pwc-archive/papers-with-abstracts huggingface dataset:
General Probabilistic Surface Optimization and Log Density Estimation
Authors: Dmitry Kopitkov, Vadim Indelman
Date: 2019-03-25 00:00:00
Tasks: Density Estimation

In this paper we contribute a novel algorithm family, which generalizes many unsupervised techniques including unnormalized and energy models, and allows us to infer different statistical modalities (e.g. data likelihood and ratio between densities) from data samples. The proposed unsupervised technique, named Probabilistic Surface Optimization (PSO), views a model as a flexible surface which can be pushed according to loss-specific virtual stochastic forces, where a dynamical equilibrium is achieved when the pointwise forces on the surface become equal. Concretely, the surface is pushed up and down at points sampled from two different distributions. The averaged up and down forces become functions of these two distribution densities and of force magnitudes defined by the loss of a particular PSO instance. Upon convergence, the force equilibrium imposes an optimized model to be equal to various statistical functions depending on the used magnitude functions. Furthermore, this dynamical-statistical equilibrium is extremely intuitive and useful, providing many implications and possible usages in probabilistic inference. We connect PSO to numerous existing statistical works which are also PSO instances, and derive new PSO-based inference methods as demonstration of PSO exceptional usability. Likewise, based on the insights coming from the virtual-force perspective we analyze PSO stability and propose new ways to improve it. Finally, we present new instances of PSO, termed PSO-LDE, for data log-density estimation and also provide a new NN block-diagonal architecture for increased surface flexibility, which significantly improves estimation accuracy. Both PSO-LDE and the new architecture are combined together as a new density estimation technique. In our experiments we demonstrate this technique to be superior over state-of-the-art baselines in density estimation task for multimodal 20D data.
On Measuring Social Biases in Sentence Encoders
Authors: Chandler May, Alex Wang, Shikha Bordia, Samuel R. Bowman, Rachel Rudinger
Date: 2019-03-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Sentence, Word Embeddings

The Word Embedding Association Test shows that GloVe and word2vec word
embeddings exhibit human-like implicit biases based on gender, race, and other
social constructs (Caliskan et al., 2017). Meanwhile, research on learning
reusable text representations has begun to explore sentence-level texts, with
some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the
Word Embedding Association Test to measure bias in sentence encoders. We then
test several sentence encoders, including state-of-the-art methods such as ELMo
and BERT, for the social biases studied in prior work and two important biases
that are difficult or impossible to test at the word level. We observe mixed
results including suspicious patterns of sensitivity that suggest the test's
assumptions may not hold in general. We conclude by proposing directions for
future work on measuring bias in sentence encoders.
The Mode of Computing
Authors: Luis A. Pineda
Date: 2019-03-25 00:00:00

The Turing Machine is the paradigmatic case of computing machines, but there are others such as analogical, connectionist, quantum and diverse forms of unconventional computing, each based on a particular intuition of the phenomenon of computing. This variety can be captured in terms of system levels, re-interpreting and generalizing Newell's hierarchy, which includes the knowledge level at the top and the symbol level immediately below it. In this re-interpretation the knowledge level consists of human knowledge and the symbol level is generalized into a new level that here is called The Mode of Computing. Mental processes performed by natural brains are often thought of informally as computing process and that the brain is alike to computing machinery. However, if natural computing does exist it should be characterized on its own. A proposal to such an effect is that natural computing appeared when interpretations were first made by biological entities, so natural computing and interpreting are two aspects of the same phenomenon, or that consciousness and experience are the manifestations of computing/interpreting. By analogy with computing machinery, there must be a system level at the top of the neural circuitry and directly below the knowledge level that is named here The mode of Natural Computing. If it turns out that such putative object does not exist the proposition that the mind is a computing process should be dropped; but characterizing it would come with solving the hard problem of consciousness.
The Random Conditional Distribution for Higher-Order Probabilistic Inference
Authors: Zenna Tavares, Xin Zhang, Edgar Minaysan, Javier Burroni, Rajesh Ranganath, Armando Solar Lezama
Date: 2019-03-25 00:00:00
Tasks: Fairness, Probabilistic Programming

