pwc-archive/papers-with-abstracts huggingface dataset:
3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models
Authors: Daniel Koguciuk, Łukasz Chechliński, Tarek El-Gaaly
Date: 2019-04-17 00:00:00
Tasks: 3D Classification, 3D Object Detection, 3D Object Recognition, Ensemble Learning, General Classification, object-detection, Object Detection, Object Recognition

In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification and detection. An ensemble of multiple model instances is known to outperform a single model instance, but there is little study of the topic of ensemble learning for 3D point clouds. First, an ensemble of multiple model instances trained on the same part of the $\textit{ModelNet40}$ dataset was tested for seven deep learning, point cloud-based classification algorithms: $\textit{PointNet}$, $\textit{PointNet++}$, $\textit{SO-Net}$, $\textit{KCNet}$, $\textit{DeepSets}$, $\textit{DGCNN}$, and $\textit{PointCNN}$. Second, the ensemble of different architectures was tested. Results of our experiments show that the tested ensemble learning methods improve over state-of-the-art on the $\textit{ModelNet40}$ dataset, from $92.65\%$ to $93.64\%$ for the ensemble of single architecture instances, $94.03\%$ for two different architectures, and $94.15\%$ for five different architectures. We show that the ensemble of two models with different architectures can be as effective as the ensemble of 10 models with the same architecture. Third, a study on classic bagging i.e. with different subsets used for training multiple model instances) was tested and sources of ensemble accuracy growth were investigated for best-performing architecture, i.e. $\textit{SO-Net}$. We also investigate the ensemble learning of $\textit{Frustum PointNet}$ approach in the task of 3D object detection, increasing the average precision of 3D box detection on the $\textit{KITTI}$ dataset from $63.1\%$ to $66.5\%$ using only three model instances. We measure the inference time of all 3D classification architectures on a $\textit{Nvidia Jetson TX2}$, a common embedded computer for mobile robots, to allude to the use of these models in real-life applications.
Compositional Network Embedding
Authors: Tianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang
Date: 2019-04-17 00:00:00
Tasks: Attribute, Link Prediction, Network Embedding, Node Classification, Representation Learning

Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the existing methods from being applied in real cases. Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem. The heterogeneous network usually requires extra work to encode node types, as node type is not able to be identified by node ID. Node ID carries rare information, resulting in the criticism that the existing methods are not robust to noise. To address this issue, we introduce Compositional Network Embedding, a general inductive network representation learning framework that generates node embeddings by combining node features based on the principle of compositionally. Instead of directly optimizing an embedding lookup based on arbitrary node IDs, we learn a composition function that infers node embeddings by combining the corresponding node attribute embeddings through a graph-based loss. For evaluation, we conduct the experiments on link prediction under four different settings. The results verified the effectiveness and generalization ability of compositional network embeddings, especially on unseen nodes.
Deep learning investigation for chess player attention prediction using eye-tracking and game data
Authors: Justin Le Louedec, Thomas Guntz, James Crowley, Dominique Vaufreydaz
Date: 2019-04-17 00:00:00
Tasks: Decoder

This article reports on an investigation of the use of convolutional neural
networks to predict the visual attention of chess players. The visual attention
model described in this article has been created to generate saliency maps that
capture hierarchical and spatial features of chessboard, in order to predict
the probability fixation for individual pixels Using a skip-layer architecture
of an autoencoder, with a unified decoder, we are able to use multiscale
features to predict saliency of part of the board at different scales, showing
multiple relations between pieces. We have used scan path and fixation data
from players engaged in solving chess problems, to compute 6600 saliency maps
associated to the corresponding chess piece configurations. This corpus is
completed with synthetically generated data from actual games gathered from an
online chess platform. Experiments realized using both scan-paths from chess
players and the CAT2000 saliency dataset of natural images, highlights several
results. Deep features, pretrained on natural images, were found to be helpful
in training visual attention prediction for chess. The proposed neural network
architecture is able to generate meaningful saliency maps on unseen chess
configurations with good scores on standard metrics. This work provides a
baseline for future work on visual attention prediction in similar contexts.
Bayesian policy selection using active inference
Authors: Ozan Çatal, Johannes Nauta, Tim Verbelen, Pieter Simoens, Bart Dhoedt
Date: 2019-04-17 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Learning to take actions based on observations is a core requirement for
artificial agents to be able to be successful and robust at their task.
Reinforcement Learning (RL) is a well-known technique for learning such
policies. However, current RL algorithms often have to deal with reward
shaping, have difficulties generalizing to other environments and are most
often sample inefficient. In this paper, we explore active inference and the
free energy principle, a normative theory from neuroscience that explains how
self-organizing biological systems operate by maintaining a model of the world
and casting action selection as an inference problem. We apply this concept to
a typical problem known to the RL community, the mountain car problem, and show
how active inference encompasses both RL and learning from demonstrations.
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
Authors: Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, Woo Youn Kim
Date: 2019-04-17 00:00:00
Tasks: Drug Design, Graph Attention, Pose Prediction, Prediction

