pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Planning with Deep Neural Network Learned Transition Models
Authors: Ga Wu, Buser Say, Scott Sanner
Date: 2019-04-05 00:00:00
Tasks: GPU

In many real-world planning problems with factored, mixed discrete and continuous state and action spaces such as Reservoir Control, Heating Ventilation, and Air Conditioning, and Navigation domains, it is difficult to obtain a model of the complex nonlinear dynamics that govern state evolution. However, the ubiquity of modern sensors allows us to collect large quantities of data from each of these complex systems and build accurate, nonlinear deep neural network models of their state transitions. But there remains one major problem for the task of control -- how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model techniques that ignore model structure and do not easily extend to mixed discrete and continuous domains? In this paper, we introduce two types of nonlinear planning methods that can leverage deep neural network learned transition models: Hybrid Deep MILP Planner (HD-MILP-Plan) and Tensorflow Planner (TF-Plan). In HD-MILP-Plan, we make the critical observation that the Rectified Linear Unit transfer function for deep networks not only allows faster convergence of model learning, but also permits a direct compilation of the deep network transition model to a Mixed-Integer Linear Program encoding. Further, we identify deep network specific optimizations for HD-MILP-Plan that improve performance over a base encoding and show that we can plan optimally with respect to the learned deep networks. In TF-Plan, we take advantage of the efficiency of auto-differentiation tools and GPU-based computation where we encode a subclass of purely continuous planning problems as Recurrent Neural Networks and directly optimize the actions through backpropagation. We compare both planners and show that TF-Plan is able to approximate the optimal plans found by HD-MILP-Plan in less computation time...
Mumford-Shah Loss Functional for Image Segmentation with Deep Learning
Authors: Boah Kim, Jong Chul Ye
Date: 2019-04-05 00:00:00
Tasks: Deep Learning, Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Semantic Segmentation

Recent state-of-the-art image segmentation algorithms are mostly based on deep neural networks, thanks to their high performance and fast computation time. However, these methods are usually trained in a supervised manner, which requires large number of high quality ground-truth segmentation masks. On the other hand, classical image segmentation approaches such as level-set methods are formulated in a self-supervised manner by minimizing energy functions such as Mumford-Shah functional, so they are still useful to help generation of segmentation masks without labels. Unfortunately, these algorithms are usually computationally expensive and often have limitation in semantic segmentation. In this paper, we propose a novel loss function based on Mumford-Shah functional that can be used in deep-learning based image segmentation without or with small labeled data. This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the Mumford-Shah functional. We show that the new loss function enables semi-supervised and unsupervised segmentation. In addition, our loss function can be also used as a regularized function to enhance supervised semantic segmentation algorithms. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method.
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Authors: Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, DaCheng Tao
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Super-Resolution, Video Super-Resolution

Recently, deep learning based video super-resolution (SR) methods have
achieved promising performance. To simultaneously exploit the spatial and
temporal information of videos, employing 3-dimensional (3D) convolutions is a
natural approach. However, straight utilizing 3D convolutions may lead to an
excessively high computational complexity which restricts the depth of video SR
models and thus undermine the performance. In this paper, we present a novel
fast spatio-temporal residual network (FSTRN) to adopt 3D convolutions for the
video SR task in order to enhance the performance while maintaining a low
computational load. Specifically, we propose a fast spatio-temporal residual
block (FRB) that divide each 3D filter to the product of two 3D filters, which
have considerably lower dimensions. Furthermore, we design a cross-space
residual learning that directly links the low-resolution space and the
high-resolution space, which can greatly relieve the computational burden on
the feature fusion and up-scaling parts. Extensive evaluations and comparisons
on benchmark datasets validate the strengths of the proposed approach and
demonstrate that the proposed network significantly outperforms the current
state-of-the-art methods.
Data Shapley: Equitable Valuation of Data for Machine Learning
Authors: Amirata Ghorbani, James Zou
Date: 2019-04-05 00:00:00
Tasks: BIG-bench Machine Learning, Data Valuation, Task 2

As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on $n$ data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We develop Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. In addition to being equitable, extensive experiments across biomedical, image and synthetic data demonstrate that data Shapley has several other benefits: 1) it is more powerful than the popular leave-one-out or leverage score in providing insight on what data is more valuable for a given learning task; 2) low Shapley value data effectively capture outliers and corruptions; 3) high Shapley value data inform what type of new data to acquire to improve the predictor.
Actively Seeking and Learning from Live Data
Authors: Damien Teney, Anton Van Den Hengel
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Meta-Learning, Question Answering, Retrieval, Visual Question Answering, Visual Question Answering (VQA)

