pwc-archive/papers-with-abstracts huggingface dataset:
A Probabilistic framework for Quantum Clustering
Authors: Raúl V. Casaña-Eslava, Paulo J. G. Lisboa, Sandra Ortega-Martorell, Ian H. Jarman, José D. Martín-Guerrero
Date: 2019-02-14 00:00:00
Tasks: Clustering

Quantum Clustering is a powerful method to detect clusters in data with mixed
density. However, it is very sensitive to a length parameter that is inherent
to the Schr\"odinger equation. In addition, linking data points into clusters
requires local estimates of covariance that are also controlled by length
parameters. This raises the question of how to adjust the control parameters of
the Schr\"odinger equation for optimal clustering. We propose a probabilistic
framework that provides an objective function for the goodness-of-fit to the
data, enabling the control parameters to be optimised within a Bayesian
framework. This naturally yields probabilities of cluster membership and data
partitions with specific numbers of clusters. The proposed framework is tested
on real and synthetic data sets, assessing its validity by measuring
concordance with known data structure by means of the Jaccard score (JS). This
work also proposes an objective way to measure performance in unsupervised
learning that correlates very well with JS.
Fully Convolutional Networks for Text Classification
Authors: Jacob Anderson
Date: 2019-02-14 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

In this work I propose a new way of using fully convolutional networks for
classification while allowing for input of any size. I additionally propose two
modifications on the idea of attention and the benefits and detriments of using
the modifications. Finally, I show suboptimal results on the ITAmoji 2018 tweet
to emoji task and provide a discussion about why that might be the case as well
as a proposed fix to further improve results.
Tractography and machine learning: Current state and open challenges
Authors: Philippe Poulin, Daniel Jörgens, Pierre-Marc Jodoin, Maxime Descoteaux
Date: 2019-02-14 00:00:00
Tasks: BIG-bench Machine Learning

Supervised machine learning (ML) algorithms have recently been proposed as an
alternative to traditional tractography methods in order to address some of
their weaknesses. They can be path-based and local-model-free, and easily
incorporate anatomical priors to make contextual and non-local decisions that
should help the tracking process. ML-based techniques have thus shown promising
reconstructions of larger spatial extent of existing white matter bundles,
promising reconstructions of less false positives, and promising robustness to
known position and shape biases of current tractography techniques. But as of
today, none of these ML-based methods have shown conclusive performances or
have been adopted as a de facto solution to tractography. One reason for this
might be the lack of well-defined and extensive frameworks to train, evaluate,
and compare these methods.
  In this paper, we describe several datasets and evaluation tools that contain
useful features for ML algorithms, along with the various methods proposed in
the recent years. We then discuss the strategies that are used to evaluate and
compare those methods, as well as their shortcomings. Finally, we describe the
particular needs of ML tractography methods and discuss tangible solutions for
future works.
Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions
Authors: Necdet Serhat Aybat, Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar
Date: 2018-05-27 00:00:00

We study the trade-offs between convergence rate and robustness to gradient errors in designing a first-order algorithm. We focus on gradient descent (GD) and accelerated gradient (AG) methods for minimizing strongly convex functions when the gradient has random errors in the form of additive white noise. With gradient errors, the function values of the iterates need not converge to the optimal value; hence, we define the robustness of an algorithm to noise as the asymptotic expected suboptimality of the iterate sequence to input noise power. For this robustness measure, we provide exact expressions for the quadratic case using tools from robust control theory and tight upper bounds for the smooth strongly convex case using Lyapunov functions certified through matrix inequalities. We use these characterizations within an optimization problem which selects parameters of each algorithm to achieve a particular trade-off between rate and robustness. Our results show that AG can achieve acceleration while being more robust to random gradient errors. This behavior is quite different than previously reported in the deterministic gradient noise setting. We also establish some connections between the robustness of an algorithm and how quickly it can converge back to the optimal solution if it is perturbed from the optimal point with deterministic noise. Our framework also leads to practical algorithms that can perform better than other state-of-the-art methods in the presence of random gradient noise.
Off-Policy Actor-Critic in an Ensemble: Achieving Maximum General Entropy and Effective Environment Exploration in Deep Reinforcement Learning
Authors: Gang Chen, Yiming Peng
Date: 2019-02-14 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We propose a new policy iteration theory as an important extension of soft
policy iteration and Soft Actor-Critic (SAC), one of the most efficient model
free algorithms for deep reinforcement learning. Supported by the new theory,
arbitrary entropy measures that generalize Shannon entropy, such as Tsallis
entropy and Renyi entropy, can be utilized to properly randomize action
selection while fulfilling the goal of maximizing expected long-term rewards.
Our theory gives birth to two new algorithms, i.e., Tsallis entropy
Actor-Critic (TAC) and Renyi entropy Actor-Critic (RAC). Theoretical analysis
shows that these algorithms can be more effective than SAC. Moreover, they pave
the way for us to develop a new Ensemble Actor-Critic (EAC) algorithm in this
paper that features the use of a bootstrap mechanism for deep environment
exploration as well as a new value-function based mechanism for high-level
action selection. Empirically we show that TAC, RAC and EAC can achieve
state-of-the-art performance on a range of benchmark control tasks,
outperforming SAC and several cutting-edge learning algorithms in terms of both
sample efficiency and effectiveness.
Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond
Authors: Naimul Mefraz Khan, Nabila Abraham, Ling Guan
Date: 2019-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Segmentation, Transfer Learning

