pwc-archive/papers-with-abstracts huggingface dataset:
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
Authors: Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, Rada Mihalcea
Date: 2018-10-05 00:00:00
Proceeding: ACL 2019 7
Tasks: Dialogue Generation, Emotion Recognition, Emotion Recognition in Conversation

Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal EmotionLines Dataset (MELD), an extension and enhancement of EmotionLines. MELD contains about 13,000 utterances from 1,433 dialogues from the TV-series Friends. Each utterance is annotated with emotion and sentiment labels, and encompasses audio, visual and textual modalities. We propose several strong multimodal baselines and show the importance of contextual and multimodal information for emotion recognition in conversations. The full dataset is available for use at http:// affective-meld.github.io.
High-Dimensional Poisson DAG Model Learning Using $\ell_1$-Regularized Regression
Authors: Gunwoong Park, Sion Park
Date: 2018-10-05 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

In this paper, we develop a new approach to learning high-dimensional Poisson directed acyclic graphical (DAG) models from only observational data without strong assumptions such as faithfulness and strong sparsity. A key component of our method is to decouple the ordering estimation or parent search where the problems can be efficiently addressed using $\ell_1$-regularized regression and the mean-variance relationship. We show that sample size $n = \Omega( d^{2} \log^{9} p)$ is sufficient for our polynomial time Mean-variance Ratio Scoring (MRS) algorithm to recover the true directed graph, where $p$ is the number of nodes and $d$ is the maximum indegree. We verify through simulations that our algorithm is statistically consistent in the high-dimensional $p>n$ setting, and performs well compared to state-of-the-art ODS, GES, and MMHC algorithms. We also demonstrate through multivariate real count data that our MRS algorithm is well-suited to estimating DAG models for multivariate count data in comparison to other methods used for discrete data.
Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition
Authors: Xuan Liu, Jie Fu
Date: 2018-10-05 00:00:00

In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying temporal logic specifications. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications.
Medical Images Analysis in Cancer Diagnostic
Authors: Jelena Vasiljević, Ivica Milosavljević, Vladimir Krstić, Nataša Zivić, Lazar Berbakov, Luka Lopušina, Dhinaharan Nagamalai, Milutin Cerović
Date: 2018-10-05 00:00:00
Tasks: Diagnostic

This paper shows results of computer analysis of images in the purpose of
finding differences between medical images in order of their classifications in
terms of separation malign tissue from a normal and benign tissue. The
diagnostics of malign tissue is of the crucial importance in medicine.
Therefore, ascertainment of the correlation between multifractals parameters
and "chaotic" cells could be of the great appliance. This paper shows the
application of multifractal analysis for additional help in cancer diagnosis,
as well as diminishing. of the subjective factor and error probability
Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer
Authors: Ashnil Kumar, Michael Fulham, Dagan Feng, Jinman Kim
Date: 2018-10-05 00:00:00
Tasks: Medical Image Analysis, Tumor Segmentation

The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications requires combining the sensitivity of PET to detect abnormal regions with anatomical localization from CT. Current methods for PET-CT image analysis either process the modalities separately or fuse information from each modality based on knowledge about the image analysis task. These methods generally do not consider the spatially varying visual characteristics that encode different information across the different modalities, which have different priorities at different locations. For example, a high abnormal PET uptake in the lungs is more meaningful for tumor detection than physiological PET uptake in the heart. Our aim is to improve fusion of the complementary information in multi-modality PET-CT with a new supervised convolutional neural network (CNN) that learns to fuse complementary information for multi-modality medical image analysis. Our CNN first encodes modality-specific features and then uses them to derive a spatially varying fusion map that quantifies the relative importance of each modality's features across different spatial locations. These fusion maps are then multiplied with the modality-specific feature maps to obtain a representation of the complementary multi-modality information at different locations, which can then be used for image analysis. We evaluated the ability of our CNN to detect and segment multiple regions with different fusion requirements using a dataset of PET-CT images of lung cancer. We compared our method to baseline techniques for multi-modality image fusion and segmentation. Our findings show that our CNN had a significantly higher foreground detection accuracy (99.29%, p < 0.05) than the fusion baselines and a significantly higher Dice score (63.85%) than recent PET-CT tumor segmentation methods.
Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction
Authors: Kyle D. Julian, Mykel J. Kochenderfer
Date: 2018-10-04 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, Reinforcement Learning

