pwc-archive/papers-with-abstracts huggingface dataset:
Query Inseparability for ALC Ontologies
Authors: Elena Botoeva, Carsten Lutz, Vladislav Ryzhikov, Frank Wolter, Michael Zakharyaschev
Date: 2019-01-31 00:00:00

We investigate the problem whether two ALC ontologies are indistinguishable
(or inseparable) by means of queries in a given signature, which is fundamental
for ontology engineering tasks such as ontology versioning, modularisation,
update, and forgetting. We consider both knowledge base (KB) and TBox
inseparability. For KBs, we give model-theoretic criteria in terms of (finite
partial) homomorphisms and products and prove that this problem is undecidable
for conjunctive queries (CQs), but 2ExpTime-complete for unions of CQs (UCQs).
The same results hold if (U)CQs are replaced by rooted (U)CQs, where every
variable is connected to an answer variable. We also show that inseparability
by CQs is still undecidable if one KB is given in the lightweight DL EL and if
no restrictions are imposed on the signature of the CQs. We also consider the
problem whether two ALC TBoxes give the same answers to any query over any ABox
in a given signature and show that, for CQs, this problem is undecidable, too.
We then develop model-theoretic criteria for Horn-ALC TBoxes and show using
tree automata that, in contrast, inseparability becomes decidable and
2ExpTime-complete, even ExpTime-complete when restricted to (unions of) rooted
CQs.
Self-Supervised Visual Representations for Cross-Modal Retrieval
Authors: Yash Patel, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas, C. V. Jawahar
Date: 2019-01-31 00:00:00
Tasks: Articles, Cross-Modal Retrieval, image-classification, Image Classification, object-detection, Object Detection, Retrieval

Cross-modal retrieval methods have been significantly improved in last years
with the use of deep neural networks and large-scale annotated datasets such as
ImageNet and Places. However, collecting and annotating such datasets requires
a tremendous amount of human effort and, besides, their annotations are usually
limited to discrete sets of popular visual classes that may not be
representative of the richer semantics found on large-scale cross-modal
retrieval datasets. In this paper, we present a self-supervised cross-modal
retrieval framework that leverages as training data the correlations between
images and text on the entire set of Wikipedia articles. Our method consists in
training a CNN to predict: (1) the semantic context of the article in which an
image is more probable to appear as an illustration (global context), and (2)
the semantic context of its caption (local context). Our experiments
demonstrate that the proposed method is not only capable of learning
discriminative visual representations for solving vision tasks like image
classification and object detection, but that the learned representations are
better for cross-modal retrieval when compared to supervised pre-training of
the network on the ImageNet dataset.
Training Artificial Neural Networks by Generalized Likelihood Ratio Method: Exploring Brain-like Learning to Improve Robustness
Authors: Li Xiao, Yijie Peng, Jeff Hong, Zewu Ke, Shuhuai Yang
Date: 2019-01-31 00:00:00
Tasks: General Classification

In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,.e.g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions. The traditional back propagation method cannot train the artificial neural networks with aforementioned brain-like learning mechanisms. Numerical results show that the robustness of various artificial neural networks trained by the new method is significantly improved when the input data is affected by both the natural noises and adversarial attacks. Code is available: \url{https://github.com/LX-doctorAI/GLR_ADV} .
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search
Authors: Weiwen Jiang, Xinyi Zhang, Edwin H. -M. Sha, Lei Yang, Qingfeng Zhuge, Yiyu Shi, Jingtong Hu
Date: 2019-01-31 00:00:00
Tasks: Neural Architecture Search, Reinforcement Learning

A fundamental question lies in almost every application of deep neural
networks: what is the optimal neural architecture given a specific dataset?
Recently, several Neural Architecture Search (NAS) frameworks have been
developed that use reinforcement learning and evolutionary algorithm to search
for the solution. However, most of them take a long time to find the optimal
architecture due to the huge search space and the lengthy training process
needed to evaluate each candidate. In addition, most of them aim at accuracy
only and do not take into consideration the hardware that will be used to
implement the architecture. This will potentially lead to excessive latencies
beyond specifications, rendering the resulting architectures useless. To
address both issues, in this paper we use Field Programmable Gate Arrays
(FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely
FNAS, which will provide an optimal neural architecture with latency guaranteed
to meet the specification. In addition, with a performance abstraction model to
analyze the latency of neural architectures without training, our framework can
quickly prune architectures that do not satisfy the specification, leading to
higher efficiency. Experimental results on common data set such as ImageNet
show that in the cases where the state-of-the-art generates architectures with
latencies 7.81x longer than the specification, those from FNAS can meet the
specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to
11.13x speedup for the search process. To the best of the authors' knowledge,
this is the very first hardware aware NAS.
An Evaluation of the Human-Interpretability of Explanation
Authors: Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Sam Gershman, Finale Doshi-Velez
Date: 2019-01-31 00:00:00
Tasks: BIG-bench Machine Learning

Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable under three specific tasks that users may perform with machine learning systems: simulation of the response, verification of a suggested response, and determining whether the correctness of a suggested response changes under a change to the inputs. Through carefully controlled human-subject experiments, we identify regularizers that can be used to optimize for the interpretability of machine learning systems. Our results show that the type of complexity matters: cognitive chunks (newly defined concepts) affect performance more than variable repetitions, and these trends are consistent across tasks and domains. This suggests that there may exist some common design principles for explanation systems.
Medical Image Super-Resolution Using a Generative Adversarial Network
Authors: Yongpei Zhu, Xuesheng Zhang, Kehong Yuan
Date: 2019-01-30 00:00:00
Tasks: Brain Segmentation, Content-Based Image Retrieval, Generative Adversarial Network, Image Generation, Image Retrieval, Image Super-Resolution, Retrieval, Super-Resolution

During the growing popularity of electronic medical records, electronic medical record (EMR) data has exploded increasingly. It is very meaningful to retrieve high quality EMR in mass data. In this paper, an EMR value network with retrieval function is constructed by taking stroke disease as the research object. It mainly includes: 1) It establishes the electronic medical record database and corresponding stroke knowledge graph. 2) The strategy of similarity measurement is included three parts(patients' chief complaint, pathology results and medical images). Patients' chief complaints are text data, mainly describing patients' symptoms and expressed in words or phrases, and patients' chief complaints are input in independent tick of various symptoms. The data of the pathology results is a structured and digitized expression, so the input method is the same as the patient's chief complaint; Image similarity adopts content-based image retrieval(CBIR) technology. 3) The analytic hierarchy process (AHP) is used to establish the weights of the three types of data and then synthesize them into an indicator. The accuracy rate of similarity in top 5 was more than 85\% based on EMR database with more 200 stroke records using leave-one-out method. It will be the good tool for assistant diagnosis and doctor training, as good quality records are colleted into the databases, like Doctor Watson, in the future.
Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving
Authors: Meixin Zhu, Yinhai Wang, Ziyuan Pu, Jingyun Hu, Xuesong Wang, Ruimin Ke
Date: 2019-01-29 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL). To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed. With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment. A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model. Car-following behavior produced by the model were compared with that observed in the empirical NGSIM data, to demonstrate the model's ability to follow a lead vehicle safely, efficiently, and comfortably. Results show that the model demonstrates the capability of safe, efficient, and comfortable velocity control in that it 1) has small percentages (8\%) of dangerous minimum time to collision values (\textless\ 5s) than human drivers in the NGSIM data (35\%); 2) can maintain efficient and safe headways in the range of 1s to 2s; and 3) can follow the lead vehicle comfortably with smooth acceleration. The results indicate that reinforcement learning methods could contribute to the development of autonomous driving systems.
Geometric Interpretation of side-sharing and point-sharing solutions in the P3P Problem
Authors: Bo wang, Hao Hu, Caixia Zhang
Date: 2019-01-29 00:00:00

It is well known that the P3P problem could have 1, 2, 3 and at most 4
positive solutions under different configurations among its 3 control points
and the position of the optical center. Since in any real applications, the
knowledge on the exact number of possible solutions is a prerequisite for
selecting the right one among all the possible solutions, the study on the
phenomenon of multiple solutions in the P3P problem has been an active topic .
In this work, we provide some new geometric interpretations on the
multi-solution phenomenon in the P3P problem, our main results include: (1):
The necessary and sufficient condition for the P3P problem to have a pair of
side-sharing solutions is the two optical centers of the solutions both lie on
one of the 3 vertical planes to the base plane of control points; (2): The
necessary and sufficient condition for the P3P problem to have a pair of
point-sharing solutions is the two optical centers of the solutions both lie on
one of the 3 so-called skewed danger cylinders;(3): If the P3P problem has
other solutions in addition to a pair of side-sharing ( point-sharing)
solutions, these remaining solutions must be a point-sharing ( side-sharing )
pair. In a sense, the side-sharing pair and the point-sharing pair are
companion pairs. In sum, our results provide some new insights into the nature
of the multi-solution phenomenon in the P3P problem, in addition to their
academic value, they could also be used as some theoretical guidance for
practitioners in real applications to avoid occurrence of multiple solutions by
properly arranging the control points.
Fast Neural Network Predictions from Constrained Aerodynamics Datasets
Authors: Cristina White, Daniela Ushizima, Charbel Farhat
Date: 2019-01-26 00:00:00
Tasks: Inductive Bias

Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with sufficient accuracy. While many approaches involve simplified models of the underlying physics, others are model-free and make predictions based only on existing simulation data. We present a novel model-free approach in which we reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture (called a cluster network) with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions. Compared to the state-of-the-art in model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster, and easier to apply. Furthermore, we show that our method outperforms other model-free approaches.
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Authors: Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen
Date: 2019-02-03 00:00:00
Tasks: counterfactual, Model Selection

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.
Context-adaptive Entropy Model for End-to-end Optimized Image Compression
Authors: Jooyoung Lee, Seunghyun Cho, Seung-Kwon Beack
Date: 2018-09-27 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Compression, MS-SSIM, SSIM