Accurate prediction of drug-target interaction (DTI) is essential for in
silico drug design. For the purpose, we propose a novel approach for predicting
DTI using a GNN that directly incorporates the 3D structure of a protein-ligand
complex. We also apply a distance-aware graph attention algorithm with gate
augmentation to increase the performance of our model. As a result, our model
shows better performance than docking and other deep learning methods for both
virtual screening and pose prediction. In addition, our model can reproduce the
natural population distribution of active molecules and inactive molecules.
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis
Authors: Feiyang Chen, Ziqian Luo, Yanyan Xu, Dengfeng Ke
Date: 2019-04-17 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Multimodal Sentiment Analysis, Sentiment Analysis

Sentiment analysis, mostly based on text, has been rapidly developing in the last decade and has attracted widespread attention in both academia and industry. However, the information in the real world usually comes from multiple modalities, such as audio and text. Therefore, in this paper, based on audio and text, we consider the task of multimodal sentiment analysis and propose a novel fusion strategy including both multi-feature fusion and multi-modality fusion to improve the accuracy of audio-text sentiment analysis. We call it the DFF-ATMF (Deep Feature Fusion - Audio and Text Modality Fusion) model, which consists of two parallel branches, the audio modality based branch and the text modality based branch. Its core mechanisms are the fusion of multiple feature vectors and multiple modality attention. Experiments on the CMU-MOSI dataset and the recently released CMU-MOSEI dataset, both collected from YouTube for sentiment analysis, show the very competitive results of our DFF-ATMF model. Furthermore, by virtue of attention weight distribution heatmaps, we also demonstrate the deep features learned by using DFF-ATMF are complementary to each other and robust. Surprisingly, DFF-ATMF also achieves new state-of-the-art results on the IEMOCAP dataset, indicating that the proposed fusion strategy also has a good generalization ability for multimodal emotion recognition.
Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning
Authors: Yuji Kanagawa, Tomoyuki Kaneko
Date: 2019-04-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose Rogue-Gym, a simple and classic style roguelike game built for evaluating generalization in reinforcement learning (RL). Combined with the recent progress of deep neural networks, RL has successfully trained human-level agents without human knowledge in many games such as those for Atari 2600. However, it has been pointed out that agents trained with RL methods often overfit the training environment, and they work poorly in slightly different environments. To investigate this problem, some research environments with procedural content generation have been proposed. Following these studies, we propose the use of roguelikes as a benchmark for evaluating the generalization ability of RL agents. In our Rogue-Gym, agents need to explore dungeons that are structured differently each time they start a new game. Thanks to the very diverse structures of the dungeons, we believe that the generalization benchmark of Rogue-Gym is sufficiently fair. In our experiments, we evaluate a standard reinforcement learning method, PPO, with and without enhancements for generalization. The results show that some enhancements believed to be effective fail to mitigate the overfitting in Rogue-Gym, although others slightly improve the generalization ability.
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
Authors: Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein
Date: 2019-04-17 00:00:00
Tasks: Deep Learning, Image Segmentation, Medical Image Segmentation, scientific discovery, Segmentation, Semantic Segmentation

Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning. While semantic segmentation algorithms enable 3D image analysis and quantification in many applications, the design of respective specialised solutions is non-trivial and highly dependent on dataset properties and hardware conditions. We propose nnU-Net, a deep learning framework that condenses the current domain knowledge and autonomously takes the key decisions required to transfer a basic architecture to different datasets and segmentation tasks. Without manual tuning, nnU-Net surpasses most specialised deep learning pipelines in 19 public international competitions and sets a new state of the art in the majority of the 49 tasks. The results demonstrate a vast hidden potential in the systematic adaptation of deep learning methods to different datasets. We make nnU-Net publicly available as an open-source tool that can effectively be used out-of-the-box, rendering state of the art segmentation accessible to non-experts and catalyzing scientific progress as a framework for automated method design.
Explainability in Human-Agent Systems
Authors: Avi Rosenfeld, Ariella Richardson
Date: 2019-04-17 00:00:00

This paper presents a taxonomy of explainability in Human-Agent Systems. We
consider fundamental questions about the Why, Who, What, When and How of
explainability. First, we define explainability, and its relationship to the
related terms of interpretability, transparency, explicitness, and
faithfulness. These definitions allow us to answer why explainability is needed
in the system, whom it is geared to and what explanations can be generated to
meet this need. We then consider when the user should be presented with this
information. Last, we consider how objective and subjective measures can be
used to evaluate the entire system. This last question is the most encompassing
as it will need to evaluate all other issues regarding explainability.
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
Authors: Jingwen He, Chao Dong, Yu Qiao
Date: 2019-04-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of few additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.
A Survey on Traffic Signal Control Methods
Authors: Hua Wei, Guanjie Zheng, Vikash Gayah, Zhenhui Li
Date: 2019-04-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey, Traffic Signal Control

Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now have richer data, more computing power and advanced methods to drive the development of intelligent transportation. With the growing interest in intelligent transportation using machine learning methods like reinforcement learning, this survey covers the widely acknowledged transportation approaches and a comprehensive list of recent literature on reinforcement for traffic signal control. We hope this survey can foster interdisciplinary research on this important topic.
Contextual Aware Joint Probability Model Towards Question Answering System
Authors: Liu Yang, Lijing Song
Date: 2019-04-17 00:00:00
Tasks: Question Answering, Word Embeddings

In this paper, we address the question answering challenge with the SQuAD 2.0
dataset. We design a model architecture which leverages BERT's capability of
context-aware word embeddings and BiDAF's context interactive exploration
mechanism. By integrating these two state-of-the-art architectures, our system
tries to extract the contextual word representation at word and character
levels, for better comprehension of both question and context and their
correlations. We also propose our original joint posterior probability
predictor module and its associated loss functions. Our best model so far
obtains F1 score of 75.842% and EM score of 72.24% on the test PCE leaderboad.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
Authors: Jee-weon Jung, Hee-Soo Heo, Ju-ho Kim, Hye-jin Shim, Ha-Jin Yu
Date: 2019-04-17 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Speaker Verification, Text-Independent Speaker Verification

Recently, direct modeling of raw waveforms using deep neural networks has been widely studied for a number of tasks in audio domains. In speaker verification, however, utilization of raw waveforms is in its preliminary phase, requiring further investigation. In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification. Adjustment of model architecture using a pre-training scheme can extract speaker embeddings, giving a significant improvement in performance. Additional objective functions simplify the process of extracting speaker embeddings by merging conventional two-phase processes: extracting utterance-level features such as i-vectors or x-vectors and the feature enhancement phase, e.g., linear discriminant analysis. Effective back-end classification models that suit the proposed speaker embedding are also explored. We propose an end-to-end system that comprises two deep neural networks, one front-end for utterance-level speaker embedding extraction and the other for back-end classification. Experiments conducted on the VoxCeleb1 dataset demonstrate that the proposed model achieves state-of-the-art performance among systems without data augmentation. The proposed system is also comparable to the state-of-the-art x-vector system that adopts data augmentation.
Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
Authors: Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue
Date: 2019-04-17 00:00:00
Tasks: Bayesian Optimization, Experimental Design