Small unmanned aircraft can help firefighters combat wildfires by providing
real-time surveillance of the growing fires. However, guiding the aircraft
autonomously given only wildfire images is a challenging problem. This work
models noisy images obtained from on-board cameras and proposes two approaches
to filtering the wildfire images. The first approach uses a simple Kalman
filter to reduce noise and update a belief map in observed areas. The second
approach uses a particle filter to predict wildfire growth and uses
observations to estimate uncertainties relating to wildfire expansion. The
belief maps are used to train a deep reinforcement learning controller, which
learns a policy to navigate the aircraft to survey the wildfire while avoiding
flight directly over the fire. Simulation results show that the proposed
controllers precisely guide the aircraft and accurately estimate wildfire
growth, and a study of observation noise demonstrates the robustness of the
particle filter approach.
Correcting the bias in least squares regression with volume-rescaled sampling
Authors: Michał Dereziński, Manfred K. Warmuth, Daniel Hsu
Date: 2018-10-04 00:00:00
Tasks: regression

Consider linear regression where the examples are generated by an unknown
distribution on $R^d\times R$. Without any assumptions on the noise, the linear
least squares solution for any i.i.d. sample will typically be biased w.r.t.
the least squares optimum over the entire distribution. However, we show that
if an i.i.d. sample of any size k is augmented by a certain small additional
sample, then the solution of the combined sample becomes unbiased. We show this
when the additional sample consists of d points drawn jointly according to the
input distribution that is rescaled by the squared volume spanned by the
points. Furthermore, we propose algorithms to sample from this volume-rescaled
distribution when the data distribution is only known through an i.i.d sample.
FashionNet: Personalized Outfit Recommendation with Deep Neural Network
Authors: Tong He, Yang Hu
Date: 2018-10-04 00:00:00
Tasks: Recommendation Systems

With the rapid growth of fashion-focused social networks and online shopping,
intelligent fashion recommendation is now in great need. We design algorithms
which automatically suggest users outfits (e.g. a shirt, together with a skirt
and a pair of high-heel shoes), that fit their personal fashion preferences.
Recommending sets, each of which is composed of multiple interacted items, is
relatively new to recommender systems, which usually recommend individual items
to users. We explore the use of deep networks for this challenging task. Our
system, dubbed FashionNet, consists of two components, a feature network for
feature extraction and a matching network for compatibility computation. The
former is achieved through a deep convolutional network. And for the latter, we
adopt a multi-layer fully-connected network structure. We design and compare
three alternative architectures for FashionNet. To achieve personalized
recommendation, we develop a two-stage training strategy, which uses the
fine-tuning technique to transfer a general compatibility model to a model that
embeds personal preference. Experiments on a large scale data set collected
from a popular fashion-focused social network validate the effectiveness of the
proposed networks.
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Authors: Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, Eric Xing
Date: 2018-10-04 00:00:00
Tasks: Image Generation, Machine Translation, Meta-Learning, NMT, Scheduling, Translation

Many machine learning problems involve iteratively and alternately optimizing
different task objectives with respect to different sets of parameters.
Appropriately scheduling the optimization of a task objective or a set of
parameters is usually crucial to the quality of convergence. In this paper, we
present AutoLoss, a meta-learning framework that automatically learns and
determines the optimization schedule. AutoLoss provides a generic way to
represent and learn the discrete optimization schedule from metadata, allows
for a dynamic and data-driven schedule in ML problems that involve alternating
updates of different parameters or from different loss objectives. We apply
AutoLoss on four ML tasks: d-ary quadratic regression, classification using a
multi-layer perceptron (MLP), image generation using GANs, and multi-task
neural machine translation (NMT). We show that the AutoLoss controller is able
to capture the distribution of better optimization schedules that result in
higher quality of convergence on all four tasks. The trained AutoLoss
controller is generalizable -- it can guide and improve the learning of a new
task model with different specifications, or on different datasets.
Dynamics and Reachability of Learning Tasks
Authors: Alessandro Achille, Glen Mbeng, Stefano Soatto
Date: 2018-10-04 00:00:00
Tasks: Semantic Textual Similarity, Transfer Learning

