pwc-archive/papers-with-abstracts huggingface dataset:
Few-Shot Goal Inference for Visuomotor Learning and Planning
Authors: Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn
Date: 2018-09-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Navigation

Reinforcement learning and planning methods require an objective or reward
function that encodes the desired behavior. Yet, in practice, there is a wide
range of scenarios where an objective is difficult to provide programmatically,
such as tasks with visual observations involving unknown object positions or
deformable objects. In these cases, prior methods use engineered
problem-specific solutions, e.g., by instrumenting the environment with
additional sensors to measure a proxy for the objective. Such solutions require
a significant engineering effort on a per-task basis, and make it impractical
for robots to continuously learn complex skills outside of laboratory settings.
We aim to find a more general and scalable solution for specifying goals for
robot learning in unconstrained environments. To that end, we formulate the
few-shot objective learning problem, where the goal is to learn a task
objective from only a few example images of successful end states for that
task. We propose a simple solution to this problem: meta-learn a classifier
that can recognize new goals from a few examples. We show how this approach can
be used with both model-free reinforcement learning and visual model-based
planning and show results in three domains: rope manipulation from images in
simulation, visual navigation in a simulated 3D environment, and object
arrangement into user-specified configurations on a real robot.
Two new results about quantum exact learning
Authors: Srinivasan Arunachalam, Sourav Chakraborty, Troy Lee, Manaswi Paraashar, Ronald de Wolf
Date: 2018-09-30 00:00:00
Tasks: LEMMA, Vocal Bursts Valence Prediction

We present two new results about exact learning by quantum computers. First, we show how to exactly learn a $k$-Fourier-sparse $n$-bit Boolean function from $O(k^{1.5}(\log k)^2)$ uniform quantum examples for that function. This improves over the bound of $\widetilde{\Theta}(kn)$ uniformly random \emph{classical} examples (Haviv and Regev, CCC'15). Additionally, we provide a possible direction to improve our $\widetilde{O}(k^{1.5})$ upper bound by proving an improvement of Chang's lemma for $k$-Fourier-sparse Boolean functions. Second, we show that if a concept class $\mathcal{C}$ can be exactly learned using $Q$ quantum membership queries, then it can also be learned using $O\left(\frac{Q^2}{\log Q}\log|\mathcal{C}|\right)$ \emph{classical} membership queries. This improves the previous-best simulation result (Servedio and Gortler, SICOMP'04) by a $\log Q$-factor.
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation
Authors: Riddhish Bhalodia, Anupama Goparaju, Tim Sodergren, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Joshua Cates, Ross Whitaker, Shireen Elhabian
Date: 2018-09-30 00:00:00
Tasks: Anatomy, Atrial Fibrillation Recurrence Estimation, Data Augmentation, Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation

Left atrium shape has been shown to be an independent predictor of recurrence
after atrial fibrillation (AF) ablation. Shape-based representation is
imperative to such an estimation process, where correspondence-based
representation offers the most flexibility and ease-of-computation for
population-level shape statistics. Nonetheless, population-level shape
representations in the form of image segmentation and correspondence models
derived from cardiac MRI require significant human resources with sufficient
anatomy-specific expertise. In this paper, we propose a machine learning
approach that uses deep networks to estimate AF recurrence by predicting shape
descriptors directly from MRI images, with NO image pre-processing involved. We
also propose a novel data augmentation scheme to effectively train a deep
network in a limited training data setting. We compare this new method of
estimating shape descriptors from images with the state-of-the-art
correspondence-based shape modeling that requires image segmentation and
correspondence optimization. Results show that the proposed method and the
current state-of-the-art produce statistically similar outcomes on AF
recurrence, eliminating the need for expensive pre-processing pipelines and
associated human labor.
Automatic Evaluation of Neural Personality-based Chatbots
Authors: Yujie Xing, Raquel Fernández
Date: 2018-09-30 00:00:00
Proceeding: WS 2018 11
Tasks: Response Generation

Stylistic variation is critical to render the utterances generated by
conversational agents natural and engaging. In this paper, we focus on
sequence-to-sequence models for open-domain dialogue response generation and
propose a new method to evaluate the extent to which such models are able to
generate responses that reflect different personality traits.
Identifying Bias in AI using Simulation
Authors: Daniel McDuff, Roger Cheng, Ashish Kapoor
Date: 2018-09-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Face Detection

