pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
Authors: Hao Tang, Dan Xu, Nicu Sebe, Yanzhi Wang, Jason J. Corso, Yan Yan
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Bird View Synthesis, Cross-View Image-to-Image Translation, Image-to-Image Translation, Translation

Cross-view image translation is challenging because it involves images with
drastically different views and severe deformation. In this paper, we propose a
novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that
makes it possible to generate images of natural scenes in arbitrary viewpoints,
based on an image of the scene and a novel semantic map. The proposed
SelectionGAN explicitly utilizes the semantic information and consists of two
stages. In the first stage, the condition image and the target semantic map are
fed into a cycled semantic-guided generation network to produce initial coarse
results. In the second stage, we refine the initial results by using a
multi-channel attention selection mechanism. Moreover, uncertainty maps
automatically learned from attentions are used to guide the pixel loss for
better network optimization. Extensive experiments on Dayton, CVUSA and Ego2Top
datasets show that our model is able to generate significantly better results
than the state-of-the-art methods. The source code, data and trained models are
available at https://github.com/Ha0Tang/SelectionGAN.
Universal Bounding Box Regression and Its Applications
Authors: Seungkwan Lee, Suha Kwak, Minsu Cho
Date: 2019-04-15 00:00:00
Tasks: Object, object-detection, Object Detection, Object Discovery, regression, Weakly Supervised Object Detection

Bounding-box regression is a popular technique to refine or predict
localization boxes in recent object detection approaches. Typically,
bounding-box regressors are trained to regress from either region proposals or
fixed anchor boxes to nearby bounding boxes of a pre-defined target object
classes. This paper investigates whether the technique is generalizable to
unseen classes and is transferable to other tasks beyond supervised object
detection. To this end, we propose a class-agnostic and anchor-free box
regressor, dubbed Universal Bounding-Box Regressor (UBBR), which predicts a
bounding box of the nearest object from any given box. Trained on a relatively
small set of annotated images, UBBR successfully generalizes to unseen classes,
and can be used to improve localization in many vision problems. We demonstrate
its effectivenss on weakly supervised object detection and object discovery.
Multi-Branch Tensor Network Structure for Tensor-Train Discriminant Analysis
Authors: Seyyid Emre Sofuoglu, Selin Aviyente
Date: 2019-04-15 00:00:00
Tasks: Classification, General Classification, Tensor Decomposition, Video Classification

Higher-order data with high dimensionality arise in a diverse set of application areas such as computer vision, video analytics and medical imaging. Tensors provide a natural tool for representing these types of data. Although there has been a lot of work in the area of tensor decomposition and low-rank tensor approximation, extensions to supervised learning, feature extraction and classification are still limited. Moreover, most of the existing supervised tensor learning approaches are based on the orthogonal Tucker model. However, this model has some limitations for large tensors including high memory and computational costs. In this paper, we introduce a supervised learning approach for tensor classification based on the tensor-train model. In particular, we introduce a multi-branch tensor network structure for efficient implementation of tensor-train discriminant analysis (TTDA). The proposed approach takes advantage of the flexibility of the tensor train structure to implement various computationally efficient versions of TTDA. This approach is then evaluated on image and video classification tasks with respect to computation time, storage cost and classification accuracy and is compared to both vector and tensor based discriminant analysis methods.
Curious iLQR: Resolving Uncertainty in Model-based RL
Authors: Sarah Bechtle, Yixin Lin, Akshara Rai, Ludovic Righetti, Franziska Meier
Date: 2019-04-15 00:00:00
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Curiosity as a means to explore during reinforcement learning problems has recently become very popular. However, very little progress has been made in utilizing curiosity for learning control. In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty. During trajectory optimization the curious iLQR attempts to minimize both the task-dependent cost and the uncertainty in the dynamics model. We demonstrate the approach on reaching tasks with 7-DoF manipulators in simulation and on a real robot. Our experiments show that MBRL with curious iLQR reaches desired end-effector targets more reliably and with less system rollouts when learning a new task from scratch, and that the learned model generalizes better to new reaching tasks.
No Adjective Ordering Mystery, and No Raven Paradox, Just an Ontological Mishap
Authors: Walid S. Saba
Date: 2019-04-14 00:00:00