The need to condition distributional properties such as expectation,
variance, and entropy arises in algorithmic fairness, model simplification,
robustness and many other areas. At face value however, distributional
properties are not random variables, and hence conditioning them is a semantic
error and type error in probabilistic programming languages. On the other hand,
distributional properties are contingent on other variables in the model,
change in value when we observe more information, and hence in a precise sense
are random variables too. In order to capture the uncertain over distributional
properties, we introduce a probability construct -- the random conditional
distribution -- and incorporate it into a probabilistic programming language
Omega. A random conditional distribution is a higher-order random variable
whose realizations are themselves conditional random variables. In Omega we
extend distributional properties of random variables to random conditional
distributions, such that for example while the expectation a real valued random
variable is a real value, the expectation of a random conditional distribution
is a distribution over expectations. As a consequence, it requires minimal
syntax to encode inference problems over distributional properties, which so
far have evaded treatment within probabilistic programming systems and
probabilistic modeling in general. We demonstrate our approach case studies in
algorithmic fairness and robustness.
Deep Learning for Localization in the Lung
Authors: Jake Sganga, David Eng, Chauncey Graetzel, David B. Camarillo
Date: 2019-03-25 00:00:00
Tasks: Deep Learning, Navigate, Position

Lung cancer is the leading cause of cancer-related death worldwide, and early
diagnosis is critical to improving patient outcomes. To diagnose cancer, a
highly trained pulmonologist must navigate a flexible bronchoscope deep into
the branched structure of the lung for biopsy. The biopsy fails to sample the
target tissue in 26-33% of cases largely because of poor registration with the
preoperative CT map. We developed two deep learning approaches to localize the
bronchoscope in the preoperative CT map in real time and tested the algorithms
across 13 trajectories in a lung phantom and 68 trajectories in 11 human
cadaver lungs. In the lung phantom, we observe performance reaching 95%
precision and recall of visible airways and 3 mm average position error. On a
successful cadaver lung sequence, the algorithms trained on simulation alone
achieved 77%-94% precision and recall of visible airways and 4-6 mm average
position error. We also compare the effect of GAN-stylizing images and we look
at aggregate statistics over the entire set of trajectories.
Recognizing Arrow Of Time In The Short Stories
Authors: Fahimeh Hosseini, Hosein Fooladi, Mohammad Reza Samsami
Date: 2019-03-25 00:00:00
Proceeding: WS 2019 8

Recognizing arrow of time in short stories is a challenging task. i.e., given
only two paragraphs, determining which comes first and which comes next is a
difficult task even for humans. In this paper, we have collected and curated a
novel dataset for tackling this challenging task. We have shown that a
pre-trained BERT architecture achieves reasonable accuracy on the task, and
outperforms RNN-based architectures.
Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph
Authors: Yao-Hung Hubert Tsai, Santosh Divvala, Louis-Philippe Morency, Ruslan Salakhutdinov, Ali Farhadi
Date: 2019-03-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Model Optimization, Video Relationship, Video Visual Relation Detection

Visual relationship reasoning is a crucial yet challenging task for
understanding rich interactions across visual concepts. For example, a
relationship 'man, open, door' involves a complex relation 'open' between
concrete entities 'man, door'. While much of the existing work has studied this
problem in the context of still images, understanding visual relationships in
videos has received limited attention. Due to their temporal nature, videos
enable us to model and reason about a more comprehensive set of visual
relationships, such as those requiring multiple (temporal) observations (e.g.,
'man, lift up, box' vs. 'man, put down, box'), as well as relationships that
are often correlated through time (e.g., 'woman, pay, money' followed by
'woman, buy, coffee'). In this paper, we construct a Conditional Random Field
on a fully-connected spatio-temporal graph that exploits the statistical
dependency between relational entities spatially and temporally. We introduce a
novel gated energy function parametrization that learns adaptive relations
conditioned on visual observations. Our model optimization is computationally
efficient, and its space computation complexity is significantly amortized
through our proposed parameterization. Experimental results on benchmark video
datasets (ImageNet Video and Charades) demonstrate state-of-the-art performance
across three standard relationship reasoning tasks: Detection, Tagging, and
Recognition.
Winning Isn't Everything: Enhancing Game Development with Intelligent Agents
Authors: Yunqi Zhao, Igor Borovikov, Fernando De Mesentier Silva, Ahmad Beirami, Jason Rupert, Caedmon Somers, Jesse Harder, John Kolen, Jervis Pinto, Reza Pourabolghasem, James Pestrak, Harold Chaput, Mohsen Sardari, Long Lin, Sundeep Narravula, Navid Aghdaie, Kazi Zaman
Date: 2019-03-25 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Recently, there have been several high-profile achievements of agents learning to play games against humans and beat them. In this paper, we study the problem of training intelligent agents in service of game development. Unlike the agents built to "beat the game", our agents aim to produce human-like behavior to help with game evaluation and balancing. We discuss two fundamental metrics based on which we measure the human-likeness of agents, namely skill and style, which are multi-faceted concepts with practical implications outlined in this paper. We report four case studies in which the style and skill requirements inform the choice of algorithms and metrics used to train agents; ranging from A* search to state-of-the-art deep reinforcement learning. We, further, show that the learning potential of state-of-the-art deep RL models does not seamlessly transfer from the benchmark environments to target ones without heavily tuning their hyperparameters, leading to linear scaling of the engineering efforts and computational cost with the number of target domains.
Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning
Authors: Muhamad Risqi U. Saputra, Pedro P. B. de Gusmao, Sen Wang, Andrew Markham, Niki Trigoni
Date: 2019-03-25 00:00:00
Tasks: Monocular Visual Odometry, Optical Flow Estimation, Visual Odometry