We propose a context-adaptive entropy model for use in end-to-end optimized
image compression. Our model exploits two types of contexts, bit-consuming
contexts and bit-free contexts, distinguished based upon whether additional bit
allocation is required. Based on these contexts, we allow the model to more
accurately estimate the distribution of each latent representation with a more
generalized form of the approximation models, which accordingly leads to an
enhanced compression performance. Based on the experimental results, the
proposed method outperforms the traditional image codecs, such as BPG and
JPEG2000, as well as other previous artificial-neural-network (ANN) based
approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale
structural similarity (MS-SSIM) index.
Fine-Grained Temporal Relation Extraction
Authors: Siddharth Vashishtha, Benjamin Van Durme, Aaron Steven White
Date: 2019-02-04 00:00:00
Proceeding: ACL 2019 7
Tasks: Relation, Relation Extraction, Temporal Relation Extraction, Transfer Learning

We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.
Re-examination of the Role of Latent Variables in Sequence Modeling
Authors: Zihang Dai, Guokun Lai, Yiming Yang, Shinjae Yoo
Date: 2019-02-04 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Density Estimation

With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence. However, opposite results are also observed in other domains, where standard recurrent networks often outperform stochastic models. To better understand this discrepancy, we re-examine the roles of latent variables in stochastic recurrent models for speech density estimation. Our analysis reveals that under the restriction of fully factorized output distribution in previous evaluations, the stochastic models were implicitly leveraging intra-step correlation but the standard recurrent baselines were prohibited to do so, resulting in an unfair comparison. To correct the unfairness, we remove such restriction in our re-examination, where all the models can explicitly leverage intra-step correlation with an auto-regressive structure. Over a diverse set of sequential data, including human speech, MIDI music, handwriting trajectory and frame-permuted speech, our results show that stochastic recurrent models fail to exhibit any practical advantage despite the claimed theoretical superiority. In contrast, standard recurrent models equipped with an auto-regressive output distribution consistently perform better, significantly advancing the state-of-the-art results on three speech datasets.
Embodied Multimodal Multitask Learning
Authors: Devendra Singh Chaplot, Lisa Lee, Ruslan Salakhutdinov, Devi Parikh, Dhruv Batra
Date: 2019-02-04 00:00:00
Tasks: Deep Reinforcement Learning, Disentanglement, Embodied Question Answering, Question Answering, Reinforcement Learning, Transfer Learning, Visual Navigation

Recent efforts on training visual navigation agents conditioned on language
using deep reinforcement learning have been successful in learning policies for
different multimodal tasks, such as semantic goal navigation and embodied
question answering. In this paper, we propose a multitask model capable of
jointly learning these multimodal tasks, and transferring knowledge of words
and their grounding in visual objects across the tasks. The proposed model uses
a novel Dual-Attention unit to disentangle the knowledge of words in the
textual representations and visual concepts in the visual representations, and
align them with each other. This disentangled task-invariant alignment of
representations facilitates grounding and knowledge transfer across both tasks.
We show that the proposed model outperforms a range of baselines on both tasks
in simulated 3D environments. We also show that this disentanglement of
representations makes our model modular, interpretable, and allows for transfer
to instructions containing new words by leveraging object detectors.
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
Authors: Yuan Cao, Quanquan Gu
Date: 2019-02-04 00:00:00
Tasks: Generalization Bounds

Empirical studies show that gradient-based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. Very recently, a line of work explains in theory that with over-parameterization and proper random initialization, gradient-based methods can find the global minima of the training loss for DNNs. However, existing generalization error bounds are unable to explain the good generalization performance of over-parameterized DNNs. The major limitation of most existing generalization bounds is that they are based on uniform convergence and are independent of the training algorithm. In this work, we derive an algorithm-dependent generalization error bound for deep ReLU networks, and show that under certain assumptions on the data distribution, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small generalization error. Our work sheds light on explaining the good generalization performance of over-parameterized deep neural networks.
The FLoRes Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English
Authors: Francisco Guzmán, Peng-Jen Chen, Myle Ott, Juan Pino, Guillaume Lample, Philipp Koehn, Vishrav Chaudhary, Marc'Aurelio Ranzato
Date: 2019-02-04 00:00:00
Tasks: Machine Translation, Translation

For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available. Besides the technical challenges of learning with limited supervision, it is difficult to evaluate methods trained on low-resource language pairs because of the lack of freely and publicly available benchmarks. In this work, we introduce the FLoRes evaluation datasets for Nepali-English and Sinhala-English, based on sentences translated from Wikipedia. Compared to English, these are languages with very different morphology and syntax, for which little out-of-domain parallel data is available and for which relatively large amounts of monolingual data are freely available. We describe our process to collect and cross-check the quality of translations, and we report baseline performance using several learning settings: fully supervised, weakly supervised, semi-supervised, and fully unsupervised. Our experiments demonstrate that current state-of-the-art methods perform rather poorly on this benchmark, posing a challenge to the research community working on low-resource MT. Data and code to reproduce our experiments are available at https://github.com/facebookresearch/flores.
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
Authors: Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius, Danny Lange
Date: 2019-02-04 00:00:00
Tasks: Atari Games, Board Games

The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd person, procedurally generated environment. An agent playing Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players. These algorithms fail to produce agents capable of performing near human level.