One of the key limitations of traditional machine learning methods is their
requirement for training data that exemplifies all the information to be
learned. This is a particular problem for visual question answering methods,
which may be asked questions about virtually anything. The approach we propose
is a step toward overcoming this limitation by searching for the information
required at test time. The resulting method dynamically utilizes data from an
external source, such as a large set of questions/answers or images/captions.
Concretely, we learn a set of base weights for a simple VQA model, that are
specifically adapted to a given question with the information specifically
retrieved for this question. The adaptation process leverages recent advances
in gradient-based meta learning and contributions for efficient retrieval and
cross-domain adaptation. We surpass the state-of-the-art on the VQA-CP v2
benchmark and demonstrate our approach to be intrinsically more robust to
out-of-distribution test data. We demonstrate the use of external non-VQA data
using the MS COCO captioning dataset to support the answering process. This
approach opens a new avenue for open-domain VQA systems that interface with
diverse sources of data.
Deep Tree Learning for Zero-shot Face Anti-Spoofing
Authors: Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Xiaoming Liu
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Anti-Spoofing, Face Recognition

Face anti-spoofing is designed to keep face recognition systems from
recognizing fake faces as the genuine users. While advanced face anti-spoofing
methods are developed, new types of spoof attacks are also being created and
becoming a threat to all existing systems. We define the detection of unknown
spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous works of ZSFA
only study 1-2 types of spoof attacks, such as print/replay attacks, which
limits the insight of this problem. In this work, we expand the ZSFA problem to
a wide range of 13 types of spoof attacks, including print attack, replay
attack, 3D mask attacks, and so on. A novel Deep Tree Network (DTN) is proposed
to tackle the ZSFA. The tree is learned to partition the spoof samples into
semantic sub-groups in an unsupervised fashion. When a data sample arrives,
being know or unknown attacks, DTN routes it to the most similar spoof cluster,
and make the binary decision. In addition, to enable the study of ZSFA, we
introduce the first face anti-spoofing database that contains diverse types of
spoof attacks. Experiments show that our proposed method achieves the state of
the art on multiple testing protocols of ZSFA.
Graph Pattern Entity Ranking Model for Knowledge Graph Completion
Authors: Takuma Ebisu, Ryutaro Ichise
Date: 2019-04-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction

Knowledge graphs have evolved rapidly in recent years and their usefulness
has been demonstrated in many artificial intelligence tasks. However, knowledge
graphs often have lots of missing facts. To solve this problem, many knowledge
graph embedding models have been developed to populate knowledge graphs and
these have shown outstanding performance. However, knowledge graph embedding
models are so-called black boxes, and the user does not know how the
information in a knowledge graph is processed and the models can be difficult
to interpret. In this paper, we utilize graph patterns in a knowledge graph to
overcome such problems. Our proposed model, the {\it graph pattern entity
ranking model} (GRank), constructs an entity ranking system for each graph
pattern and evaluates them using a ranking measure. By doing so, we can find
graph patterns which are useful for predicting facts. Then, we perform link
prediction tasks on standard datasets to evaluate our GRank method. We show
that our approach outperforms other state-of-the-art approaches such as ComplEx
and TorusE for standard metrics such as HITS@{\it n} and MRR. Moreover, our
model is easily interpretable because the output facts are described by graph
patterns.
Planning under non-rational perception of uncertain spatial costs
Authors: Aamodh Suresh, Sonia Martinez
Date: 2019-04-05 00:00:00
Tasks: Motion Planning

This work investigates the design of risk-perception-aware motion-planning strategies that incorporate non-rational perception of risks associated with uncertain spatial costs. Our proposed method employs the Cumulative Prospect Theory (CPT) to generate a perceived risk map over a given environment. CPT-like perceived risks and path-length metrics are then combined to define a cost function that is compliant with the requirements of asymptotic optimality of sampling-based motion planners (RRT*). The modeling power of CPT is illustrated in theory and in simulation, along with a comparison to other risk perception models like Conditional Value at Risk (CVaR). Theoretically, we define a notion of expressiveness for a risk perception model and show that CPT's is higher than that of CVaR and expected risk. We then show that this expressiveness translates to our path planning setting, where we observe that a planner equipped with CPT together with a simultaneous perturbation stochastic approximation (SPSA) method can better approximate arbitrary paths in an environment. Additionally, we show in simulation that our planner captures a rich set of meaningful paths, representative of different risk perceptions in a custom environment. We then compare the performance of our planner with T-RRT* (a planner for continuous cost spaces) and Risk-RRT* (a risk-aware planner for dynamic human obstacles) through simulations in cluttered and dynamic environments respectively, showing the advantage of our proposed planner.
Deep Learning-based Universal Beamformer for Ultrasound Imaging
Authors: Shujaat Khan, Jaeyoung Huh, Jong Chul Ye
Date: 2019-04-05 00:00:00
Tasks: Deep Learning