In the concluding remarks of Ontological Promiscuity Hobbs (1985) made what
we believe to be a very insightful observation: given that semantics is an
attempt at specifying the relation between language and the world, if "one can
assume a theory of the world that is isomorphic to the way we talk about it ...
then semantics becomes nearly trivial". But how exactly can we rectify our
logical formalisms so that semantics, an endeavor that has occupied the most
penetrating minds for over two centuries, can become (nearly) trivial, and what
exactly does it mean to assume a theory of the world in our semantics? In this
paper we hope to provide answers for both questions. First, we believe that a
commonsense theory of the world can (and should) be embedded in our semantic
formalisms resulting in a logical semantics grounded in commonsense
metaphysics. Moreover, we believe the first step to accomplishing this vision
is rectifying what we think was a crucial oversight in logical semantics,
namely the failure to distinguish between two fundamentally different types of
concepts: (i) ontological concepts, that correspond to what Cocchiarella (2001)
calls first-intension concepts and are types in a strongly-typed ontology; and
(ii) logical concepts (or second intension concepts), that are predicates
corresponding to properties of (and relations between) objects of various
ontological types1. In such a framework, which we will refer to henceforth by
ontologik, it will be shown how type unification and other type operations can
be used to account for the `missing text phenomenon' (MTP) (see Saba, 2019a)
that is at the heart of most challenges in the semantics of natural language,
by uncovering the significant amount of missing text that is never explicitly
stated in everyday discourse, but is often implicitly assumed as shared
background knowledge.
See the World through Network Cameras
Authors: Yung-Hsiang Lu, George K. Thiruvathukal, Ahmed S. Kaseb, Kent Gauen, Damini Rijhwani, Ryan Dailey, Deeptanshu Malik, Yutong Huang, Sarah Aghajanzadeh, Minghao Guo
Date: 2019-04-14 00:00:00
Tasks: Management

Millions of network cameras have been deployed worldwide. Real-time data from
many network cameras can offer instant views of multiple locations with
applications in public safety, transportation management, urban planning,
agriculture, forestry, social sciences, atmospheric information, and more. This
paper describes the real-time data available from worldwide network cameras and
potential applications. Second, this paper outlines the CAM2 System available
to users at https://www.cam2project.net/. This information includes strategies
to discover network cameras and create the camera database, user interface, and
computing platforms. Third, this paper describes many opportunities provided by
data from network cameras and challenges to be addressed.
Gyroscope-aided Relative Pose Estimation for Rolling Shutter Cameras
Authors: Chang-Ryeol Lee, Ju Hong Yoon, Min-Gyu Park, Kuk-Jin Yoon
Date: 2019-04-14 00:00:00
Tasks: Pose Estimation

The rolling shutter camera has received great attention due to its low cost
imaging capability, however, the estimation of relative pose between rolling
shutter cameras still remains a difficult problem owing to its line-by-line
image capturing characteristics. To alleviate this problem, we exploit
gyroscope measurements, angular velocity, along with image measurement to
compute the relative pose between rolling shutter cameras. The gyroscope
measurements provide the information about instantaneous motion that causes the
rolling shutter distortion. Having gyroscope measurements in one hand, we
simplify the relative pose estimation problem and find a minimal solution for
the problem based on the Grobner basis polynomial solver. The proposed method
requires only five points to compute relative pose between rolling shutter
cameras, whereas previous methods require 20 or 44 corresponding points for
linear and uniform rolling shutter geometry models, respectively. Experimental
results on synthetic and real data verify the superiority of the proposed
method over existing relative pose estimation methods.
A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills
Authors: Alexander Fabisch
Date: 2019-04-14 00:00:00
Tasks: Imitation Learning

Imitation learning is a way to teach robots skills that are demonstrated by
humans. Transfering skills between these different kinematic structures seems
to be straightforward in Cartesian space. Because of the correspondence
problem, however, the result will most likely not be identical. This is why
refinement is required, for example, by policy search. Policy search in
Cartesian space is prone to reachability problems when using conventional
inverse kinematic solvers. We propose a configurable approximate inverse
kinematic solver and show that it can accelerate the refinement process
considerably. We also compare empirically refinement in Cartesian space and
refinement in joint space.
Probabilistic Kernel Support Vector Machines
Authors: Yongxin Chen, Tryphon T. Georgiou, Allen R. Tannenbaum
Date: 2019-04-14 00:00:00
Tasks: Binary Classification