We compute the transition probability between two learning tasks, and show that it decomposes into two factors. The first depends on the geometry of the loss landscape of a model trained on each task, independent of any particular model used. This is related to an information theoretic distance function, but is insufficient to predict success in transfer learning, as nearby tasks can be unreachable via fine-tuning. The second factor depends on the ease of traversing the path between two tasks. With this dynamic component, we derive strict lower bounds on the complexity necessary to learn a task starting from the solution to another, which is one of the most common forms of transfer learning.
Abstracting Probabilistic Models: A Logical Perspective
Authors: Vaishak Belle
Date: 2018-10-04 00:00:00

Abstraction is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood. In this paper, we provide a semantical framework for analyzing such abstractions from first principles. We develop the framework in a general way, allowing for expressive languages, including logic-based ones that admit relational and hierarchical constructs with stochastic primitives. We motivate a definition of consistency between a high-level model and its low-level counterpart, but also treat the case when the high-level model is missing critical information present in the low-level model. We prove properties of abstractions, both at the level of the parameter as well as the structure of the models. We conclude with some observations about how abstractions can be derived automatically.
Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness
Authors: Chihuang Liu, Joseph JaJa
Date: 2018-10-04 00:00:00
Tasks: Adversarial Robustness, Denoising

Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and $L_2$ feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extract similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.
Generalizing the theory of cooperative inference
Authors: Pei Wang, Pushpi Paranamana, Patrick Shafto
Date: 2018-10-04 00:00:00
Tasks: BIG-bench Machine Learning

Cooperation information sharing is important to theories of human learning
and has potential implications for machine learning. Prior work derived
conditions for achieving optimal Cooperative Inference given strong, relatively
restrictive assumptions. We relax these assumptions by demonstrating
convergence for any discrete joint distribution, robustness through equivalence
classes and stability under perturbation, and effectiveness by deriving bounds
from structural properties of the original joint distribution. We provide
geometric interpretations, connections to and implications for optimal
transport, and connections to importance sampling, and conclude by outlining
open questions and challenges to realizing the promise of Cooperative
Inference.
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
Authors: Zhanpeng He, Ryan Julian, Eric Heiden, Hejia Zhang, Stefan Schaal, Joseph J. Lim, Gaurav Sukhatme, Karol Hausman
Date: 2018-10-04 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning, Zero-shot Generalization

Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing policies which generalize across tasks, despite training for thousands of hours equivalent real robot time. To address this shortcoming, we present a novel approach to efficiently learning new robotic skills directly on a real robot, based on model-predictive control (MPC) and an algorithm for learning task representations. In short, we show how to reuse the simulation from the pre-training step of sim-to-real methods as a tool for foresight, allowing the sim-to-real policy adapt to unseen tasks. Rather than end-to-end learning policies for single tasks and attempting to transfer them, we first use simulation to simultaneously learn (1) a continuous parameterization (i.e. a skill embedding or latent) of task-appropriate primitive skills, and (2) a single policy for these skills which is conditioned on this representation. We then directly transfer our multi-skill policy to a real robot, and actuate the robot by choosing sequences of skill latents which actuate the policy, with each latent corresponding to a pre-learned primitive skill controller. We complete unseen tasks by choosing new sequences of skill latents to control the robot using MPC, where our MPC model is composed of the pre-trained skill policy executed in the simulation environment, run in parallel with the real robot. We discuss the background and principles of our method, detail its practical implementation, and evaluate its performance by using our method to train a real Sawyer Robot to achieve motion tasks such as drawing and block pushing.
Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs
Authors: Dinesh Acharya, Zhiwu Huang, Danda Pani Paudel, Luc van Gool
Date: 2018-10-04 00:00:00
Tasks: Action Recognition, Image Generation, Temporal Action Localization, Video Generation

