pwc-archive/papers-with-abstracts huggingface dataset:
Techniques for Inferring Context-Free Lindenmayer Systems With Genetic Algorithm
Authors: Jason Bernard, Ian McQuillan
Date: 2019-05-15 00:00:00

Lindenmayer systems (L-systems) are a formal grammar system, where the most notable feature is a set of rewriting rules that are used to replace every symbol in a string in parallel; by repeating this process, a sequence of strings is produced. Some symbols in the strings may be interpreted as instructions for simulation software. Thus, the sequence can be used to model the steps of a process. Currently, creating an L-system for a specific process is done by hand by experts through much effort. The inductive inference problem attempts to infer an L-system from such a sequence of strings generated by an unknown system; this can be thought of as an intermediate step to inferring from a sequence of images. This paper evaluates and analyzes different genetic algorithm encoding schemes and mathematical properties for the L-system inductive inference problem. A new tool, the Plant Model Inference Tool for Context-Free L-systems (PMIT-D0L) is implemented based on these techniques. PMIT-D0L has been successfully evaluated on 28 known L-systems, with alphabets up to 31 symbols and a total sum of 281 symbols across the rewriting rules. PMIT-D0L can infer even the largest of these L-systems in less than a few seconds.
Towards Efficient Neural Networks On-a-chip: Joint Hardware-Algorithm Approaches
Authors: Xiaocong Du, Gokul Krishnan, Abinash Mohanty, Zheng Li, Gouranga Charan, Yu Cao
Date: 2019-05-28 00:00:00

Machine learning algorithms have made significant advances in many applications. However, their hardware implementation on the state-of-the-art platforms still faces several challenges and are limited by various factors, such as memory volume, memory bandwidth and interconnection overhead. The adoption of the crossbar architecture with emerging memory technology partially solves the problem but induces process variation and other concerns. In this paper, we will present novel solutions to two fundamental issues in crossbar implementation of Artificial Intelligence (AI) algorithms: device variation and insufficient interconnections. These solutions are inspired by the statistical properties of algorithms themselves, especially the redundancy in neural network nodes and connections. By Random Sparse Adaptation and pruning the connections following the Small-World model, we demonstrate robust and efficient performance on representative datasets such as MNIST and CIFAR-10. Moreover, we present Continuous Growth and Pruning algorithm for future learning and adaptation on hardware.
Self-adaptive Potential-based Stopping Criteria for Particle Swarm Optimization
Authors: Bernd Bassimir, Manuel Schmitt, Rolf Wanka
Date: 2019-05-29 00:00:00

We study the variant of Particle Swarm Optimization (PSO) that applies random velocities in a dimension instead of the regular velocity update equations as soon as the so-called potential of the swarm falls below a certain bound in this dimension, arbitrarily set by the user. In this case, the swarm performs a forced move. In this paper, we are interested in how, by counting the forced moves, the swarm can decide for itself to stop its movement because it is improbable to find better solution candidates as it already has found. We formally prove that when the swarm is close to a (local) optimum, it behaves like a blind-searching cloud, and that the frequency of forced moves exceeds a certain, objective function-independent value. Based on this observation, we define stopping criteria and evaluate them experimentally showing that good solution candidates can be found much faster than applying other criteria.
Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification
Authors: Sheng-Jie Liu, Haowen Luo, Ying Tu, Zhi He, Jun Li
Date: 2018-07-22 00:00:00
Proceeding: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018 7
Tasks: Active Learning, Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, Remote Sensing Image Classification

In this paper, we propose a wide contextual residual network (WCRN) with active learning (AL) for remote sensing image (RSI)
classification. Although ResNets have achieved great success in various applications (e.g. RSI classification), its performance is limited by the requirement of abundant labeled samples. As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples. Specifically, we first design
a wide contextual residual network for RSI classification. We then integrate it with AL to achieve good machine generalization with
limited number of training sampling. Experimental results on the University of Pavia and Flevoland datasets demonstrate that the proposed WCRN with AL can significantly reduce the needs of samples.
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
Authors: Asma Ghandeharioun, Judy Hanwen Shen, Natasha Jaques, Craig Ferguson, Noah Jones, Agata Lapedriza, Rosalind Picard
Date: 2019-06-21 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Dialogue Evaluation, Knowledge Distillation, Open-Domain Dialog

Building an open-domain conversational agent is a challenging problem. Current evaluation methods, mostly post-hoc judgments of static conversation, do not capture conversation quality in a realistic interactive context. In this paper, we investigate interactive human evaluation and provide evidence for its necessity; we then introduce a novel, model-agnostic, and dataset-agnostic method to approximate it. In particular, we propose a self-play scenario where the dialog system talks to itself and we calculate a combination of proxies such as sentiment and semantic coherence on the conversation trajectory. We show that this metric is capable of capturing the human-rated quality of a dialog model better than any automated metric known to-date, achieving a significant Pearson correlation (r>.7, p<.05). To investigate the strengths of this novel metric and interactive evaluation in comparison to state-of-the-art metrics and human evaluation of static conversations, we perform extended experiments with a set of models, including several that make novel improvements to recent hierarchical dialog generation architectures through sentiment and semantic knowledge distillation on the utterance level. Finally, we open-source the interactive evaluation platform we built and the dataset we collected to allow researchers to efficiently deploy and evaluate dialog models.
SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors
Authors: Guillaume Lambard, Ekaterina Gracheva
Date: 2019-06-20 00:00:00