In many high-throughput experimental design settings, such as those common in
biochemical engineering, batched queries are more cost effective than
one-by-one sequential queries. Furthermore, it is often not possible to
directly choose items to query. Instead, the experimenter specifies a set of
constraints that generates a library of possible items, which are then selected
stochastically. Motivated by these considerations, we investigate \emph{Batched
Stochastic Bayesian Optimization} (BSBO), a novel Bayesian optimization scheme
for choosing the constraints in order to guide exploration towards items with
greater utility. We focus on \emph{site-saturation mutagenesis}, a prototypical
setting of BSBO in biochemical engineering, and propose a natural objective
function for this problem. Importantly, we show that our objective function can
be efficiently decomposed as a difference of submodular functions (DS), which
allows us to employ DS optimization tools to greedily identify sets of
constraints that increase the likelihood of finding items with high utility.
Our experimental results show that our algorithm outperforms common heuristics
on both synthetic and two real protein datasets.
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
Authors: Lei Lei, Jiaju Qi, Kan Zheng
Date: 2019-04-17 00:00:00
Tasks: Clustering, Information Retrieval, Retrieval, Sentence

The number of approved patents worldwide increases rapidly each year, which
requires new patent analytics to efficiently mine the valuable information
attached to these patents. Vector space model (VSM) represents documents as
high-dimensional vectors, where each dimension corresponds to a unique term.
While originally proposed for information retrieval systems, VSM has also seen
wide applications in patent analytics, and used as a fundamental tool to map
patent documents to structured data. However, VSM method suffers from several
limitations when applied to patent analysis tasks, such as loss of
sentence-level semantics and curse-of-dimensionality problems. In order to
address the above limitations, we propose a patent analytics based on feature
vector space model (FVSM), where the FVSM is constructed by mapping patent
documents to feature vectors extracted by convolutional neural networks (CNN).
The applications of FVSM for three typical patent analysis tasks, i.e., patents
similarity comparison, patent clustering, and patent map generation are
discussed. A case study using patents related to Internet of Things (IoT)
technology is illustrated to demonstrate the performance and effectiveness of
FVSM. The proposed FVSM can be adopted by other patent analysis studies to
replace VSM, based on which various big data learning tasks can be performed.
Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification
Authors: Qiang Li, Bo Xie, Jane You, Wei Bian, DaCheng Tao
Date: 2019-04-17 00:00:00
Tasks: Classification, feature selection, General Classification, image-classification, Image Classification

In this paper, we present correlated logistic (CorrLog) model for multilabel
image classification. CorrLog extends conventional logistic regression model
into multilabel cases, via explicitly modeling the pairwise correlation between
labels. In addition, we propose to learn the model parameters of CorrLog with
elastic net regularization, which helps exploit the sparsity in feature
selection and label correlations and thus further boost the performance of
multilabel classification. CorrLog can be efficiently learned, though
approximately, by regularized maximum pseudo likelihood estimation, and it
enjoys a satisfying generalization bound that is independent of the number of
labels. CorrLog performs competitively for multilabel image classification on
benchmark data sets MULAN scene, MIT outdoor scene, PASCAL VOC 2007, and PASCAL
VOC 2012, compared with the state-of-the-art multilabel classification
algorithms.
TextCaps : Handwritten Character Recognition with Very Small Datasets
Authors: Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Jathushan Rajasegaran, Suranga Seneviratne, Ranga Rodrigo
Date: 2019-04-17 00:00:00
Tasks: Few-Shot Image Classification, Image Classification, Image Generation

Many localized languages struggle to reap the benefits of recent advancements
in character recognition systems due to the lack of substantial amount of
labeled training data. This is due to the difficulty in generating large
amounts of labeled data for such languages and inability of deep learning
techniques to properly learn from small number of training samples. We solve
this problem by introducing a technique of generating new training samples from
the existing samples, with realistic augmentations which reflect actual
variations that are present in human hand writing, by adding random controlled
noise to their corresponding instantiation parameters. Our results with a mere
200 training samples per class surpass existing character recognition results
in the EMNIST-letter dataset while achieving the existing results in the three
datasets: EMNIST-balanced, EMNIST-digits, and MNIST. We also develop a strategy
to effectively use a combination of loss functions to improve reconstructions.
Our system is useful in character recognition for localized languages that lack
much labeled training data and even in other related more general contexts such
as object recognition.