. Empirical
comparisons against state-of-the-art teaching methods show that our teaching
agents not only learn significantly faster, but also learn to coordinate in
tasks where existing methods fail.
Confounding-Robust Policy Improvement
Authors: Nathan Kallus, Angela Zhou
Date: 2018-05-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Causal Inference

We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding. Previous approaches, which assume unconfoundedness, i.e., that no unobserved confounders affect both the treatment assignment as well as outcome, can lead to policies that introduce harm rather than benefit when some unobserved confounding is present, as is generally the case with observational data. Instead, since policy value and regret may not be point-identifiable, we study a method that minimizes the worst-case estimated regret of a candidate policy against a baseline policy over an uncertainty set for propensity weights that controls the extent of unobserved confounding. We prove generalization guarantees that ensure our policy will be safe when applied in practice and will in fact obtain the best-possible uniform control on the range of all possible population regrets that agree with the possible extent of confounding. We develop efficient algorithmic solutions to compute this confounding-robust policy. Finally, we assess and compare our methods on synthetic and semi-synthetic data. In particular, we consider a case study on personalizing hormone replacement therapy based on observational data, where we validate our results on a randomized experiment. We demonstrate that hidden confounding can hinder existing policy learning approaches and lead to unwarranted harm, while our robust approach guarantees safety and focuses on well-evidenced improvement, a necessity for making personalized treatment policies learned from observational data reliable in practice.
Computable Variants of AIXI which are More Powerful than AIXItl
Authors: Susumu Katayama
Date: 2018-05-22 00:00:00

This paper presents Unlimited Computable AI, or UCAI, that is a family of
computable variants of AIXI. UCAI is more powerful than AIXItl, that is a
conventional family of computable variants of AIXI, in the following ways: 1)
UCAI supports models of terminating computation, including typed lambda
calculus, while AIXItl only supports Turing machine with timeout t, which can
be simulated by typed lambda calculus for any t; 2) unlike UCAI, AIXItl limits
the program length to l.
Multi-level Wavelet-CNN for Image Restoration
Authors: Pengju Liu, Hongzhi Zhang, Kai Zhang, Liang Lin, WangMeng Zuo
Date: 2018-05-18 00:00:00
Tasks: Computational Efficiency, Denoising, Image Denoising, Image Restoration, Image Super-Resolution, JPEG Artifact Correction, Super-Resolution

The tradeoff between receptive field size and efficiency is a crucial issue
in low level vision. Plain convolutional networks (CNNs) generally enlarge the
receptive field at the expense of computational cost. Recently, dilated
filtering has been adopted to address this issue. But it suffers from gridding
effect, and the resulting receptive field is only a sparse sampling of input
image with checkerboard patterns. In this paper, we present a novel multi-level
wavelet CNN (MWCNN) model for better tradeoff between receptive field size and
computational efficiency. With the modified U-Net architecture, wavelet
transform is introduced to reduce the size of feature maps in the contracting
subnetwork. Furthermore, another convolutional layer is further used to
decrease the channels of feature maps. In the expanding subnetwork, inverse
wavelet transform is then deployed to reconstruct the high resolution feature
maps. Our MWCNN can also be explained as the generalization of dilated
filtering and subsampling, and can be applied to many image restoration tasks.
The experimental results clearly show the effectiveness of MWCNN for image
denoising, single image super-resolution, and JPEG image artifacts removal.
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users
Authors: Stefano Teso, Kristian Kersting
Date: 2018-05-22 00:00:00
Tasks: General Classification, image-classification, Image Classification

