pwc-archive/papers-with-abstracts huggingface dataset:
Risk Aware Ranking for Top-$k$ Recommendations
Authors: Shameem A Puthiya Parambath, Nishant Vijayakumar, Sanjay Chawla
Date: 2019-03-17 00:00:00

Given an incomplete ratings data over a set of users and items, the
preference completion problem aims to estimate a personalized total preference
order over a subset of the items. In practical settings, a ranked list of
top-$k$ items from the estimated preference order is recommended to the end
user in the decreasing order of preference for final consumption. We analyze
this model and observe that such a ranking model results in suboptimal
performance when the payoff associated with the recommended items is different.
We propose a novel and very efficient algorithm for the preference ranking
considering the uncertainty regarding the payoffs of the items. Once the
preference scores for the users are obtained using any preference learning
algorithm, we show that ranking the items using a risk seeking utility function
results in the best ranking performance.
GraSPy: Graph Statistics in Python
Authors: Jaewon Chung, Benjamin D. Pedigo, Eric W. Bridgeford, Bijan K. Varjavand, Hayden S. Helm, Joshua T. Vogelstein
Date: 2019-03-29 00:00:00
Tasks: BIG-bench Machine Learning

We introduce GraSPy, a Python library devoted to statistical inference, machine learning, and visualization of random graphs and graph populations. This package provides flexible and easy-to-use algorithms for analyzing and understanding graphs with a scikit-learn compliant API. GraSPy can be downloaded from Python Package Index (PyPi), and is released under the Apache 2.0 open-source license. The documentation and all releases are available at https://neurodata.io/graspy.
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks
Authors: Arash A. Amini, Marina S. Paez, Lizhen Lin
Date: 2019-03-30 00:00:00
Tasks: Community Detection, Stochastic Block Model

Multiplex networks have become increasingly more prevalent in many fields, and have emerged as a powerful tool for modeling the complexity of real networks. There is a critical need for developing inference models for multiplex networks that can take into account potential dependencies across different layers, particularly when the aim is community detection. We add to a limited literature by proposing a novel and efficient Bayesian model for community detection in multiplex networks. A key feature of our approach is the ability to model varying communities at different network layers. In contrast, many existing models assume the same communities for all layers. Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples). This is appealing, since deciding the number of communities is a challenging aspect of community detection, and especially so in the multiplex setting, if one allows the communities to change across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a hierarchical Dirichlet prior to model community labels across layers, allowing dependency in their structure. Given the community labels, a stochastic block model (SBM) is assumed for each layer. We develop an efficient slice sampler for sampling the posterior distribution of the community labels as well as the link probabilities between communities. In doing so, we address some unique challenges posed by coupling the complex likelihood of SBM with the hierarchical nature of the prior on the labels. An extensive empirical validation is performed on simulated and real data, demonstrating the superior performance of the model over single-layer alternatives, as well as the ability to uncover interesting structures in real networks.
Community detection over a heterogeneous population of non-aligned networks
Authors: Guilherme Gomes, Vinayak Rao, Jennifer Neville
Date: 2019-04-04 00:00:00
Tasks: Clustering, Community Detection

Clustering and community detection with multiple graphs have typically
focused on aligned graphs, where there is a mapping between nodes across the
graphs (e.g., multi-view, multi-layer, temporal graphs). However, there are
numerous application areas with multiple graphs that are only partially
aligned, or even unaligned. These graphs are often drawn from the same
population, with communities of potentially different sizes that exhibit
similar structure. In this paper, we develop a joint stochastic blockmodel
(Joint SBM) to estimate shared communities across sets of heterogeneous
non-aligned graphs. We derive an efficient spectral clustering approach to
learn the parameters of the joint SBM. We evaluate the model on both synthetic
and real-world datasets and show that the joint model is able to exploit
cross-graph information to better estimate the communities compared to learning
separate SBMs on each individual graph.
Bayesian estimation of the latent dimension and communities in stochastic blockmodels
Authors: Francesco Sanna Passino, Nicholas A. Heard
Date: 2019-04-06 00:00:00
Tasks: Community Detection

