pwc-archive/papers-with-abstracts huggingface dataset:
Agnostic Lane Detection
Authors: Yuenan Hou
Date: 2019-05-02 00:00:00
Tasks: Autonomous Driving, Instance Segmentation, Lane Detection, Multi-Task Learning, Semantic Segmentation

Lane detection is an important yet challenging task in autonomous driving,
which is affected by many factors, e.g., light conditions, occlusions caused by
other vehicles, irrelevant markings on the road and the inherent long and thin
property of lanes. Conventional methods typically treat lane detection as a
semantic segmentation task, which assigns a class label to each pixel of the
image. This formulation heavily depends on the assumption that the number of
lanes is pre-defined and fixed and no lane changing occurs, which does not
always hold. To make the lane detection model applicable to an arbitrary number
of lanes and lane changing scenarios, we adopt an instance segmentation
approach, which first differentiates lanes and background and then classify
each lane pixel into each lane instance. Besides, a multi-task learning
paradigm is utilized to better exploit the structural information and the
feature pyramid architecture is used to detect extremely thin lanes. Three
popular lane detection benchmarks, i.e., TuSimple, CULane and BDD100K, are used
to validate the effectiveness of our proposed algorithm.
Accurate Visual Localization for Automotive Applications
Authors: Eli Brosh, Matan Friedmann, Ilan Kadar, Lev Yitzhak Lavy, Elad Levi, Shmuel Rippa, Yair Lempert, Bruno Fernandez-Ruiz, Roei Herzig, Trevor Darrell
Date: 2019-05-01 00:00:00
Tasks: Retrieval, Visual Localization

Accurate vehicle localization is a crucial step towards building effective
Vehicle-to-Vehicle networks and automotive applications. Yet standard grade GPS
data, such as that provided by mobile phones, is often noisy and exhibits
significant localization errors in many urban areas. Approaches for accurate
localization from imagery often rely on structure-based techniques, and thus
are limited in scale and are expensive to compute. In this paper, we present a
scalable visual localization approach geared for real-time performance. We
propose a hybrid coarse-to-fine approach that leverages visual and GPS location
cues. Our solution uses a self-supervised approach to learn a compact road
image representation. This representation enables efficient visual retrieval
and provides coarse localization cues, which are fused with vehicle ego-motion
to obtain high accuracy location estimates. As a benchmark to evaluate the
performance of our visual localization approach, we introduce a new large-scale
driving dataset based on video and GPS data obtained from a large-scale network
of connected dash-cams. Our experiments confirm that our approach is highly
effective in challenging urban environments, reducing localization error by an
order of magnitude.
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
Authors: Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, Yoshua Bengio
Date: 2019-05-02 00:00:00

We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping that can then be applied to images of locations that have not yet experienced these events. This visual transformation is paired with climate model predictions to assess likelihood and type of climate-related events in the long term (50 years) in order to bring the future closer in the viewers mind. The eventual goal of our project is to enable individuals to make more informed choices about their climate future by creating a more visceral understanding of the effects of climate change, while maintaining scientific credibility by drawing on climate model projections.
Tuned Inception V3 for Recognizing States of Cooking Ingredients
Authors: Kin Ng
Date: 2019-05-05 00:00:00
Tasks: Transfer Learning

Cooking is a task that must be performed in a daily basis, and thus it is an activity that many people take for granted. For humans preparing a meal comes naturally, but for robots even preparing a simple sandwich results in an extremely difficult task. In robotics, designing kitchen robots is complicated since cooking relies on a variety of physical interactions that are dependent on different conditions such as changes in the environment, proper execution of sequential instructions, along with motions, and detection of the different states in which cooking-ingredients can be in for their correct grasping and manipulation. In this paper, we focus on the challenge of state recognition and propose a fine tuned convolutional neural network that makes use of transfer learning by reusing the Inception V3 pre-trained model. The model is trained and validated on a cooking dataset consisting of eleven states (e.g. peeled, diced, whole, etc.). The work presented on this paper could provide insight into finding a potential solution to the problem.
A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem
Authors: Luca Mossina, Emmanuel Rachelson, Daniel Delahaye
Date: 2019-05-09 00:00:00
Tasks: Evolutionary Algorithms, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study how Reinforcement Learning can be employed to optimally control parameters in evolutionary algorithms. We control the mutation probability of a (1+1) evolutionary algorithm on the OneMax function. This problem is modeled as a Markov Decision Process and solved with Value Iteration via the known transition probabilities. It is then solved via Q-Learning, a Reinforcement Learning algorithm, where the exact transition probabilities are not needed. This approach also allows previous expert or empirical knowledge to be included into learning. It opens new perspectives, both formally and computationally, for the problem of parameter control in optimization.
Interactive Image Generation Using Scene Graphs
Authors: Gaurav Mittal, Shubham Agrawal, Anuva Agarwal, Sushant Mehta, Tanya Marwah
Date: 2019-05-09 00:00:00
Proceeding: ICLR Workshop DeepGenStruct 2019
Tasks: Image Generation, Translation