Inspired by the cognitive process of humans and animals, Curriculum Learning (CL) trains a model by gradually increasing the difficulty of the training data. In this paper, we study whether CL can be applied to complex geometry problems like estimating monocular Visual Odometry (VO). Unlike existing CL approaches, we present a novel CL strategy for learning the geometry of monocular VO by gradually making the learning objective more difficult during training. To this end, we propose a novel geometry-aware objective function by jointly optimizing relative and composite transformations over small windows via bounded pose regression loss. A cascade optical flow network followed by recurrent network with a differentiable windowed composition layer, termed CL-VO, is devised to learn the proposed objective. Evaluation on three real-world datasets shows superior performance of CL-VO over state-of-the-art feature-based and learning-based VO.
Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
Authors: Luke Kumar, Russell Greiner
Date: 2019-03-25 00:00:00
Tasks: Dimensionality Reduction, Survival Prediction

Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional GE data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient's cancer, we represent each patient (~document) as a mixture over cancer-topics, where each cancer-topic is a mixture over GE values (~words). This required some extensions to the standard LDA model eg: to accommodate the "real-valued" expression values - leading to our novel "discretized" Latent Dirichlet Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which describes breast cancer patients using the r=49,576 GE values, from microarrays. Our results show that our approach provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this approach by running it on the Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq modality - and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent "D-calibrated" measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach.
Learning Embodied Semantics via Music and Dance Semiotic Correlations
Authors: Francisco Afonso Raposo, David Martins de Matos, Ricardo Ribeiro
Date: 2019-03-25 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

Music semantics is embodied, in the sense that meaning is biologically
mediated by and grounded in the human body and brain. This embodied cognition
perspective also explains why music structures modulate kinetic and
somatosensory perception. We leverage this aspect of cognition, by considering
dance as a proxy for music perception, in a statistical computational model
that learns semiotic correlations between music audio and dance video. We
evaluate the ability of this model to effectively capture underlying semantics
in a cross-modal retrieval task. Quantitative results, validated with
statistical significance testing, strengthen the body of evidence for embodied
cognition in music and show the model can recommend music audio for dance video
queries and vice-versa.
Micro-Batch Training with Batch-Channel Normalization and Weight Standardization
Authors: Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille
Date: 2019-03-25 00:00:00
Tasks: GPU, image-classification, Image Classification, Instance Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation, Video Recognition

Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typically has only 1-2 images for training, which is inevitable for many computer vision tasks, e.g., object detection and semantic segmentation, constrained by memory consumption. To address this issue, we propose Weight Standardization (WS) and Batch-Channel Normalization (BCN) to bring two success factors of BN into micro-batch training: 1) the smoothing effects on the loss landscape and 2) the ability to avoid harmful elimination singularities along the training trajectory. WS standardizes the weights in convolutional layers to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients; BCN combines batch and channel normalizations and leverages estimated statistics of the activations in convolutional layers to keep networks away from elimination singularities. We validate WS and BCN on comprehensive computer vision tasks, including image classification, object detection, instance segmentation, video recognition and semantic segmentation. All experimental results consistently show that WS and BCN improve micro-batch training significantly. Moreover, using WS and BCN with micro-batch training is even able to match or outperform the performances of BN with large-batch training.
GEVR: An Event Venue Recommendation System for Groups of Mobile Users
Authors: Jason Shuo Zhang, Mike Gartrell, Richard Han, Qin Lv, Shivakant Mishra
Date: 2019-03-25 00:00:00
Tasks: Prediction