Spectral embedding of adjacency or Laplacian matrices of undirected graphs is a common technique for representing a network in a lower dimensional latent space, with optimal theoretical guarantees. The embedding can be used to estimate the community structure of the network, with strong consistency results in the stochastic blockmodel framework. One of the main practical limitations of standard algorithms for community detection from spectral embeddings is that the number of communities and the latent dimension of the embedding must be specified in advance. In this article, a novel Bayesian model for simultaneous and automatic selection of the appropriate dimension of the latent space and the number of blocks is proposed. Extensions to directed and bipartite graphs are discussed. The model is tested on simulated and real world network data, showing promising performance for recovering latent community structure.
Adapting Stochastic Block Models to Power-Law Degree Distributions
Authors: Maoying Qiao, Jun Yu, Wei Bian, Qiang Li, DaCheng Tao
Date: 2019-04-05 00:00:00

Stochastic block models (SBMs) have been playing an important role in
modeling clusters or community structures of network data. But, it is incapable
of handling several complex features ubiquitously exhibited in real-world
networks, one of which is the power-law degree characteristic. To this end, we
propose a new variant of SBM, termed power-law degree SBM (PLD-SBM), by
introducing degree decay variables to explicitly encode the varying degree
distribution over all nodes. With an exponential prior, it is proved that
PLD-SBM approximately preserves the scale-free feature in real networks. In
addition, from the inference of variational E-Step, PLD-SBM is indeed to
correct the bias inherited in SBM with the introduced degree decay factors.
Furthermore, experiments conducted on both synthetic networks and two
real-world datasets including Adolescent Health Data and the political blogs
network verify the effectiveness of the proposed model in terms of cluster
prediction accuracies.
Short note on the behavior of recurrent neural network for noisy dynamical system
Authors: Kyongmin Yeo
Date: 2019-04-05 00:00:00
Tasks: Time Series, Time Series Analysis

The behavior of recurrent neural network for the data-driven simulation of
noisy dynamical systems is studied by training a set of Long Short-Term Memory
Networks (LSTM) on the Mackey-Glass time series with a wide range of noise
level. It is found that, as the training noise becomes larger, LSTM learns to
depend more on its autonomous dynamics than the noisy input data. As a result,
LSTM trained on noisy data becomes less susceptible to the perturbation in the
data, but has a longer relaxation timescale. On the other hand, when trained on
noiseless data, LSTM becomes extremely sensitive to a small perturbation, but
is able to adjusts to the changes in the input data.
Text-based depression detection on sparse data
Authors: Heinrich Dinkel, Mengyue Wu, Kai Yu
Date: 2019-04-08 00:00:00
Tasks: Depression Detection, Sentence, Word Embeddings

Previous text-based depression detection is commonly based on large user-generated data. Sparse scenarios like clinical conversations are less investigated. This work proposes a text-based multi-task BGRU network with pretrained word embeddings to model patients' responses during clinical interviews. Our main approach uses a novel multi-task loss function, aiming at modeling both depression severity and binary health state. We independently investigate word- and sentence-level word-embeddings as well as the use of large-data pretraining for depression detection. To strengthen our findings, we report mean-averaged results for a multitude of independent runs on sparse data. First, we show that pretraining is helpful for word-level text-based depression detection. Second, our results demonstrate that sentence-level word-embeddings should be mostly preferred over word-level ones. While the choice of pooling function is less crucial, mean and attention pooling should be preferred over last-timestep pooling. Our method outputs depression presence results as well as predicted severity score, culminating a macro F1 score of 0.84 and MAE of 3.48 on the DAIC-WOZ development set.
Flavour Enhanced Food Recommendation
Authors: Nitish Nag, Aditya Bharadwaj, Aditya Narendra Rao, Akash Kulhalli, Kushal Samir Mehta, Nishant Bhattacharya, Pratul Ramkumar, Dinkar Sitaram, Ramesh Jain
Date: 2019-04-02 00:00:00
Tasks: Food recommendation, Recommendation Systems

