pwc-archive/papers-with-abstracts huggingface dataset:
SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning
Authors: Samineh Bagheri, Wolfgang Konen, Thomas Bäck
Date: 2019-04-17 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Real-world optimization problems often have expensive objective functions in
terms of cost and time. It is desirable to find near-optimal solutions with
very few function evaluations. Surrogate-assisted optimizers tend to reduce the
required number of function evaluations by replacing the real function with an
efficient mathematical model built on few evaluated points. Problems with a
high condition number are a challenge for many surrogate-assisted optimizers
including SACOBRA. To address such problems we propose a new online whitening
operating in the black-box optimization paradigm. We show on a set of
high-conditioning functions that online whitening tackles SACOBRA's early
stagnation issue and reduces the optimization error by a factor between 10 to
1e12 as compared to the plain SACOBRA, though it imposes many extra function
evaluations. Covariance matrix adaptation evolution strategy (CMA-ES) has for
very high numbers of function evaluations even lower errors, whereas SACOBRA
performs better in the expensive setting (less than 1e03 function evaluations).
If we count all parallelizable function evaluations (population evaluation in
CMA-ES, online whitening in our approach) as one iteration, then both
algorithms have comparable strength even on the long run. This holds for
problems with dimension D <= 20.
Process of image super-resolution
Authors: Sebastien Lablanche, Gerard Lablanche
Date: 2019-04-17 00:00:00
Tasks: compressed sensing, Image Super-Resolution, Super-Resolution

In this paper we explain a process of super-resolution reconstruction allowing to increase the resolution of an image.The need for high-resolution digital images exists in diverse domains, for example the medical and spatial domains. The obtaining of high-resolution digital images can be made at the time of the shooting, but it is often synonymic of important costs because of the necessary material to avoid such costs, it is known how to use methods of super-resolution reconstruction, consisting from one or several low resolution images to obtain a high-resolution image. The american patent US 9208537 describes such an algorithm. A zone of one low-resolution image is isolated and categorized according to the information contained in pixels forming the borders of the zone. The category of it zone determines the type of interpolation used to add pixels in aforementioned zone, to increase the neatness of the images. It is also known how to reconstruct a low-resolution image there high-resolution image by using a model of super-resolution reconstruction whose learning is based on networks of neurons and on image or a picture library. The demand of chinese patent CN 107563965 and the scientist publication "Pixel Recursive Super Resolution", R. Dahl, M. Norouzi, J. Shlens propose such methods. The aim of this paper is to demonstrate that it is possible to reconstruct coherent human faces from very degraded pixelated images with a very fast algorithm, more faster than compressed sensing (CS), easier to compute and without deep learning, so without important technology resources, i.e. a large database of thousands training images (see arXiv:2003.13063). This technological breakthrough has been patented in 2018 with the demand of French patent FR 1855485 (https://patents.google.com/patent/FR3082980A1, see the HAL reference https://hal.archives-ouvertes.fr/hal-01875898v1).
Casting Light on Invisible Cities: Computationally Engaging with Literary Criticism
Authors: Shufan Wang, Mohit Iyyer
Date: 2019-04-17 00:00:00
Proceeding: NAACL 2019 6

Literary critics often attempt to uncover meaning in a single work of
literature through careful reading and analysis. Applying natural language
processing methods to aid in such literary analyses remains a challenge in
digital humanities. While most previous work focuses on "distant reading" by
algorithmically discovering high-level patterns from large collections of
literary works, here we sharpen the focus of our methods to a single literary
theory about Italo Calvino's postmodern novel Invisible Cities, which consists
of 55 short descriptions of imaginary cities. Calvino has provided a
classification of these cities into eleven thematic groups, but literary
scholars disagree as to how trustworthy his categorization is. Due to the
unique structure of this novel, we can computationally weigh in on this debate:
we leverage pretrained contextualized representations to embed each city's
description and use unsupervised methods to cluster these embeddings.
Additionally, we compare results of our computational approach to similarity
judgments generated by human readers. Our work is a first step towards
incorporating natural language processing into literary criticism.
Vid2Game: Controllable Characters Extracted from Real-World Videos
Authors: Oran Gafni, Lior Wolf, Yaniv Taigman
Date: 2019-04-17 00:00:00
Proceeding: ICLR 2020 1