Machine learned models exhibit bias, often because the datasets used to train
them are biased. This presents a serious problem for the deployment of such
technology, as the resulting models might perform poorly on populations that
are minorities within the training set and ultimately present higher risks to
them. We propose to use high-fidelity computer simulations to interrogate and
diagnose biases within ML classifiers. We present a framework that leverages
Bayesian parameter search to efficiently characterize the high dimensional
feature space and more quickly identify weakness in performance. We apply our
approach to an example domain, face detection, and show that it can be used to
help identify demographic biases in commercial face application programming
interfaces (APIs).
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks
Authors: Kenneth T. Co, Luis Muñoz-González, Sixte de Maupeou, Emil C. Lupu
Date: 2018-09-30 00:00:00
Tasks: Bayesian Optimization

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules
Authors: Michalis K. Titsias, Sotirios Nikoloutsopoulos
Date: 2018-09-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning, Transfer Reinforcement Learning

We propose a probabilistic framework to directly insert prior knowledge in
reinforcement learning (RL) algorithms by defining the behaviour policy as a
Bayesian posterior distribution. Such a posterior combines task specific
information with prior knowledge, thus allowing to achieve transfer learning
across tasks. The resulting method is flexible and it can be easily
incorporated to any standard off-policy and on-policy algorithms, such as those
based on temporal differences and policy gradients. We develop a specific
instance of this Bayesian transfer RL framework by expressing prior knowledge
as general deterministic rules that can be useful in a large variety of tasks,
such as navigation tasks. Also, we elaborate more on recent probabilistic and
entropy-regularised RL by developing a novel temporal learning algorithm and
show how to combine it with Bayesian transfer RL. Finally, we demonstrate our
method for solving mazes and show that significant speed ups can be obtained.
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Authors: Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar, Jens Kober
Date: 2018-09-30 00:00:00
Tasks: Car Racing, Decision Making, Deep Reinforcement Learning, Reinforcement Learning

Deep Reinforcement Learning (DRL) has become a powerful strategy to solve
complex decision making problems based on Deep Neural Networks (DNNs). However,
it is highly data demanding, so unfeasible in physical systems for most
applications. In this work, we approach an alternative Interactive Machine
Learning (IML) strategy for training DNN policies based on human corrective
feedback, with a method called Deep COACH (D-COACH). This approach not only
takes advantage of the knowledge and insights of human teachers as well as the
power of DNNs, but also has no need of a reward function (which sometimes
implies the need of external perception for computing rewards). We combine Deep
Learning with the COrrective Advice Communicated by Humans (COACH) framework,
in which non-expert humans shape policies by correcting the agent's actions
during execution. The D-COACH framework has the potential to solve complex
problems without much data or time required. Experimental results validated the
efficiency of the framework in three different problems (two simulated, one
with a real robot), with state spaces of low and high dimensions, showing the
capacity to successfully learn policies for continuous action spaces like in
the Car Racing and Cart-Pole problems faster than with DRL.
3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image
Authors: Priyanka Mandikal, Navaneet K L, R. Venkatesh Babu
Date: 2018-09-30 00:00:00
Tasks: 3D Point Cloud Reconstruction, Point cloud reconstruction, Segmentation

We propose a mechanism to reconstruct part annotated 3D point clouds of
objects given just a single input image. We demonstrate that jointly training
for both reconstruction and segmentation leads to improved performance in both
the tasks, when compared to training for each task individually. The key idea
is to propagate information from each task so as to aid the other during the
training procedure. Towards this end, we introduce a location-aware
segmentation loss in the training regime. We empirically show the effectiveness
of the proposed loss in generating more faithful part reconstructions while
also improving segmentation accuracy. We thoroughly evaluate the proposed
approach on different object categories from the ShapeNet dataset to obtain
improved results in reconstruction as well as segmentation.
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming
Authors: Ciro Potena, Raghav Khanna, Juan Nieto, Roland Siegwart, Daniele Nardi, Alberto Pretto
Date: 2018-09-30 00:00:00
Tasks: Optical Flow Estimation