In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases rapidly in situations where data acquisition is not ideal. Herein, for the first time, we demonstrate that a single data-driven adaptive beamformer designed as a deep neural network can generate high quality images robustly for various detector channel configurations and subsampling rates. The proposed deep beamformer is evaluated for two distinct acquisition schemes: focused ultrasound imaging and planewave imaging. Experimental results showed that the proposed deep beamformer exhibit significant performance gain for both focused and planar imaging schemes, in terms of contrast-to-noise ratio and structural similarity.
Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks
Authors: Fatemeh Sheikholeslami, Swayambhoo Jain, Georgios B. Giannakis
Date: 2019-04-05 00:00:00

Despite their unprecedented performance in various domains, utilization of Deep Neural Networks (DNNs) in safety-critical environments is severely limited in the presence of even small adversarial perturbations. The present work develops a randomized approach to detecting such perturbations based on minimum uncertainty metrics that rely on sampling at the hidden layers during the DNN inference stage. Inspired by Bayesian approaches to uncertainty estimation, the sampling probabilities are designed for effective detection of the adversarially corrupted inputs. Being modular, the novel detector of adversaries can be conveniently employed by any pre-trained DNN at no extra training overhead. Selecting which units to sample per hidden layer entails quantifying the amount of DNN output uncertainty, where the overall uncertainty is expressed in terms of its layer-wise components - what also promotes scalability. Sampling probabilities are then sought by minimizing uncertainty measures layer-by-layer, leading to a novel convex optimization problem that admits an exact solver with superlinear convergence rate. By simplifying the objective function, low-complexity approximate solvers are also developed. In addition to valuable insights, these approximations link the novel approach with state-of-the-art randomized adversarial detectors. The effectiveness of the novel detectors in the context of competing alternatives is highlighted through extensive tests for various types of adversarial attacks with variable levels of strength.
Combining Sentiment Lexica with a Multi-View Variational Autoencoder
Authors: Alexander Hoyle, Lawrence Wolf-Sonkin, Hanna Wallach, Ryan Cotterell, Isabelle Augenstein
Date: 2019-04-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: General Classification, Sentiment Analysis, text-classification, Text Classification

When assigning quantitative labels to a dataset, different methodologies may
rely on different scales. In particular, when assigning polarities to words in
a sentiment lexicon, annotators may use binary, categorical, or continuous
labels. Naturally, it is of interest to unify these labels from disparate
scales to both achieve maximal coverage over words and to create a single, more
robust sentiment lexicon while retaining scale coherence. We introduce a
generative model of sentiment lexica to combine disparate scales into a common
latent representation. We realize this model with a novel multi-view
variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a
downstream text classification task involving nine English-Language sentiment
analysis datasets; our representation outperforms six individual sentiment
lexica, as well as a straightforward combination thereof.
FLightNNs: Lightweight Quantized Deep Neural Networks for Fast and Accurate Inference
Authors: Ruizhou Ding, Zeye Liu, Ting-Wu Chin, Diana Marculescu, R. D., Blanton
Date: 2019-04-05 00:00:00
Tasks: Quantization

To improve the throughput and energy efficiency of Deep Neural Networks
(DNNs) on customized hardware, lightweight neural networks constrain the
weights of DNNs to be a limited combination (denoted as $k\in\{1,2\}$) of
powers of 2. In such networks, the multiply-accumulate operation can be
replaced with a single shift operation, or two shifts and an add operation. To
provide even more design flexibility, the $k$ for each convolutional filter can
be optimally chosen instead of being fixed for every filter. In this paper, we
formulate the selection of $k$ to be differentiable, and describe model
training for determining $k$-based weights on a per-filter basis. Over 46
FPGA-design experiments involving eight configurations and four data sets
reveal that lightweight neural networks with a flexible $k$ value (dubbed
FLightNNs) fully utilize the hardware resources on Field Programmable Gate
Arrays (FPGAs), our experimental results show that FLightNNs can achieve
2$\times$ speedup when compared to lightweight NNs with $k=2$, with only 0.1\%
accuracy degradation. Compared to a 4-bit fixed-point quantization, FLightNNs
achieve higher accuracy and up to 2$\times$ inference speedup, due to their
lightweight shift operations. In addition, our experiments also demonstrate
that FLightNNs can achieve higher computational energy efficiency for ASIC
implementation.
A Regularization Approach for Instance-Based Superset Label Learning
Authors: Chen Gong, Tongliang Liu, Yuanyan Tang, Jian Yang, Jie Yang, DaCheng Tao
Date: 2019-04-05 00:00:00