In this paper, we highlight three issues that limit performance of machine
learning on biomedical images, and tackle them through 3 case studies: 1)
Interactive Machine Learning (IML): we show how IML can drastically improve
exploration time and quality of direct volume rendering. 2) transfer learning:
we show how transfer learning along with intelligent pre-processing can result
in better Alzheimer's diagnosis using a much smaller training set 3) data
imbalance: we show how our novel focal Tversky loss function can provide better
segmentation results taking into account the imbalanced nature of segmentation
datasets. The case studies are accompanied by in-depth analytical discussion of
results with possible future directions.
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems
Authors: Lixin Zou, Long Xia, Zhuoye Ding, Jiaxing Song, Weidong Liu, Dawei Yin
Date: 2019-02-13 00:00:00
Tasks: Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has been widely used in the recommender system, especially on the mobile Apps. The feed streaming setting provides users the interactive manner of recommendation in never-ending feeds. In such an interactive manner, a good recommender system should pay more attention to user stickiness, which is far beyond classical instant metrics, and typically measured by {\bf long-term user engagement}. Directly optimizing the long-term user engagement is a non-trivial problem, as the learning target is usually not available for conventional supervised learning methods. Though reinforcement learning~(RL) naturally fits the problem of maximizing the long term rewards, applying RL to optimize long-term user engagement is still facing challenges: user behaviors are versatile and difficult to model, which typically consists of both instant feedback~(e.g. clicks, ordering) and delayed feedback~(e.g. dwell time, revisit); in addition, performing effective off-policy learning is still immature, especially when combining bootstrapping and function approximation. To address these issues, in this work, we introduce a reinforcement learning framework --- FeedRec to optimize the long-term user engagement. FeedRec includes two components: 1)~a Q-Network which designed in hierarchical LSTM takes charge of modeling complex user behaviors, and 2)~an S-Network, which simulates the environment, assists the Q-Network and voids the instability of convergence in policy learning. Extensive experiments on synthetic data and a real-world large scale data show that FeedRec effectively optimizes the long-term user engagement and outperforms state-of-the-arts.
Interest-Related Item Similarity Model Based on Multimodal Data for Top-N Recommendation
Authors: Junmei Lv, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani
Date: 2019-02-13 00:00:00
Tasks: Recommendation Systems

Nowadays, the recommendation systems are applied in the fields of e-commerce,
video websites, social networking sites, etc., which bring great convenience to
people's daily lives. The types of the information are diversified and abundant
in recommendation systems, therefore the proportion of unstructured multimodal
data like text, image and video is increasing. However, due to the
representation gap between different modalities, it is intractable to
effectively use unstructured multimodal data to improve the efficiency of
recommendation systems. In this paper, we propose an end-to-end Multimodal
Interest-Related Item Similarity model (Multimodal IRIS) to provide
recommendations based on multimodal data source. Specifically, the Multimodal
IRIS model consists of three modules, i.e., multimodal feature learning module,
the Interest-Related Network (IRN) module and item similarity recommendation
module. The multimodal feature learning module adds knowledge sharing unit
among different modalities. Then IRN learn the interest relevance between
target item and different historical items respectively. At last, the
multimodal data feature learning, IRN and item similarity recommendation
modules are unified into an integrated system to achieve performance
enhancements and to accommodate the addition or absence of different modal
data. Extensive experiments on real-world datasets show that, by dealing with
the multimodal data which people may pay more attention to when selecting
items, the proposed Multimodal IRIS significantly improves accuracy and
interpretability on top-N recommendation task over the state-of-the-art
methods.
Sparse and noisy LiDAR completion with RGB guidance anduncertainty
Authors: Wouter Van Gansbeke, Davy Neven, Bert de Brabandere, Luc van Gool
Date: 2019-02-14 00:00:00
Proceeding: arXiv 2019 2
Tasks: Autonomous Vehicles, Depth Completion, Depth Estimation, Depth Prediction