The extension of image generation to video generation turns out to be a very
difficult task, since the temporal dimension of videos introduces an extra
challenge during the generation process. Besides, due to the limitation of
memory and training stability, the generation becomes increasingly challenging
with the increase of the resolution/duration of videos. In this work, we
exploit the idea of progressive growing of Generative Adversarial Networks
(GANs) for higher resolution video generation. In particular, we begin to
produce video samples of low-resolution and short-duration, and then
progressively increase both resolution and duration alone (or jointly) by
adding new spatiotemporal convolutional layers to the current networks.
Starting from the learning on a very raw-level spatial appearance and temporal
movement of the video distribution, the proposed progressive method learns
spatiotemporal information incrementally to generate higher resolution videos.
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to
improve the distribution learning on the video data of high-dimension and
mixed-spatiotemporal distribution. SWGAN loss replaces the distance between
joint distributions by that of one-dimensional marginal distributions, making
the loss easier to compute. We evaluate the proposed model on our collected
face video dataset of 10,900 videos to generate photorealistic face videos of
256x256x32 resolution. In addition, our model also reaches a record inception
score of 14.57 in unsupervised action recognition dataset UCF-101.
Projective Inference in High-dimensional Problems: Prediction and Feature Selection
Authors: Juho Piironen, Markus Paasiniemi, Aki Vehtari
Date: 2018-10-04 00:00:00
Tasks: feature selection, Vocal Bursts Intensity Prediction

This paper discusses predictive inference and feature selection for
generalized linear models with scarce but high-dimensional data. We argue that
in many cases one can benefit from a decision theoretically justified two-stage
approach: first, construct a possibly non-sparse model that predicts well, and
then find a minimal subset of features that characterize the predictions. The
model built in the first step is referred to as the \emph{reference model} and
the operation during the latter step as predictive \emph{projection}. The key
characteristic of this approach is that it finds an excellent tradeoff between
sparsity and predictive accuracy, and the gain comes from utilizing all
available information including prior and that coming from the left out
features. We review several methods that follow this principle and provide
novel methodological contributions. We present a new projection technique that
unifies two existing techniques and is both accurate and fast to compute. We
also propose a way of evaluating the feature selection process using fast
leave-one-out cross-validation that allows for easy and intuitive model size
selection. Furthermore, we prove a theorem that helps to understand the
conditions under which the projective approach could be beneficial. The
benefits are illustrated via several simulated and real world examples.
A method to Suppress Facial Expression in Posed and Spontaneous Videos
Authors: Ghada Zamzmi, Gabriel Ruiz, Matthew Shreve, Dmitry Goldgof, Rangachar Kasturi, Sudeep Sarkar
Date: 2018-10-04 00:00:00
Tasks: Face Recognition, Retrieval

We address the problem of suppressing facial expressions in videos because
expressions can hinder the retrieval of important information in applications
such as face recognition. To achieve this, we present an optical strain
suppression method that removes any facial expression without requiring
training for a specific expression. For each frame in a video, an optical
strain map that provides the strain magnitude value at each pixel is generated;
this strain map is then utilized to neutralize the expression by replacing
pixels of high strain values with pixels from a reference face frame.
Experimental results of testing the method on various expressions namely
happiness, sadness, and anger for two publicly available data sets (i.e.,
BU-4DFE and AM-FED) show the ability of our method in suppressing facial
expressions.
Clustering-based Anomaly Detection for microservices
Authors: Roman Nikiforov
Date: 2018-10-04 00:00:00
Tasks: Anomaly Detection, Cloud Computing, Clustering, Management

Anomaly detection is an important step in the management and monitoring of
data centers and cloud computing platforms. The ability to detect anomalous
virtual machines before real failures occur results in reduced downtime while
operations engineers urgently recover malfunctioning virtual machines,
efficient root cause analysis, and improved customer optics in the event said
malfunction lead to an outage. Virtual machines could fail at any time, whether
in a lab or production system. If there is no anomaly detection system, and a
virtual machine in a lab environment fails, the QA and DEV team will have to
switch to another environment while the OPS team fixes the failure. The
potential impact of failing to detect anomalous virtual machines can result in
financial ramifications, both when developing new features and servicing
existing ones. This paper presents a model that can efficiently detect
anomalous virtual machines both in production and testing environments.