Although interactive learning puts the user into the loop, the learner
remains mostly a black box for the user. Understanding the reasons behind
queries and predictions is important when assessing how the learner works and,
in turn, trust. Consequently, we propose the novel framework of explanatory
interactive learning: in each step, the learner explains its interactive query
to the user, and she queries of any active classifier for visualizing
explanations of the corresponding predictions. We demonstrate that this can
boost the predictive and explanatory powers of and the trust into the learned
model, using text (e.g. SVMs) and image classification (e.g. neural networks)
experiments as well as a user study.
WSNet: Compact and Efficient Networks Through Weight Sampling
Authors: Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, Shuicheng Yan
Date: 2017-11-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Audio Classification, General Classification, Quantization

We present a new approach and a novel architecture, termed WSNet, for
learning compact and efficient deep neural networks. Existing approaches
conventionally learn full model parameters independently and then compress them
via ad hoc processing such as model pruning or filter factorization.
Alternatively, WSNet proposes learning model parameters by sampling from a
compact set of learnable parameters, which naturally enforces {parameter
sharing} throughout the learning process. We demonstrate that such a novel
weight sampling approach (and induced WSNet) promotes both weights and
computation sharing favorably. By employing this method, we can more
efficiently learn much smaller networks with competitive performance compared
to baseline networks with equal numbers of convolution filters. Specifically,
we consider learning compact and efficient 1D convolutional neural networks for
audio classification. Extensive experiments on multiple audio classification
datasets verify the effectiveness of WSNet. Combined with weight quantization,
the resulted models are up to 180 times smaller and theoretically up to 16
times faster than the well-established baselines, without noticeable
performance drop.
On Coresets for Logistic Regression
Authors: Alexander Munteanu, Chris Schwiegelshohn, Christian Sohler, David P. Woodruff
Date: 2018-05-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: regression

Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce a complexity measure $\mu(X)$, which quantifies the hardness of compressing a data set for logistic regression. $\mu(X)$ has an intuitive statistical interpretation that may be of independent interest. For data sets with bounded $\mu(X)$-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear $(1\pm\varepsilon)$-coreset. We illustrate the performance of our method by comparing to uniform sampling as well as to state of the art methods in the area. The experiments are conducted on real world benchmark data for logistic regression.
Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks
Authors: Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Date: 2018-05-22 00:00:00
Tasks: Management, Surgical phase recognition

Real-time algorithms for automatically recognizing surgical phases are needed
to develop systems that can provide assistance to surgeons, enable better
management of operating room (OR) resources and consequently improve safety
within the OR. State-of-the-art surgical phase recognition algorithms using
laparoscopic videos are based on fully supervised training. This limits their
potential for widespread application, since creation of manual annotations is
an expensive process considering the numerous types of existing surgeries and
the vast amount of laparoscopic videos available. In this work, we propose a
new self-supervised pre-training approach based on the prediction of remaining
surgery duration (RSD) from laparoscopic videos. The RSD prediction task is
used to pre-train a convolutional neural network (CNN) and long short-term
memory (LSTM) network in an end-to-end manner. Our proposed approach utilizes
all available data and reduces the reliance on annotated data, thereby
facilitating the scaling up of surgical phase recognition algorithms to
different kinds of surgeries. Additionally, we present EndoN2N, an end-to-end
trained CNN-LSTM model for surgical phase recognition and evaluate the
performance of our approach on a dataset of 120 Cholecystectomy laparoscopic
videos (Cholec120). This work also presents the first systematic study of
self-supervised pre-training approaches to understand the amount of annotations
required for surgical phase recognition. Interestingly, the proposed RSD
pre-training approach leads to performance improvement even when all the
training data is manually annotated and outperforms the single pre-training
approach for surgical phase recognition presently published in the literature.
It is also observed that end-to-end training of CNN-LSTM networks boosts
surgical phase recognition performance.
Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks
Authors: Stefan Richthofer, Laurenz Wiskott
Date: 2018-05-22 00:00:00