We are given a video of a person performing a certain activity, from which we
extract a controllable model. The model generates novel image sequences of that
person, according to arbitrary user-defined control signals, typically marking
the displacement of the moving body. The generated video can have an arbitrary
background, and effectively capture both the dynamics and appearance of the
person.
  The method is based on two networks. The first network maps a current pose,
and a single-instance control signal to the next pose. The second network maps
the current pose, the new pose, and a given background, to an output frame.
Both networks include multiple novelties that enable high-quality performance.
This is demonstrated on multiple characters extracted from various videos of
dancers and athletes.
Dynamic Evaluation of Transformer Language Models
Authors: Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals
Date: 2019-04-17 00:00:00
Tasks: Language Modeling, Language Modelling

This research note combines two methods that have recently improved the state
of the art in language modeling: Transformers and dynamic evaluation.
Transformers use stacked layers of self-attention that allow them to capture
long range dependencies in sequential data. Dynamic evaluation fits models to
the recent sequence history, allowing them to assign higher probabilities to
re-occurring sequential patterns. By applying dynamic evaluation to
Transformer-XL models, we improve the state of the art on enwik8 from 0.99 to
0.94 bits/char, text8 from 1.08 to 1.04 bits/char, and WikiText-103 from 18.3
to 16.4 perplexity points.
Gaze Training by Modulated Dropout Improves Imitation Learning
Authors: Yuying Chen, Congcong Liu, Lei Tai, Ming Liu, Bertram E. Shi
Date: 2019-04-17 00:00:00
Tasks: Autonomous Driving, Imitation Learning

Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior. Typically, a convolutional neural network learns to predict the steering commands from raw driver-view images by mimicking the behaviors of human drivers. However, there are other cues, such as gaze behavior, available from human drivers that have yet to be exploited. Previous researches have shown that novice human learners can benefit from observing experts' gaze patterns. We present here that deep neural networks can also profit from this. We propose a method, gaze-modulated dropout, for integrating this gaze information into a deep driving network implicitly rather than as an additional input. Our experimental results demonstrate that gaze-modulated dropout enhances the generalization capability of the network to unseen scenes. Prediction error in steering commands is reduced by 23.5% compared to uniform dropout. Running closed loop in the simulator, the gaze-modulated dropout net increased the average distance travelled between infractions by 58.5%. Consistent with these results, the gaze-modulated dropout net shows lower model uncertainty.
Document Expansion by Query Prediction
Authors: Rodrigo Nogueira, Wei Yang, Jimmy Lin, Kyunghyun Cho
Date: 2019-04-17 00:00:00
Tasks: Passage Re-Ranking, Prediction, Question Answering, Re-Ranking, Retrieval

One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content.From the perspective of a question answering system, this might comprise questions the document can potentially answer. Following this observation, we propose a simple method that predicts which queries will be issued for a given document and then expands it with those predictions with a vanilla sequence-to-sequence model, trained using datasets consisting of pairs of query and relevant documents. By combining our method with a highly-effective re-ranking component, we achieve the state of the art in two retrieval tasks. In a latency-critical regime, retrieval results alone (without re-ranking) approach the effectiveness of more computationally expensive neural re-rankers but are much faster.
Relay: A High-Level Compiler for Deep Learning
Authors: Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Logan Weber, Josh Pollock, Luis Vega, Ziheng Jiang, Tianqi Chen, Thierry Moreau, Zachary Tatlock
Date: 2019-04-17 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending these frameworks to accommodate the rapidly diversifying landscape of DL models and hardware platforms presents challenging tradeoffs between expressivity, composability, and portability. We present Relay, a new compiler framework for DL. Relay's functional, statically typed intermediate representation (IR) unifies and generalizes existing DL IRs to express state-of-the-art models. The introduction of Relay's expressive IR requires careful design of domain-specific optimizations, addressed via Relay's extension mechanisms. Using these extension mechanisms, Relay supports a unified compiler that can target a variety of hardware platforms. Our evaluation demonstrates Relay's competitive performance for a broad class of models and devices (CPUs, GPUs, and emerging accelerators). Relay's design demonstrates how a unified IR can provide expressivity, composability, and portability without compromising performance.
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion
Authors: Tao Hu, Zhizhong Han, Abhinav Shrivastava, Matthias Zwicker
Date: 2019-04-17 00:00:00
Tasks: Image-to-Image Translation, Translation