his work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images.  For autonomous  vehicles  and  robotics  the  use  of  LiDAR is  indispensable  in  order  to  achieve  precise  depth  predictions.   A  multitude  of  applications  depend  on  the awareness  of  their  surroundings,  and  use  depth  cues to  reason  and  react  accordingly.    On  the  one  hand, monocular depth prediction methods fail to generate absolute  and  precise  depth  maps.    On  the  other  hand, stereoscopic  approaches  are  still  significantly  outperformed  by  LiDAR  based  approaches.   The  goal  of  the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped  to  a  2D  plane.  We  propose  a  new  framework which extracts both global and local information in order to produce proper depth maps.  We argue that simple  depth  completion  does  not  require  a  deep  network. However, we additionally propose a fusion method with RGB  guidance  from  a  monocular  camera  in  order  to leverage  object  information  and  to  correct  mistakes  in the  sparse  input.   This  improves  the  accuracy  significantly.   Moreover,  confidence  masks  are  exploited  in order to take into account the uncertainty in the depth predictions  from  each  modality.    This  fusion  method outperforms  the  state-of-the-art  and  ranks  first  on  the KITTI  depth  completion  benchmark.
The Optimal Approximation Factor in Density Estimation
Authors: Olivier Bousquet, Daniel Kane, Shay Moran
Date: 2019-02-10 00:00:00
Tasks: Density Estimation

Consider the following problem: given two arbitrary densities $q_1,q_2$ and a sample-access to an unknown target density $p$, find which of the $q_i$'s is closer to $p$ in total variation. A remarkable result due to Yatracos shows that this problem is tractable in the following sense: there exists an algorithm that uses $O(\epsilon^{-2})$ samples from $p$ and outputs~$q_i$ such that with high probability, $TV(q_i,p) \leq 3\cdot\mathsf{opt} + \epsilon$, where $\mathsf{opt}= \min\{TV(q_1,p),TV(q_2,p)\}$. Moreover, this result extends to any finite class of densities $\mathcal{Q}$: there exists an algorithm that outputs the best density in $\mathcal{Q}$ up to a multiplicative approximation factor of 3. We complement and extend this result by showing that: (i) the factor 3 can not be improved if one restricts the algorithm to output a density from $\mathcal{Q}$, and (ii) if one allows the algorithm to output arbitrary densities (e.g.\ a mixture of densities from $\mathcal{Q}$), then the approximation factor can be reduced to 2, which is optimal. In particular this demonstrates an advantage of improper learning over proper in this setup. We develop two approaches to achieve the optimal approximation factor of 2: an adaptive one and a static one. Both approaches are based on a geometric point of view of the problem and rely on estimating surrogate metrics to the total variation. Our sample complexity bounds exploit techniques from {\it Adaptive Data Analysis}.
Sentiment Analysis by Capsules
Authors: Yequan Wang, Aixin Sun, Jialong Han, Ying Liu, Xiaoyan Zhu
Date: 2018-02-01 00:00:00
Proceeding: The Web Conference (WWW) 2018 2
Tasks: Attribute, Sentiment Analysis, Sentiment Classification, Specificity

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a given problem, one capsule is built for each sentiment category e.g., ‘positive’ and ‘negative’. Each capsule has an attribute, a state, and three modules: representation module, probability module, and reconstruction module. The attribute of a capsule is the assigned sentiment category. Given an instance encoded in hidden vectors by a typical RNN, the representation module builds capsule representation by the attention mechanism. Based on capsule representation, the probability module computes the capsule’s state probability. A capsule’s state is active if its state probability is the largest among all capsules for the given instance, and inactive otherwise. On two benchmark datasets (i.e., Movie Review and Stanford Sentiment Treebank) and one proprietary dataset (i.e., Hospital Feedback), we show that RNN-Capsule achieves state-of-the-art performance on sentiment classification. More importantly, without using any linguistic knowledge, RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules’ attributes. The words well reflect the domain specificity of the dataset.
A Monte Carlo AIXI Approximation
Authors: Joel Veness, Kee Siong Ng, Marcus Hutter, William Uther, David Silver
Date: 2009-09-04 00:00:00
Tasks: General Reinforcement Learning, Open-Ended Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. Our approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the design of practical algorithms. We answer this hitherto open
question in the affirmative, by providing the first computationally feasible
approximation to the AIXI agent. To develop our approximation, we introduce a
new Monte-Carlo Tree Search algorithm along with an agent-specific extension to
the Context Tree Weighting algorithm. Empirically, we present a set of
encouraging results on a variety of stochastic and partially observable
domains. We conclude by proposing a number of directions for future research.
Contextual Encoder-Decoder Network for Visual Saliency Prediction
Authors: Alexander Kroner, Mario Senden, Kurt Driessens, Rainer Goebel
Date: 2019-02-18 00:00:00
Tasks: Decoder, General Classification, image-classification, Image Classification, Prediction, Saliency Prediction, Video Saliency Detection