The combination of aerial survey capabilities of Unmanned Aerial Vehicles
with targeted intervention abilities of agricultural Unmanned Ground Vehicles
can significantly improve the effectiveness of robotic systems applied to
precision agriculture. In this context, building and updating a common map of
the field is an essential but challenging task. The maps built using robots of
different types show differences in size, resolution and scale, the associated
geolocation data may be inaccurate and biased, while the repetitiveness of both
visual appearance and geometric structures found within agricultural contexts
render classical map merging techniques ineffective. In this paper we propose
AgriColMap, a novel map registration pipeline that leverages a grid-based
multimodal environment representation which includes a vegetation index map and
a Digital Surface Model. We cast the data association problem between maps
built from UAVs and UGVs as a multimodal, large displacement dense optical flow
estimation. The dominant, coherent flows, selected using a voting scheme, are
used as point-to-point correspondences to infer a preliminary non-rigid
alignment between the maps. A final refinement is then performed, by exploiting
only meaningful parts of the registered maps. We evaluate our system using real
world data for 3 fields with different crop species. The results show that our
method outperforms several state of the art map registration and matching
techniques by a large margin, and has a higher tolerance to large initial
misalignments. We release an implementation of the proposed approach along with
the acquired datasets with this paper.
An Application of ASP Theories of Intentions to Understanding Restaurant Scenarios: Insights and Narrative Corpus
Authors: Qinglin Zhang, Chris Benton, Daniela Inclezan
Date: 2018-09-30 00:00:00

This paper presents a practical application of Answer Set Programming to the
understanding of narratives about restaurants. While this task was investigated
in depth by Erik Mueller, exceptional scenarios remained a serious challenge
for his script-based story comprehension system. We present a methodology that
remedies this issue by modeling characters in a restaurant episode as
intentional agents. We focus especially on the refinement of certain components
of this methodology in order to increase coverage and performance. We present a
restaurant story corpus that we created to design and evaluate our methodology.
Under consideration in Theory and Practice of Logic Programming (TPLP).
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
Authors: Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
Date: 2018-09-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Neural Network Compression, Quantization

While deep neural networks are a highly successful model class, their large
memory footprint puts considerable strain on energy consumption, communication
bandwidth, and storage requirements. Consequently, model size reduction has
become an utmost goal in deep learning. A typical approach is to train a set of
deterministic weights, while applying certain techniques such as pruning and
quantization, in order that the empirical weight distribution becomes amenable
to Shannon-style coding schemes. However, as shown in this paper, relaxing
weight determinism and using a full variational distribution over weights
allows for more efficient coding schemes and consequently higher compression
rates. In particular, following the classical bits-back argument, we encode the
network weights using a random sample, requiring only a number of bits
corresponding to the Kullback-Leibler divergence between the sampled
variational distribution and the encoding distribution. By imposing a
constraint on the Kullback-Leibler divergence, we are able to explicitly
control the compression rate, while optimizing the expected loss on the
training set. The employed encoding scheme can be shown to be close to the
optimal information-theoretical lower bound, with respect to the employed
variational family. Our method sets new state-of-the-art in neural network
compression, as it strictly dominates previous approaches in a Pareto sense: On
the benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best
test performance for a fixed memory budget, and vice versa, it achieves the
highest compression rates for a fixed test performance.
Parameter-free Sentence Embedding via Orthogonal Basis
Authors: Ziyi Yang, Chenguang Zhu, Weizhu Chen
Date: 2018-09-30 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentence, Sentence Embedding, Sentence-Embedding, Word Embeddings

We propose a simple and robust non-parameterized approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word's novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Our model shows superior performance compared with non-parameterized alternatives and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video
Authors: Huizi Mao, Taeyoung Kong, William J. Dally
Date: 2018-09-30 00:00:00
Tasks: object-detection, Object Detection

Detecting objects in a video is a compute-intensive task. In this paper we
propose CaTDet, a system to speedup object detection by leveraging the temporal
correlation in video. CaTDet consists of two DNN models that form a cascaded
detector, and an additional tracker to predict regions of interests based on
historic detections. We also propose a new metric, mean Delay(mD), which is
designed for latency-critical video applications. Experiments on the KITTI
dataset show that CaTDet reduces operation count by 5.1-8.7x with the same mean
Average Precision(mAP) as the single-model Faster R-CNN detector and incurs
additional delay of 0.3 frame. On CityPersons dataset, CaTDet achieves 13.0x
reduction in operations with 0.8% mAP loss.
Efficient Sequence Labeling with Actor-Critic Training
Authors: Saeed Najafi, Colin Cherry, Grzegorz Kondrak
Date: 2018-09-30 00:00:00
Tasks: Decision Making, NER, Sequential Decision Making, Transliteration