Extended Predictable Feature Analysis (PFAx) [Richthofer and Wiskott, 2017]
is an extension of PFA [Richthofer and Wiskott, 2015] that allows generating a
goal-directed control signal of an agent whose dynamics has previously been
learned during a training phase in an unsupervised manner. PFAx hardly requires
assumptions or prior knowledge of the agent's sensor or control mechanics, or
of the environment. It selects features from a high-dimensional input by
intrinsic predictability and organizes them into a reasonably low-dimensional
model.
  While PFA obtains a well predictable model, PFAx yields a model ideally
suited for manipulations with predictable outcome. This allows for
goal-directed manipulation of an agent and thus for local navigation, i.e. for
reaching states where intermediate actions can be chosen by a permanent descent
of distance to the goal. The approach is limited when it comes to global
navigation, e.g. involving obstacles or multiple rooms.
  In this article, we extend theoretical results from [Sprekeler and Wiskott,
2008], enabling PFAx to perform stable global navigation. So far, the most
widely exploited characteristic of Slow Feature Analysis (SFA) was that
slowness yields invariances. We focus on another fundamental characteristics of
slow signals: They tend to yield monotonicity and one significant property of
monotonicity is that local optimization is sufficient to find a global optimum.
  We present an SFA-based algorithm that structures an environment such that
navigation tasks hierarchically decompose into subgoals. Each of these can be
efficiently achieved by PFAx, yielding an overall global solution of the task.
The algorithm needs to explore and process an environment only once and can
then perform all sorts of navigation tasks efficiently. We support this
algorithm by mathematical theory and apply it to different problems.
A 2D laser rangefinder scans dataset of standard EUR pallets
Authors: Ihab S. Mohamed, Alessio Capitanelli, Fulvio Mastrogiovanni, Stefano Rovetta, Renato Zaccaria
Date: 2018-05-22 00:00:00

In the past few years, the technology of automated guided vehicles (AGVs) has
notably advanced. In particular, in the context of factory and warehouse
automation, different approaches have been presented for detecting and
localizing pallets inside warehouses and shop-floor environments. In a related
research paper [1], we show that an AGVs can detect, localize, and track
pallets using machine learning techniques based only on the data of an on-board
2D laser rangefinder. Such sensor is very common in industrial scenarios due to
its simplicity and robustness, but it can only provide a limited amount of
data. Therefore, it has been neglected in the past in favor of more complex
solutions. In this paper, we release to the community the data we collected in
[1] for further research activities in the field of pallet localization and
tracking. The dataset comprises a collection of 565 2D scans from real-world
environments, which are divided into 340 samples where pallets are present, and
225 samples where they are not. The data have been manually labelled and are
provided in different formats.
Best of many worlds: Robust model selection for online supervised learning
Authors: Vidya Muthukumar, Mitas Ray, Anant Sahai, Peter L. Bartlett
Date: 2018-05-22 00:00:00
Tasks: Model Selection

We introduce algorithms for online, full-information prediction that are
competitive with contextual tree experts of unknown complexity, in both
probabilistic and adversarial settings. We show that by incorporating a
probabilistic framework of structural risk minimization into existing adaptive
algorithms, we can robustly learn not only the presence of stochastic structure
when it exists (leading to constant as opposed to $\mathcal{O}(\sqrt{T})$
regret), but also the correct model order. We thus obtain regret bounds that
are competitive with the regret of an optimal algorithm that possesses strong
side information about both the complexity of the optimal contextual tree
expert and whether the process generating the data is stochastic or
adversarial. These are the first constructive guarantees on simultaneous
adaptivity to the model and the presence of stochasticity.
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
Authors: Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
Date: 2018-04-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstractive Text Summarization, Decoder, Text Summarization, Unsupervised Extractive Summarization

Neural abstractive summarization models have led to promising results in
summarizing relatively short documents. We propose the first model for
abstractive summarization of single, longer-form documents (e.g., research
papers). Our approach consists of a new hierarchical encoder that models the
discourse structure of a document, and an attentive discourse-aware decoder to
generate the summary. Empirical results on two large-scale datasets of
scientific papers show that our model significantly outperforms
state-of-the-art models.