We propose a novel approach for 3D shape completion by synthesizing multi-view depth maps. While previous work for shape completion relies on volumetric representations, meshes, or point clouds, we propose to use multi-view depth maps from a set of fixed viewing angles as our shape representation. This allows us to be free of the limitations of memory for volumetric representations and point clouds by casting shape completion into an image-to-image translation problem. Specifically, we render depth maps of the incomplete shape from a fixed set of viewpoints, and perform depth map completion in each view. Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view. This enables MVCN to leverage more information from different depth views to achieve high accuracy in single depth view completion and keep the consistency among the completed depth images in different views. Benefited by the multi-view representation and the novel network structure, MVCN significantly improves the accuracy of 3D shape completion in large-scale benchmarks compared to the state of the art.
Aggregation Cross-Entropy for Sequence Recognition
Authors: Zecheng Xie, Yaoxiong Huang, Yuanzhi Zhu, Lianwen Jin, Yuliang Liu, Lele Xie
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Prediction

In this paper, we propose a novel method, aggregation cross-entropy (ACE),
for sequence recognition from a brand new perspective. The ACE loss function
exhibits competitive performance to CTC and the attention mechanism, with much
quicker implementation (as it involves only four fundamental formulas), faster
inference\back-propagation (approximately O(1) in parallel), less storage
requirement (no parameter and negligible runtime memory), and convenient
employment (by replacing CTC with ACE). Furthermore, the proposed ACE loss
function exhibits two noteworthy properties: (1) it can be directly applied for
2D prediction by flattening the 2D prediction into 1D prediction as the input
and (2) it requires only characters and their numbers in the sequence
annotation for supervision, which allows it to advance beyond sequence
recognition, e.g., counting problem. The code is publicly available at
https://github.com/summerlvsong/Aggregation-Cross-Entropy.
Decoupled Data Based Approach for Learning to Control Nonlinear Dynamical Systems
Authors: Ran Wang, Karthikeya Parunandi, Dan Yu, Dileep Kalathil, Suman Chakravorty
Date: 2019-04-17 00:00:00
Tasks: Reinforcement Learning

This paper addresses the problem of learning the optimal control policy for a
nonlinear stochastic dynamical system with continuous state space, continuous
action space and unknown dynamics. This class of problems are typically
addressed in stochastic adaptive control and reinforcement learning literature
using model-based and model-free approaches respectively. Both methods rely on
solving a dynamic programming problem, either directly or indirectly, for
finding the optimal closed loop control policy. The inherent `curse of
dimensionality' associated with dynamic programming method makes these
approaches also computationally difficult.
  This paper proposes a novel decoupled data-based control (D2C) algorithm that
addresses this problem using a decoupled, `open loop - closed loop', approach.
First, an open-loop deterministic trajectory optimization problem is solved
using a black-box simulation model of the dynamical system. Then, a closed loop
control is developed around this open loop trajectory by linearization of the
dynamics about this nominal trajectory. By virtue of linearization, a linear
quadratic regulator based algorithm can be used for this closed loop control.
We show that the performance of D2C algorithm is approximately optimal.
Moreover, simulation performance suggests significant reduction in training
time compared to other state of the art algorithms.
Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures
Authors: Filipe Rodrigues, Carlos Lima Azevedo
Date: 2019-04-17 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Traffic Signal Control

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
Authors: Chen-Chou Lo, Szu-Wei Fu, Wen-Chin Huang, Xin Wang, Junichi Yamagishi, Yu Tsao, Hsin-Min Wang
Date: 2019-04-17 00:00:00
Tasks: Deep Learning, Voice Conversion