Recent years have witnessed some exciting developments in the domain of generating images from scene-based text descriptions. These approaches have primarily focused on generating images from a static text description and are limited to generating images in a single pass. They are unable to generate an image interactively based on an incrementally additive text description (something that is more intuitive and similar to the way we describe an image). We propose a method to generate an image incrementally based on a sequence of graphs of scene descriptions (scene-graphs). We propose a recurrent network architecture that preserves the image content generated in previous steps and modifies the cumulative image as per the newly provided scene information. Our model utilizes Graph Convolutional Networks (GCN) to cater to variable-sized scene graphs along with Generative Adversarial image translation networks to generate realistic multi-object images without needing any intermediate supervision during training. We experiment with Coco-Stuff dataset which has multi-object images along with annotations describing the visual scene and show that our model significantly outperforms other approaches on the same dataset in generating visually consistent images for incrementally growing scene graphs.
The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
Authors: Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith
Date: 2019-05-09 00:00:00

We investigate how the final parameters found by stochastic gradient descent are influenced by over-parameterization. We generate families of models by increasing the number of channels in a base network, and then perform a large hyper-parameter search to study how the test error depends on learning rate, batch size, and network width. We find that the optimal SGD hyper-parameters are determined by a "normalized noise scale," which is a function of the batch size, learning rate, and initialization conditions. In the absence of batch normalization, the optimal normalized noise scale is directly proportional to width. Wider networks, with their higher optimal noise scale, also achieve higher test accuracy. These observations hold for MLPs, ConvNets, and ResNets, and for two different parameterization schemes ("Standard" and "NTK"). We observe a similar trend with batch normalization for ResNets. Surprisingly, since the largest stable learning rate is bounded, the largest batch size consistent with the optimal normalized noise scale decreases as the width increases.
Importance Weighted Hierarchical Variational Inference
Authors: Artem Sobolev, Dmitry Vetrov
Date: 2019-05-08 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Variational Inference

Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior distribution. In turn, the expressivity of the variational family is largely limited by the requirement of having a tractable density function. To overcome this roadblock, we introduce a new family of variational upper bounds on a marginal log density in the case of hierarchical models (also known as latent variable models). We then give an upper bound on the Kullback-Leibler divergence and derive a family of increasingly tighter variational lower bounds on the otherwise intractable standard evidence lower bound for hierarchical variational distributions, enabling the use of more expressive approximate posteriors. We show that previously known methods, such as Hierarchical Variational Models, Semi-Implicit Variational Inference and Doubly Semi-Implicit Variational Inference can be seen as special cases of the proposed approach, and empirically demonstrate superior performance of the proposed method in a set of experiments.
Bayesian Optimization using Deep Gaussian Processes
Authors: Ali Hebbal, Loic Brevault, Mathieu Balesdent, El-Ghazali Talbi, Nouredine Melab
Date: 2019-05-07 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, global-optimization

Bayesian Optimization using Gaussian Processes is a popular approach to deal with the optimization of expensive black-box functions. However, because of the a priori on the stationarity of the covariance matrix of classic Gaussian Processes, this method may not be adapted for non-stationary functions involved in the optimization problem. To overcome this issue, a new Bayesian Optimization approach is proposed. It is based on Deep Gaussian Processes as surrogate models instead of classic Gaussian Processes. This modeling technique increases the power of representation to capture the non-stationarity by simply considering a functional composition of stationary Gaussian Processes, providing a multiple layer structure. This paper proposes a new algorithm for Global Optimization by coupling Deep Gaussian Processes and Bayesian Optimization. The specificities of this optimization method are discussed and highlighted with academic test cases. The performance of the proposed algorithm is assessed on analytical test cases and an aerospace design optimization problem and compared to the state-of-the-art stationary and non-stationary Bayesian Optimization approaches.
Two-stage Best-scored Random Forest for Large-scale Regression
Authors: Hanyuan Hang, Yingyi Chen, Johan A. K. Suykens
Date: 2019-05-09 00:00:00
Tasks: Computational Efficiency, Ensemble Learning, regression, Vocal Bursts Valence Prediction