We propose a mechanism to use the features of flavour to enhance the quality of food recommendations. An empirical method to determine the flavour of food is incorporated into a recommendation engine based on major gustatory nerves. Such a system has advantages of suggesting food items that the user is more likely to enjoy based upon matching with their flavour profile through use of the taste biological domain knowledge. This preliminary intends to spark more robust mechanisms by which flavour of food is taken into consideration as a major feature set into food recommendation systems. Our long term vision is to integrate this with health factors to recommend healthy and tasty food to users to enhance quality of life.
Extremely Randomized CNets for Multi-label Classification
Authors: Teresa M.A. Basile, Nicola Di Mauro, Floriana Esposito
Date: 2018-10-01 00:00:00
Proceeding: XVIIth International Conference of the Italian Association for Artificial Intelligence 2018 10
Tasks: Classification, Density Estimation, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification (MLC) is a challenging task in ma-chine learning consisting in the prediction of multiple labels associated with  a  single  instance.  Promising  approaches  for  MLC  are  those  able to capture label dependencies by learning a single probabilistic model—differently  from  other  competitive  approaches  requiring  to  learn  many models. The model is then exploited to compute the most probable label configuration given the observed attributes. Cutset Networks (CNets) are density estimators leveraging context-specific independencies providing exact inference in polynomial time. The recently introduced Extremely Randomized CNets (XCNets) reduce the structure learning complexity making able to learn ensembles of XCNets outperforming state-of-the-art  density  estimators.  In  this  paper  we  employ  XCNets  for  MLC  by exploiting efficient Most Probable Explanations (MPE). An experimental evaluation on real-world datasets shows how the proposed approach is competitive w.r.t. other sophisticated methods for MLC
Wavenet based low rate speech coding
Authors: W. Bastiaan Kleijn, Felicia S. C. Lim, Alejandro Luebs, Jan Skoglund, Florian Stimberg, Quan Wang, Thomas C. Walters
Date: 2017-12-01 00:00:00
Tasks: Bandwidth Extension

Traditional parametric coding of speech facilitates low rate but provides
poor reconstruction quality because of the inadequacy of the model used. We
describe how a WaveNet generative speech model can be used to generate high
quality speech from the bit stream of a standard parametric coder operating at
2.4 kb/s. We compare this parametric coder with a waveform coder based on the
same generative model and show that approximating the signal waveform incurs a
large rate penalty. Our experiments confirm the high performance of the WaveNet
based coder and show that the speech produced by the system is able to
additionally perform implicit bandwidth extension and does not significantly
impair recognition of the original speaker for the human listener, even when
that speaker has not been used during the training of the generative model.
GMM-Based Hidden Markov Random Field for Color Image and 3D Volume Segmentation
Authors: Quan Wang
Date: 2012-12-18 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

In this project, we first study the Gaussian-based hidden Markov random field
(HMRF) model and its expectation-maximization (EM) algorithm. Then we
generalize it to Gaussian mixture model-based hidden Markov random field. The
algorithm is implemented in MATLAB. We also apply this algorithm to color image
segmentation problems and 3D volume segmentation problems.
Resolving the measurement uncertainty paradox in ecological management
Authors: Milad Memarzadeh, Carl Boettiger
Date: 2018-12-28 00:00:00
Tasks: Decision Making, Management

Ecological management and decision-making typically focus on uncertainty
about the future, but surprisingly little is known about how to account for
uncertainty of the present: that is, the realities of having only partial or
imperfect measurements. Our primary paradigms for handling decisions under
uncertainty -- the precautionary principle and optimal control -- have so far
given contradictory results. This paradox is best illustrated in the example of
fisheries management, where many ideas that guide thinking about ecological
decision making were first developed. We find that simplistic optimal control
approaches have repeatedly concluded that a manager should increase catch
quotas when faced with greater uncertainty about the fish biomass. Current best
practices take a more precautionary approach, decreasing catch quotas by a
fixed amount to account for uncertainty. Using comparisons to both simulated
and historical catch data, we find that neither approach is sufficient to avoid
stock collapses under moderate observational uncertainty. Using partially
observed Markov decision process (POMDP) methods, we demonstrate how this
paradox arises from flaws in the standard theory, which contributes to
over-exploitation of fisheries and increased probability of economic and
ecological collapse. In contrast, we find POMDP-based management avoids such
over-exploitation while also generating higher economic value. These results
have significant implications for how we handle uncertainty in both fisheries
and ecological management more generally.
Factor Graph Attention
Authors: Idan Schwartz, Seunghak Yu, Tamir Hazan, Alexander Schwing
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Attention, Question Answering, Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