Predicting salient regions in natural images requires the detection of objects that are present in a scene. To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information. However, existing models aimed at explaining human fixation maps do not incorporate such a mechanism explicitly. Here we propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. The architecture forms an encoder-decoder structure and includes a module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, we combine the resulting representations with global scene information for accurately predicting visual saliency. Our model achieves competitive and consistent results across multiple evaluation metrics on two public saliency benchmarks and we demonstrate the effectiveness of the suggested approach on five datasets and selected examples. Compared to state of the art approaches, the network is based on a lightweight image classification backbone and hence presents a suitable choice for applications with limited computational resources, such as (virtual) robotic systems, to estimate human fixations across complex natural scenes.
R$^2$-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images
Authors: Jiangmiao Pang, Cong Li, Jianping Shi, Zhihai Xu, Huajun Feng
Date: 2019-02-16 00:00:00
Tasks: object-detection, Object Detection

Recently, the convolutional neural network has brought impressive
improvements for object detection. However, detecting tiny objects in
large-scale remote sensing images still remains challenging. First, the extreme
large input size makes the existing object detection solutions too slow for
practical use. Second, the massive and complex backgrounds cause serious false
alarms. Moreover, the ultratiny objects increase the difficulty of accurate
detection. To tackle these problems, we propose a unified and self-reinforced
network called remote sensing region-based convolutional neural network
($\mathcal{R}^2$-CNN), composing of backbone Tiny-Net, intermediate global
attention block, and final classifier and detector. Tiny-Net is a lightweight
residual structure, which enables fast and powerful features extraction from
inputs. Global attention block is built upon Tiny-Net to inhibit false
positives. Classifier is then used to predict the existence of targets in each
patch, and detector is followed to locate them accurately if available. The
classifier and detector are mutually reinforced with end-to-end training, which
further speed up the process and avoid false alarms. Effectiveness of
$\mathcal{R}^2$-CNN is validated on hundreds of GF-1 images and GF-2 images
that are 18 000 $\times$ 18 192 pixels, 2.0-m resolution, and 27 620 $\times$
29 200 pixels, 0.8-m resolution, respectively. Specifically, we can process a
GF-1 image in 29.4 s on Titian X just with single thread. According to our
knowledge, no previous solution can detect the tiny object on such huge remote
sensing images gracefully. We believe that it is a significant step toward
practical real-time remote sensing systems.
Min-Entropy Latent Model for Weakly Supervised Object Detection
Authors: Fang Wan, Pengxu Wei, Zhenjun Han, Jianbin Jiao, Qixiang Ye
Date: 2019-02-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Image Classification, Object, object-detection, Object Detection, Object Localization, Weakly Supervised Object Detection, Weakly-Supervised Object Localization

Weakly supervised object detection is a challenging task when provided with
image category supervision but required to learn, at the same time, object
locations and object detectors. The inconsistency between the weak supervision
and learning objectives introduces significant randomness to object locations
and ambiguity to detectors. In this paper, a min-entropy latent model (MELM) is
proposed for weakly supervised object detection. Min-entropy serves as a model
to learn object locations and a metric to measure the randomness of object
localization during learning. It aims to principally reduce the variance of
learned instances and alleviate the ambiguity of detectors. MELM is decomposed
into three components including proposal clique partition, object clique
discovery, and object localization. MELM is optimized with a recurrent learning
algorithm, which leverages continuation optimization to solve the challenging
non-convexity problem. Experiments demonstrate that MELM significantly improves
the performance of weakly supervised object detection, weakly supervised object
localization, and image classification, against the state-of-the-art
approaches.
RES-SE-NET: Boosting Performance of Resnets by Enhancing Bridge-connections
Authors: Varshaneya V, Balasubramanian S, Darshan Gera
Date: 2019-02-16 00:00:00

One of the ways to train deep neural networks effectively is to use residual
connections. Residual connections can be classified as being either identity
connections or bridge-connections with a reshaping convolution. Empirical
observations on CIFAR-10 and CIFAR-100 datasets using a baseline Resnet model,
with bridge-connections removed, have shown a significant reduction in
accuracy. This reduction is due to lack of contribution, in the form of feature
maps, by the bridge-connections. Hence bridge-connections are vital for Resnet.
However, all feature maps in the bridge-connections are considered to be
equally important. In this work, an upgraded architecture "Res-SE-Net" is
proposed to further strengthen the contribution from the bridge-connections by
quantifying the importance of each feature map and weighting them accordingly
using Squeeze-and-Excitation (SE) block. It is demonstrated that Res-SE-Net
generalizes much better than Resnet and SE-Resnet on the benchmark CIFAR-10 and
CIFAR-100 datasets.