Neural approaches to sequence labeling often use a Conditional Random Field
(CRF) to model their output dependencies, while Recurrent Neural Networks (RNN)
are used for the same purpose in other tasks. We set out to establish RNNs as
an attractive alternative to CRFs for sequence labeling. To do so, we address
one of the RNN's most prominent shortcomings, the fact that it is not exposed
to its own errors with the maximum-likelihood training. We frame the prediction
of the output sequence as a sequential decision-making process, where we train
the network with an adjusted actor-critic algorithm (AC-RNN). We
comprehensively compare this strategy with maximum-likelihood training for both
RNNs and CRFs on three structured-output tasks. The proposed AC-RNN efficiently
matches the performance of the CRF on NER and CCG tagging, and outperforms it
on Machine Transliteration. We also show that our training strategy is
significantly better than other techniques for addressing RNN's exposure bias,
such as Scheduled Sampling, and Self-Critical policy training.
Optical Illusions Images Dataset
Authors: Robert Max Williams, Roman V. Yampolskiy
Date: 2018-09-30 00:00:00

Human vision is capable of performing many tasks not optimized for in its
long evolution. Reading text and identifying artificial objects such as road
signs are both tasks that mammalian brains never encountered in the wild but
are very easy for us to perform. However, humans have discovered many very
specific tricks that cause us to misjudge color, size, alignment and movement
of what we are looking at. A better understanding of these phenomenon could
reveal insights into how human perception achieves these feats. In this paper
we present a dataset of 6725 illusion images gathered from two websites, and a
smaller dataset of 500 hand-picked images. We will discuss the process of
collecting this data, models trained on it, and the work that needs to be done
to make it of value to computer vision researchers.
Distributed linear regression by averaging
Authors: Edgar Dobriban, Yue Sheng
Date: 2018-09-30 00:00:00
Tasks: regression

Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communicate short messages. Communication is often the bottleneck. In this paper, we study one-step and iterative weighted parameter averaging in statistical linear models under data parallelism. We do linear regression on each machine, send the results to a central server, and take a weighted average of the parameters. Optionally, we iterate, sending back the weighted average and doing local ridge regressions centered at it. How does this work compared to doing linear regression on the full data? Here we study the performance loss in estimation, test error, and confidence interval length in high dimensions, where the number of parameters is comparable to the training data size. We find the performance loss in one-step weighted averaging, and also give results for iterative averaging. We also find that different problems are affected differently by the distributed framework. Estimation error and confidence interval length increase a lot, while prediction error increases much less. We rely on recent results from random matrix theory, where we develop a new calculus of deterministic equivalents as a tool of broader interest.
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
Authors: Guillaume Baudart, Javier Burroni, Martin Hirzel, Louis Mandel, Avraham Shinnar
Date: 2018-09-30 00:00:00
Tasks: Probabilistic Programming, Representation Learning, Variational Inference

Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming languages like Church, Anglican, or Pyro. This paper presents a comprehensive compilation scheme to compile any Stan model to a generative language and proves its correctness. We use our compilation scheme to build two new backends for the Stanc3 compiler targeting Pyro and NumPyro. Experimental results show that the NumPyro backend yields a 2.3x speedup compared to Stan in geometric mean over 26 benchmarks. Building on Pyro we extend Stan with support for explicit variational inference guides and deep probabilistic models. That way, users familiar with Stan get access to new features without having to learn a fundamentally new language.
Modelling local phase of images and textures with applications in phase denoising and phase retrieval
Authors: Ido Zachevsky, Yehoshua Y. Zeevi
Date: 2018-09-30 00:00:00
Tasks: Denoising, Image Retrieval, Retrieval

The Fourier magnitude has been studied extensively, but less effort has been
devoted to the Fourier phase, despite its well-established importance in image
representation. Global phase was shown to be more important for image
representation than the magnitude, whereas local phase, exhibited in Gabor
filters, has been used for analysis purposes in detecting image contours and
edges. Neither global nor local phase has been modelled in closed form,
suitable for Bayesian estimation.
  In this work, we analyze the local phase of textured images and propose a
local (Markovian) model for local phase coefficients. This model is
Gaussian-mixture-based, learned from the graph representation of images, based
on their complex wavelet decomposition. We demonstrate the applicability of the
model in restoration of images with noisy local phase and in image retrieval,
where we show superior performance to the well-known hybrid input-output (HIO)
method. We also provide a framework for application of the model in a general
setup of image processing.