Dialog is an effective way to exchange information, but subtle details and nuances are extremely important. While significant progress has paved a path to address visual dialog with algorithms, details and nuances remain a challenge. Attention mechanisms have demonstrated compelling results to extract details in visual question answering and also provide a convincing framework for visual dialog due to their interpretability and effectiveness. However, the many data utilities that accompany visual dialog challenge existing attention techniques. We address this issue and develop a general attention mechanism for visual dialog which operates on any number of data utilities. To this end, we design a factor graph based attention mechanism which combines any number of utility representations. We illustrate the applicability of the proposed approach on the challenging and recently introduced VisDial datasets, outperforming recent state-of-the-art methods by 1.1% for VisDial0.9 and by 2% for VisDial1.0 on MRR. Our ensemble model improved the MRR score on VisDial1.0 by more than 6%.
Max-Sliced Wasserstein Distance and its use for GANs
Authors: Ishan Deshpande, Yuan-Ting Hu, Ruoyu Sun, Ayis Pyrros, Nasir Siddiqui, Sanmi Koyejo, Zhizhen Zhao, David Forsyth, Alexander Schwing
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image-to-Image Translation, Translation

Generative adversarial nets (GANs) and variational auto-encoders have
significantly improved our distribution modeling capabilities, showing promise
for dataset augmentation, image-to-image translation and feature learning.
However, to model high-dimensional distributions, sequential training and
stacked architectures are common, increasing the number of tunable
hyper-parameters as well as the training time. Nonetheless, the sample
complexity of the distance metrics remains one of the factors affecting GAN
training. We first show that the recently proposed sliced Wasserstein distance
has compelling sample complexity properties when compared to the Wasserstein
distance. To further improve the sliced Wasserstein distance we then analyze
its `projection complexity' and develop the max-sliced Wasserstein distance
which enjoys compelling sample complexity while reducing projection complexity,
albeit necessitating a max estimation. We finally illustrate that the proposed
distance trains GANs on high-dimensional images up to a resolution of 256x256
easily.
Knowledge Flow: Improve Upon Your Teachers
Authors: Iou-Jen Liu, Jian Peng, Alexander G. Schwing
Date: 2019-04-11 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

A zoo of deep nets is available these days for almost any given task, and it
is increasingly unclear which net to start with when addressing a new task, or
which net to use as an initialization for fine-tuning a new model. To address
this issue, in this paper, we develop knowledge flow which moves 'knowledge'
from multiple deep nets, referred to as teachers, to a new deep net model,
called the student. The structure of the teachers and the student can differ
arbitrarily and they can be trained on entirely different tasks with different
output spaces too. Upon training with knowledge flow the student is independent
of the teachers. We demonstrate our approach on a variety of supervised and
reinforcement learning tasks, outperforming fine-tuning and other 'knowledge
exchange' methods.
Two Body Problem: Collaborative Visual Task Completion
Authors: Unnat Jain, Luca Weihs, Eric Kolve, Mohammad Rastegari, Svetlana Lazebnik, Ali Farhadi, Alexander Schwing, Aniruddha Kembhavi
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Task 2, Vocal Bursts Valence Prediction