We propose a probabilistic enhancement of standard kernel Support Vector Machines for binary classification, in order to address the case when, along with given data sets, a description of uncertainty (e.g., error bounds) may be available on each datum. In the present paper, we specifically consider Gaussian distributions to model uncertainty. Thereby, our data consist of pairs $(x_i,\Sigma_i)$, $i\in\{1,\ldots,N\}$, along with an indicator $y_i\in\{-1,1\}$ to declare membership in one of two categories for each pair. These pairs may be viewed to represent the mean and covariance, respectively, of random vectors $\xi_i$ taking values in a suitable linear space (typically $\mathbb R^n$). Thus, our setting may also be viewed as a modification of Support Vector Machines to classify distributions, albeit, at present, only Gaussian ones. We outline the formalism that allows computing suitable classifiers via a natural modification of the standard "kernel trick." The main contribution of this work is to point out a suitable kernel function for applying Support Vector techniques to the setting of uncertain data for which a detailed uncertainty description is also available (herein, "Gaussian points").
Finding a latent k-simplex in O(k . nnz(data)) time via Subset Smoothing
Authors: Chiranjib Bhattacharyya, Ravindran Kannan
Date: 2019-04-14 00:00:00
Tasks: Clustering, Community Detection, parameter estimation, Topic Models

In this paper we show that a large class of Latent variable models, such as Mixed Membership Stochastic Block(MMSB) Models, Topic Models, and Adversarial Clustering, can be unified through a geometric perspective, replacing model specific assumptions and algorithms for individual models. The geometric perspective leads to the formulation: \emph{find a latent $k-$ polytope $K$ in ${\bf R}^d$ given $n$ data points, each obtained by perturbing a latent point in $K$}. This problem does not seem to have been considered in the literature. The most important contribution of this paper is to show that the latent $k-$polytope problem admits an efficient algorithm under deterministic assumptions which naturally hold in Latent variable models considered in this paper. ur algorithm runs in time $O^*(k\; \mbox{nnz})$ matching the best running time of algorithms in special cases considered here and is better when the data is sparse, as is the case in applications. An important novelty of the algorithm is the introduction of \emph{subset smoothed polytope}, $K'$, the convex hull of the ${n\choose \delta n}$ points obtained by averaging all $\delta n$ subsets of the data points, for a given $\delta \in (0,1)$. We show that $K$ and $K'$ are close in Hausdroff distance. Among the consequences of our algorithm are the following: (a) MMSB Models and Topic Models: the first quasi-input-sparsity time algorithm for parameter estimation for $k \in O^*(1)$, (b) Adversarial Clustering: In $k-$means, if, an adversary is allowed to move many data points from each cluster an arbitrary amount towards the convex hull of the centers of other clusters, our algorithm still estimates cluster centers well.
A Short Survey On Memory Based Reinforcement Learning
Authors: Dhruv Ramani
Date: 2019-04-14 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Survey

Reinforcement learning (RL) is a branch of machine learning which is employed
to solve various sequential decision making problems without proper
supervision. Due to the recent advancement of deep learning, the newly proposed
Deep-RL algorithms have been able to perform extremely well in sophisticated
high-dimensional environments. However, even after successes in many domains,
one of the major challenge in these approaches is the high magnitude of
interactions with the environment required for efficient decision making.
Seeking inspiration from the brain, this problem can be solved by incorporating
instance based learning by biasing the decision making on the memories of high
rewarding experiences. This paper reviews various recent reinforcement learning
methods which incorporate external memory to solve decision making and a survey
of them is presented. We provide an overview of the different methods - along
with their advantages and disadvantages, applications and the standard
experimentation settings used for memory based models. This review hopes to be
a helpful resource to provide key insight of the recent advances in the field
and provide help in further future development of it.
Text segmentation on multilabel documents: A distant-supervised approach
Authors: Saurav Manchanda, George Karypis
Date: 2019-04-14 00:00:00
Tasks: Information Retrieval, Multilabel Text Classification, Retrieval, Segmentation, text-classification, Text Classification, Text Segmentation, Text Summarization

Segmenting text into semantically coherent segments is an important task with
applications in information retrieval and text summarization. Developing
accurate topical segmentation requires the availability of training data with
ground truth information at the segment level. However, generating such labeled
datasets, especially for applications in which the meaning of the labels is
user-defined, is expensive and time-consuming. In this paper, we develop an
approach that instead of using segment-level ground truth information, it
instead uses the set of labels that are associated with a document and are
easier to obtain as the training data essentially corresponds to a multilabel
dataset. Our method, which can be thought of as an instance of distant
supervision, improves upon the previous approaches by exploiting the fact that
consecutive sentences in a document tend to talk about the same topic, and
hence, probably belong to the same class. Experiments on the text segmentation
task on a variety of datasets show that the segmentation produced by our method
beats the competing approaches on four out of five datasets and performs at par
on the fifth dataset. On the multilabel text classification task, our method
performs at par with the competing approaches, while requiring significantly
less time to estimate than the competing approaches.
Distributed representation of multi-sense words: A loss-driven approach
Authors: Saurav Manchanda, George Karypis
Date: 2019-04-14 00:00:00
Tasks: Word Similarity