In this paper, we present GEVR, the first Group Event Venue Recommendation
system that incorporates mobility via individual location traces and context
information into a "social-based" group decision model to provide venue
recommendations for groups of mobile users. Our study leverages a real-world
dataset collected using the OutWithFriendz mobile app for group event planning,
which contains 625 users and over 500 group events. We first develop a novel
"social-based" group location prediction model, which adaptively applies
different group decision strategies to groups with different social
relationship strength to aggregate each group member's location preference, to
predict where groups will meet. Evaluation results show that our prediction
model not only outperforms commonly used and state-of-the-art group decision
strategies with over 80% accuracy for predicting groups' final meeting location
clusters, but also provides promising qualities in cold-start scenarios. We
then integrate our prediction model with the Foursquare Venue Recommendation
API to construct an event venue recommendation framework for groups of mobile
users. Evaluation results show that GEVR outperforms the comparative models by
a significant margin.
Learning Quadrangulated Patches For 3D Shape Processing
Authors: Kripasindhu Sarkar, Kiran varanasi, Didier Stricker
Date: 2019-03-25 00:00:00
Tasks: Denoising

We propose a system for surface completion and inpainting of 3D shapes using
generative models, learnt on local patches. Our method uses a novel encoding of
height map based local patches parameterized using 3D mesh quadrangulation of
the low resolution input shape. This provides us sufficient amount of local 3D
patches to learn a generative model for the task of repairing moderate sized
holes. Following the ideas from the recent progress in 2D inpainting, we
investigated both linear dictionary based model and convolutional denoising
autoencoders based model for the task for inpainting, and show our results to
be better than the previous geometry based method of surface inpainting. We
validate our method on both synthetic shapes and real world scans.
Generalized Feedback Loop for Joint Hand-Object Pose Estimation
Authors: Markus Oberweger, Paul Wohlhart, Vincent Lepetit
Date: 2019-03-25 00:00:00
Tasks: 3D Hand Pose Estimation, GPU, hand-object pose, Hand Pose Estimation, Object, Pose Estimation

We propose an approach to estimating the 3D pose of a hand, possibly handling
an object, given a depth image. We show that we can correct the mistakes made
by a Convolutional Neural Network trained to predict an estimate of the 3D pose
by using a feedback loop. The components of this feedback loop are also Deep
Networks, optimized using training data. This approach can be generalized to a
hand interacting with an object. Therefore, we jointly estimate the 3D pose of
the hand and the 3D pose of the object. Our approach performs en-par with
state-of-the-art methods for 3D hand pose estimation, and outperforms
state-of-the-art methods for joint hand-object pose estimation when using depth
images only. Also, our approach is efficient as our implementation runs in
real-time on a single GPU.
Dynamic Multi Objective Particle Swarm Optimization based on a New Environment Change Detection Strategy
Authors: Ahlem Aboud, Raja Fdhila, Adel M. ALIMI
Date: 2019-03-25 00:00:00
Tasks: Change Detection

The dynamic of real-world optimization problems raises new challenges to the
traditional particle swarm optimization (PSO). Responding to these challenges,
the dynamic optimization has received considerable attention over the past
decade. This paper introduces a new dynamic multi-objective optimization based
particle swarm optimization (Dynamic-MOPSO).The main idea of this paper is to
solve such dynamic problem based on a new environment change detection strategy
using the advantage of the particle swarm optimization. In this way, our
approach has been developed not just to obtain the optimal solution, but also
to have a capability to detect the environment changes. Thereby, DynamicMOPSO
ensures the balance between the exploration and the exploitation in dynamic
research space. Our approach is tested through the most popularized dynamic
benchmark's functions to evaluate its performance as a good method.
Machine Learning Methods Economists Should Know About
Authors: Susan Athey, Guido Imbens
Date: 2019-03-24 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference, counterfactual, Econometrics, Matrix Completion

We discuss the relevance of the recent Machine Learning (ML) literature for
economics and econometrics. First we discuss the differences in goals, methods
and settings between the ML literature and the traditional econometrics and
statistics literatures. Then we discuss some specific methods from the machine
learning literature that we view as important for empirical researchers in
economics. These include supervised learning methods for regression and
classification, unsupervised learning methods, as well as matrix completion
methods. Finally, we highlight newly developed methods at the intersection of
ML and econometrics, methods that typically perform better than either
off-the-shelf ML or more traditional econometric methods when applied to
particular classes of problems, problems that include causal inference for
average treatment effects, optimal policy estimation, and estimation of the
counterfactual effect of price changes in consumer choice models.