Collaboration is a necessary skill to perform tasks that are beyond one
agent's capabilities. Addressed extensively in both conventional and modern AI,
multi-agent collaboration has often been studied in the context of simple grid
worlds. We argue that there are inherently visual aspects to collaboration
which should be studied in visually rich environments. A key element in
collaboration is communication that can be either explicit, through messages,
or implicit, through perception of the other agents and the visual world.
Learning to collaborate in a visual environment entails learning (1) to perform
the task, (2) when and what to communicate, and (3) how to act based on these
communications and the perception of the visual world. In this paper we study
the problem of learning to collaborate directly from pixels in AI2-THOR and
demonstrate the benefits of explicit and implicit modes of communication to
perform visual tasks. Refer to our project page for more details:
https://prior.allenai.org/projects/two-body-problem
A Simple Baseline for Audio-Visual Scene-Aware Dialog
Authors: Idan Schwartz Alexander Schwing, Tamir Hazan and
Date: 2019-04-11 00:00:00

The recently proposed audio-visual scene-aware dialog task paves the way to a
more data-driven way of learning virtual assistants, smart speakers and car
navigation systems. However, very little is known to date about how to
effectively extract meaningful information from a plethora of sensors that
pound the computational engine of those devices. Therefore, in this paper, we
provide and carefully analyze a simple baseline for audio-visual scene-aware
dialog which is trained end-to-end. Our method differentiates in a data-driven
manner useful signals from distracting ones using an attention mechanism. We
evaluate the proposed approach on the recently introduced and challenging
audio-visual scene-aware dataset, and demonstrate the key features that permit
to outperform the current state-of-the-art by more than 20\% on CIDEr.
An Empirical Study of Spatial Attention Mechanisms in Deep Networks
Authors: Xizhou Zhu, Dazhi Cheng, Zheng Zhang, Stephen Lin, Jifeng Dai
Date: 2019-04-11 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decoder

Attention mechanisms have become a popular component in deep neural networks,
yet there has been little examination of how different influencing factors and
methods for computing attention from these factors affect performance. Toward a
better general understanding of attention mechanisms, we present an empirical
study that ablates various spatial attention elements within a generalized
attention formulation, encompassing the dominant Transformer attention as well
as the prevalent deformable convolution and dynamic convolution modules.
Conducted on a variety of applications, the study yields significant findings
about spatial attention in deep networks, some of which run counter to
conventional understanding. For example, we find that the query and key content
comparison in Transformer attention is negligible for self-attention, but vital
for encoder-decoder attention. A proper combination of deformable convolution
with key content only saliency achieves the best accuracy-efficiency tradeoff
in self-attention. Our results suggest that there exists much room for
improvement in the design of attention mechanisms.
An Analysis of Pre-Training on Object Detection
Authors: Hengduo Li, Bharat Singh, Mahyar Najibi, Zuxuan Wu, Larry S. Davis
Date: 2019-04-11 00:00:00
Tasks: Avg, Classification, General Classification, image-classification, Image Classification, Object, object-detection, Object Detection, Semantic Segmentation

We provide a detailed analysis of convolutional neural networks which are
pre-trained on the task of object detection. To this end, we train detectors on
large datasets like OpenImagesV4, ImageNet Localization and COCO. We analyze
how well their features generalize to tasks like image classification, semantic
segmentation and object detection on small datasets like PASCAL-VOC,
Caltech-256, SUN-397, Flowers-102 etc. Some important conclusions from our
analysis are --- 1) Pre-training on large detection datasets is crucial for
fine-tuning on small detection datasets, especially when precise localization
is needed. For example, we obtain 81.1% mAP on the PASCAL-VOC dataset at 0.7
IoU after pre-training on OpenImagesV4, which is 7.6% better than the recently
proposed DeformableConvNetsV2 which uses ImageNet pre-training. 2) Detection
pre-training also benefits other localization tasks like semantic segmentation
but adversely affects image classification. 3) Features for images (like avg.
pooled Conv5) which are similar in the object detection feature space are
likely to be similar in the image classification feature space but the converse
is not true. 4) Visualization of features reveals that detection neurons have
activations over an entire object, while activations for classification
networks typically focus on parts. Therefore, detection networks are poor at
classification when multiple instances are present in an image or when an
instance only covers a small fraction of an image.