We propose a novel method designed for large-scale regression problems, namely the two-stage best-scored random forest (TBRF). "Best-scored" means to select one regression tree with the best empirical performance out of a certain number of purely random regression tree candidates, and "two-stage" means to divide the original random tree splitting procedure into two: In stage one, the feature space is partitioned into non-overlapping cells; in stage two, child trees grow separately on these cells. The strengths of this algorithm can be summarized as follows: First of all, the pure randomness in TBRF leads to the almost optimal learning rates, and also makes ensemble learning possible, which resolves the boundary discontinuities long plaguing the existing algorithms. Secondly, the two-stage procedure paves the way for parallel computing, leading to computational efficiency. Last but not least, TBRF can serve as an inclusive framework where different mainstream regression strategies such as linear predictor and least squares support vector machines (LS-SVMs) can also be incorporated as value assignment approaches on leaves of the child trees, depending on the characteristics of the underlying data sets. Numerical assessments on comparisons with other state-of-the-art methods on several large-scale real data sets validate the promising prediction accuracy and high computational efficiency of our algorithm.
A Novel Adaptive Kernel for the RBF Neural Networks
Authors: Shujaat Khan, Imran Naseem, Roberto Togneri, Mohammed Bennamoun
Date: 2019-05-09 00:00:00
Tasks: General Classification

In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method thereby alleviating the need for predetermined weights. The proposed method is shown to outperform the manual fusion of the kernels on three major problems of estimation namely nonlinear system identification, pattern classification and function approximation.
A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables
Authors: Shu Wang, Jonathan G. Yabes, Chung-Chou H. Chang
Date: 2019-05-09 00:00:00
Tasks: Clustering, Variable Selection

Finite mixture model is an important branch of clustering methods and can be applied on data sets with mixed types of variables. However, challenges exist in its applications. First, it typically relies on the EM algorithm which could be sensitive to the choice of initial values. Second, biomarkers subject to limits of detection (LOD) are common to encounter in clinical data, which brings censored variables into finite mixture model. Additionally, researchers are recently getting more interest in variable importance due to the increasing number of variables that become available for clustering. To address these challenges, we propose a Bayesian finite mixture model to simultaneously conduct variable selection, account for biomarker LOD and obtain clustering results. We took a Bayesian approach to obtain parameter estimates and the cluster membership to bypass the limitation of the EM algorithm. To account for LOD, we added one more step in Gibbs sampling to iteratively fill in biomarker values below or above LODs. In addition, we put a spike-and-slab type of prior on each variable to obtain variable importance. Simulations across various scenarios were conducted to examine the performance of this method. Real data application on electronic health records was also conducted.
Best-scored Random Forest Density Estimation
Authors: Hanyuan Hang, Hongwei Wen
Date: 2019-05-09 00:00:00
Tasks: Density Estimation

This paper presents a brand new nonparametric density estimation strategy named the best-scored random forest density estimation whose effectiveness is supported by both solid theoretical analysis and significant experimental performance. The terminology best-scored stands for selecting one density tree with the best estimation performance out of a certain number of purely random density tree candidates and we then name the selected one the best-scored random density tree. In this manner, the ensemble of these selected trees that is the best-scored random density forest can achieve even better estimation results than simply integrating trees without selection. From the theoretical perspective, by decomposing the error term into two, we are able to carry out the following analysis: First of all, we establish the consistency of the best-scored random density trees under $L_1$-norm. Secondly, we provide the convergence rates of them under $L_1$-norm concerning with three different tail assumptions, respectively. Thirdly, the convergence rates under $L_{\infty}$-norm is presented. Last but not least, we also achieve the above convergence rates analysis for the best-scored random density forest. When conducting comparative experiments with other state-of-the-art density estimation approaches on both synthetic and real data sets, it turns out that our algorithm has not only significant advantages in terms of estimation accuracy over other methods, but also stronger resistance to the curse of dimensionality.
Reconstruction of Privacy-Sensitive Data from Protected Templates
Authors: Shideh Rezaeifar, Behrooz Razeghi, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
Date: 2019-05-08 00:00:00
Tasks: Privacy Preserving, Quantization