Different from the traditional supervised learning in which each training
example has only one explicit label, superset label learning (SLL) refers to
the problem that a training example can be associated with a set of candidate
labels, and only one of them is correct. Existing SLL methods are either
regularization-based or instance-based, and the latter of which has achieved
state-of-the-art performance. This is because the latest instance-based methods
contain an explicit disambiguation operation that accurately picks up the
groundtruth label of each training example from its ambiguous candidate labels.
However, such disambiguation operation does not fully consider the mutually
exclusive relationship among different candidate labels, so the disambiguated
labels are usually generated in a nondiscriminative way, which is unfavorable
for the instance-based methods to obtain satisfactory performance. To address
this defect, we develop a novel regularization approach for instance-based
superset label (RegISL) learning so that our instance-based method also
inherits the good discriminative ability possessed by the regularization
scheme. Specifically, we employ a graph to represent the training set, and
require the examples that are adjacent on the graph to obtain similar labels.
More importantly, a discrimination term is proposed to enlarge the gap of
values between possible labels and unlikely labels for every training example.
As a result, the intrinsic constraints among different candidate labels are
deployed, and the disambiguated labels generated by RegISL are more
discriminative and accurate than those output by existing instance-based
algorithms. The experimental results on various tasks convincingly demonstrate
the superiority of our RegISL to other typical SLL methods in terms of both
training accuracy and test accuracy.
An Evolutionary Framework for Automatic and Guided Discovery of Algorithms
Authors: Ruchira Sasanka, Konstantinos Krommydas
Date: 2019-04-05 00:00:00
Tasks: Evolutionary Algorithms

This paper presents Automatic Algorithm Discoverer (AAD), an evolutionary
framework for synthesizing programs of high complexity. To guide evolution,
prior evolutionary algorithms have depended on fitness (objective) functions,
which are challenging to design. To make evolutionary progress, instead, AAD
employs Problem Guided Evolution (PGE), which requires introduction of a group
of problems together. With PGE, solutions discovered for simpler problems are
used to solve more complex problems in the same group. PGE also enables several
new evolutionary strategies, and naturally yields to High-Performance Computing
(HPC) techniques.
  We find that PGE and related evolutionary strategies enable AAD to discover
algorithms of similar or higher complexity relative to the state-of-the-art.
Specifically, AAD produces Python code for 29 array/vector problems ranging
from min, max, reverse, to more challenging problems like sorting and
matrix-vector multiplication. Additionally, we find that AAD shows adaptability
to constrained environments/inputs and demonstrates outside-of-the-box problem
solving abilities.
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
Authors: Oliver J. Maclaren, Ruanui Nicholson
Date: 2019-04-04 00:00:00
Tasks: Causal Inference

We consider basic conceptual questions concerning the relationship between statistical estimation and causal inference. Firstly, we show how to translate causal inference problems into an abstract statistical formalism without requiring any structure beyond an arbitrarily-indexed family of probability models. The formalism is simple but can incorporate a variety of causal modelling frameworks, including 'structural causal models', but also models expressed in terms of, e.g., differential equations. We focus primarily on the structural/graphical causal modelling literature, however. Secondly, we consider the extent to which causal and statistical concerns can be cleanly separated, examining the fundamental question: 'What can be estimated from data?'. We call this the problem of estimability. We approach this by analysing a standard formal definition of 'can be estimated' commonly adopted in the causal inference literature -- identifiability -- in our abstract statistical formalism. We use elementary category theory to show that identifiability implies the existence of a Fisher-consistent estimator, but also show that this estimator may be discontinuous, and thus unstable, in general. This difficulty arises because the causal inference problem is, in general, an ill-posed inverse problem. Inverse problems have three conditions which must be satisfied to be considered well-posed: existence, uniqueness, and stability of solutions. Here identifiability corresponds to the question of uniqueness; in contrast, we take estimability to mean satisfaction of all three conditions, i.e. well-posedness. Lack of stability implies that naive translation of a causally identifiable quantity into an achievable statistical estimation target may prove impossible. Our article is primarily expository and aimed at unifying ideas from multiple fields, though we provide new constructions and proofs.