Word2Vec's Skip Gram model is the current state-of-the-art approach for
estimating the distributed representation of words. However, it assumes a
single vector per word, which is not well-suited for representing words that
have multiple senses. This work presents LDMI, a new model for estimating
distributional representations of words. LDMI relies on the idea that, if a
word carries multiple senses, then having a different representation for each
of its senses should lead to a lower loss associated with predicting its
co-occurring words, as opposed to the case when a single vector representation
is used for all the senses. After identifying the multi-sense words, LDMI
clusters the occurrences of these words to assign a sense to each occurrence.
Experiments on the contextual word similarity task show that LDMI leads to
better performance than competing approaches.
Rare Words: A Major Problem for Contextualized Embeddings And How to Fix it by Attentive Mimicking
Authors: Timo Schick, Hinrich Schütze
Date: 2019-04-14 00:00:00
Tasks: Language Modeling, Language Modelling

Pretraining deep neural network architectures with a language modeling objective has brought large improvements for many natural language processing tasks. Exemplified by BERT, a recently proposed such architecture, we demonstrate that despite being trained on huge amounts of data, deep language models still struggle to understand rare words. To fix this problem, we adapt Attentive Mimicking, a method that was designed to explicitly learn embeddings for rare words, to deep language models. In order to make this possible, we introduce one-token approximation, a procedure that enables us to use Attentive Mimicking even when the underlying language model uses subword-based tokenization, i.e., it does not assign embeddings to all words. To evaluate our method, we create a novel dataset that tests the ability of language models to capture semantic properties of words without any task-specific fine-tuning. Using this dataset, we show that adding our adapted version of Attentive Mimicking to BERT does indeed substantially improve its understanding of rare words.
Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation
Authors: Benjamin Beyret, Ali Shafti, A. Aldo Faisal
Date: 2019-04-14 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Hierarchical Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence. However, as robots and humans come closer in their interactions, the matter of interpretability, or explainability of robot decision-making processes for the human grows in importance. A successful interaction and collaboration will only take place through mutual understanding of underlying representations of the environment and the task at hand. This is currently a challenge in deep learning systems. We present a hierarchical deep reinforcement learning system, consisting of a low-level agent handling the large actions/states space of a robotic system efficiently, by following the directives of a high-level agent which is learning the high-level dynamics of the environment and task. This high-level agent forms a representation of the world and task at hand that is interpretable for a human operator. The method, which we call Dot-to-Dot, is tested on a MuJoCo-based model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its performance. Results show efficient learning of complex actions/states spaces by the low-level agent, and an interpretable representation of the task and decision-making process learned by the high-level agent.
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction
Authors: Yi Wei, Shaohui Liu, Wang Zhao, Jiwen Lu, Jie zhou
Date: 2019-04-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction, Diversity

In this paper, we present a new perspective towards image-based shape
generation. Most existing deep learning based shape reconstruction methods
employ a single-view deterministic model which is sometimes insufficient to
determine a single groundtruth shape because the back part is occluded. In this
work, we first introduce a conditional generative network to model the
uncertainty for single-view reconstruction. Then, we formulate the task of
multi-view reconstruction as taking the intersection of the predicted shape
spaces on each single image. We design new differentiable guidance including
the front constraint, the diversity constraint, and the consistency loss to
enable effective single-view conditional generation and multi-view synthesis.
Experimental results and ablation studies show that our proposed approach
outperforms state-of-the-art methods on 3D reconstruction test error and
demonstrate its generalization ability on real world data.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Authors: Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, Peng Jiang
Date: 2019-04-14 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Modeling users' dynamic and evolving preferences from their historical behaviors is challenging and crucial for recommendation systems. Previous methods employ sequential neural networks (e.g., Recurrent Neural Network) to encode users' historical interactions from left to right into hidden representations for making recommendations. Although these methods achieve satisfactory results, they often assume a rigidly ordered sequence which is not always practical. We argue that such left-to-right unidirectional architectures restrict the power of the historical sequence representations. For this purpose, we introduce a Bidirectional Encoder Representations from Transformers for sequential Recommendation (BERT4Rec). However, jointly conditioning on both left and right context in deep bidirectional model would make the training become trivial since each item can indirectly "see the target item". To address this problem, we train the bidirectional model using the Cloze task, predicting the masked items in the sequence by jointly conditioning on their left and right context. Comparing with predicting the next item at each position in a sequence, the Cloze task can produce more samples to train a more powerful bidirectional model. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential models consistently.