Existing objective evaluation metrics for voice conversion (VC) are not always correlated with human perception. Therefore, training VC models with such criteria may not effectively improve naturalness and similarity of converted speech. In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech. We adopt the convolutional and recurrent neural network models to build a mean opinion score (MOS) predictor, termed as MOSNet. The proposed models are tested on large-scale listening test results of the Voice Conversion Challenge (VCC) 2018. Experimental results show that the predicted scores of the proposed MOSNet are highly correlated with human MOS ratings at the system level while being fairly correlated with human MOS ratings at the utterance level. Meanwhile, we have modified MOSNet to predict the similarity scores, and the preliminary results show that the predicted scores are also fairly correlated with human ratings. These results confirm that the proposed models could be used as a computational evaluator to measure the MOS of VC systems to reduce the need for expensive human rating.
Question Guided Modular Routing Networks for Visual Question Answering
Authors: Yanze Wu, Qiang Sun, Jianqi Ma, Bin Li, Yanwei Fu, Yao Peng, xiangyang xue
Date: 2019-04-17 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

This paper studies the task of Visual Question Answering (VQA), which is topical in Multimedia community recently. Particularly, we explore two critical research problems existed in VQA: (1) efficiently fusing the visual and textual modalities; (2) enabling the visual reasoning ability of VQA models in answering complex questions. To address these challenging problems, a novel Question Guided Modular Routing Networks (QGMRN) has been proposed in this paper. Particularly, The QGMRN is composed of visual, textual and routing network. The visual and textual network serve as the backbones for the generic feature extractors of visual and textual modalities. QGMRN can fuse the visual and textual modalities at multiple semantic levels. Typically, the visual reasoning is facilitated by the routing network in a discrete and stochastic way by using Gumbel-Softmax trick for module selection. When the input reaches a certain modular layer, routing network newly proposed in this paper, dynamically selects a portion of modules from that layer to process the input depending on the question features generated by the textual network. It can also learn to reason by routing between the generic modules without additional supervision information or expert knowledge. Benefiting from the dynamic routing mechanism, QGMRN can outperform the previous classical VQA methods by a large margin and achieve the competitive results against the state-of-the-art methods. Furthermore, attention mechanism is integrated into our QGMRN model and thus can further boost the model performance. Empirically, extensive experiments on the CLEVR and CLEVR-Humans datasets validate the effectiveness of our proposed model, and the state-of-the-art performance has been achieved.
MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction
Authors: Oscar Araque, Lorenzo Gatti, Kyriaki Kalimeri
Date: 2019-04-17 00:00:00
Tasks: Decision Making, LEMMA, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in the text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on WordNet synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increased complexity, ranging from lemmas' statistical properties to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (p-value<0.01), and an average F1-Score of 86.25% over six different datasets. Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues.
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation
Authors: Gakuto Kurata, Kartik Audhkhasi
Date: 2019-04-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Decoder, Knowledge Distillation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Conventional automatic speech recognition (ASR) systems trained from frame-level alignments can easily leverage posterior fusion to improve ASR accuracy and build a better single model with knowledge distillation. End-to-end ASR systems trained using the Connectionist Temporal Classification (CTC) loss do not require frame-level alignment and hence simplify model training. However, sparse and arbitrary posterior spike timings from CTC models pose a new set of challenges in posterior fusion from multiple models and knowledge distillation between CTC models. We propose a method to train a CTC model so that its spike timings are guided to align with those of a pre-trained guiding CTC model. As a result, all models that share the same guiding model have aligned spike timings. We show the advantage of our method in various scenarios including posterior fusion of CTC models and knowledge distillation between CTC models with different architectures. With the 300-hour Switchboard training data, the single word CTC model distilled from multiple models improved the word error rates to 13.7%/23.1% from 14.9%/24.1% on the Hub5 2000 Switchboard/CallHome test sets without using any data augmentation, language model, or complex decoder.