In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA). The STCA is a generalization of randomization techniques which includes random projections, lossy quantization, and addition of ambiguization noise to satisfy the privacy-utility trade-off requirements. The theoretical privacy-preserving properties of STCA have been validated on synthetic data. However, the applicability of STCA to real data and potential threats linked to reconstruction based on recent deep reconstruction algorithms are still open problems. Our results demonstrate that STCA still achieves the claimed theoretical performance when facing deep reconstruction attacks for the synthetic i.i.d. data, while for real images special measures are required to guarantee proper protection of the templates.
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
Authors: Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney
Date: 2019-05-08 00:00:00
Tasks: Subgroup Discovery

The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription. Toward this end, we propose a generative model that allows discovery from observational data of subgroups that demonstrate an enhanced or diminished causal effect due to treatment. Our approach models these sub-populations as a mixture distribution, using sparsity to enhance interpretability, while jointly learning nonlinear predictors of the potential outcomes to better adjust for confounding. The approach leads to human-interpretable insights on discovered subgroups, improving the practical utility for decision support
PerceptNet: Learning Perceptual Similarity of Haptic Textures in Presence of Unorderable Triplets
Authors: Priyadarshini Kumari, Siddhartha Chaudhuri, Subhasis Chaudhuri
Date: 2019-05-08 00:00:00
Tasks: Triplet

In order to design haptic icons or build a haptic vocabulary, we require a set of easily distinguishable haptic signals to avoid perceptual ambiguity, which in turn requires a way to accurately estimate the perceptual (dis)similarity of such signals. In this work, we present a novel method to learn such a perceptual metric based on data from human studies. Our method is based on a deep neural network that projects signals to an embedding space where the natural Euclidean distance accurately models the degree of dissimilarity between two signals. The network is trained only on non-numerical comparisons of triplets of signals, using a novel triplet loss that considers both types of triplets that are easy to order (inequality constraints), as well as those that are unorderable/ambiguous (equality constraints). Unlike prior MDS-based non-parametric approaches, our method can be trained on a partial set of comparisons and can embed new haptic signals without retraining the model from scratch. Extensive experimental evaluations show that our method is significantly more effective at modeling perceptual dissimilarity than alternatives.
Data-Efficient Mutual Information Neural Estimator
Authors: Xiao Lin, Indranil Sur, Samuel A. Nastase, Ajay Divakaran, Uri Hasson, Mohamed R. Amer
Date: 2019-05-08 00:00:00
Tasks: Meta-Learning

Measuring Mutual Information (MI) between high-dimensional, continuous, random variables from observed samples has wide theoretical and practical applications. Recent work, MINE (Belghazi et al. 2018), focused on estimating tight variational lower bounds of MI using neural networks, but assumed unlimited supply of samples to prevent overfitting. In real world applications, data is not always available at a surplus. In this work, we focus on improving data efficiency and propose a Data-Efficient MINE Estimator (DEMINE), by developing a relaxed predictive MI lower bound that can be estimated at higher data efficiency by orders of magnitudes. The predictive MI lower bound also enables us to develop a new meta-learning approach using task augmentation, Meta-DEMINE, to improve generalization of the network and further boost estimation accuracy empirically. With improved data-efficiency, our estimators enables statistical testing of dependency at practical dataset sizes. We demonstrate the effectiveness of our estimators on synthetic benchmarks and a real world fMRI data, with application of inter-subject correlation analysis.
Learning Embeddings into Entropic Wasserstein Spaces
Authors: Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon
Date: 2019-05-08 00:00:00
Tasks: Dimensionality Reduction, Word Embeddings

Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metric. Wasserstein spaces are much larger and more flexible than Euclidean spaces, in that they can successfully embed a wider variety of metric structures. We exploit this flexibility by learning an embedding that captures semantic information in the Wasserstein distance between embedded distributions. We examine empirically the representational capacity of our learned Wasserstein embeddings, showing that they can embed a wide variety of metric structures with smaller distortion than an equivalent Euclidean embedding. We also investigate an application to word embedding, demonstrating a unique advantage of Wasserstein embeddings: We can visualize the high-dimensional embedding directly, since it is a probability distribution on a low-dimensional space. This obviates the need for dimensionality reduction techniques like t-SNE for visualization.