There is more and more evidence that machine learning can be successfully applied in materials science and related fields. However, datasets in these fields are often quite small ($\ll1000$ samples). It makes the most advanced machine learning techniques remain neglected, as they are considered to be applicable to big data only. Moreover, materials informatics methods often rely on human-engineered descriptors, that should be carefully chosen, or even created, to fit the physicochemical property that one intends to predict. In this article, we propose a new method that tackles both the issue of small datasets and the difficulty of task-specific descriptors development. The SMILES-X is an autonomous pipeline for molecular compounds characterisation based on a \{Embed-Encode-Attend-Predict\} neural architecture with a data-specific Bayesian hyper-parameters optimisation. The only input to the architecture -- the SMILES strings -- are de-canonicalised in order to efficiently augment the data. One of the key features of the architecture is the attention mechanism, which enables the interpretation of output predictions without extra computational cost. The SMILES-X shows new state-of-the-art results in the inference of aqueous solubility ($\overline{RMSE}_{test} \simeq 0.57 \pm 0.07$ mols/L), hydration free energy ($\overline{RMSE}_{test} \simeq 0.81 \pm 0.22$ kcal/mol, which is $\sim 24.5\%$ better than molecular dynamics simulations), and octanol/water distribution coefficient ($\overline{RMSE}_{test} \simeq 0.59 \pm 0.02$ for LogD at pH 7.4) of molecular compounds. The SMILES-X is intended to become an important asset in the toolkit of materials scientists and chemists. The source code for the SMILES-X is available at \href{https://github.com/GLambard/SMILES-X}{github.com/GLambard/SMILES-X}.
Query-driven PAC-Learning for Reasoning
Authors: Brendan Juba
Date: 2019-06-24 00:00:00
Tasks: PAC learning

We consider the problem of learning rules from a data set that support a proof of a given query, under Valiant's PAC-Semantics. We show how any backward proof search algorithm that is sufficiently oblivious to the contents of its knowledge base can be modified to learn such rules while it searches for a proof using those rules. We note that this gives such algorithms for standard logics such as chaining and resolution.
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Authors: Justin Domke, Daniel Sheldon
Date: 2019-06-24 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Variational Inference

Recent work in variational inference (VI) uses ideas from Monte Carlo estimation to tighten the lower bounds on the log-likelihood that are used as objectives. However, there is no systematic understanding of how optimizing different objectives relates to approximating the posterior distribution. Developing such a connection is important if the ideas are to be applied to inference-i.e., applications that require an approximate posterior and not just an approximation of the log-likelihood. Given a VI objective defined by a Monte Carlo estimator of the likelihood, we use a "divide and couple" procedure to identify augmented proposal and target distributions. The divergence between these is equal to the gap between the VI objective and the log-likelihood. Thus, after maximizing the VI objective, the augmented variational distribution may be used to approximate the posterior distribution.
GANalyze: Toward Visual Definitions of Cognitive Image Properties
Authors: Authors, :, Lore Goetschalckx, Alex Andonian, Aude Oliva, Phillip Isola
Date: 2019-06-24 00:00:00
Proceeding: ICCV 2019 10

We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability, aesthetics, and emotional valence. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more or less memorable? GANs allow us to generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold in directions that increase memorability, we can visualize what it looks like for a particular generated image to become more or less memorable. The resulting ``visual definitions" surface image properties (like ``object size") that may underlie memorability. Through behavioral experiments, we verify that our method indeed discovers image manipulations that causally affect human memory performance. We further demonstrate that the same framework can be used to analyze image aesthetics and emotional valence. Visit the GANalyze website at http://ganalyze.csail.mit.edu/.
CMRNet: Camera to LiDAR-Map Registration
Authors: Daniele Cattaneo, Matteo Vaghi, Augusto Luis Ballardini, Simone Fontana, Domenico Giorgio Sorrenti, Wolfram Burgard
Date: 2019-06-24 00:00:00
Tasks: Camera Localization

In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i.e. CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.27m and 1.07deg median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17deg. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.
Implicitly Learning to Reason in First-Order Logic
Authors: Vaishak Belle, Brendan Juba
Date: 2019-06-24 00:00:00
Proceeding: NeurIPS 2019 12

We consider the problem of answering queries about formulas of first-order logic based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed probability distribution. PAC semantics, introduced by Valiant, is one rigorous, general proposal for learning to reason in formal languages: although weaker than classical entailment, it allows for a powerful model theoretic framework for answering queries while requiring minimal assumptions about the form of the distribution in question. To date, however, the most significant limitation of that approach, and more generally most machine learning approaches with robustness guarantees, is that the logical language is ultimately essentially propositional, with finitely many atoms. Indeed, the theoretical findings on the learning of relational theories in such generality have been resoundingly negative. This is despite the fact that first-order logic is widely argued to be most appropriate for representing human knowledge. In this work, we present a new theoretical approach to robustly learning to reason in first-order logic, and consider universally quantified clauses over a countably infinite domain. Our results exploit symmetries exhibited by constants in the language, and generalize the notion of implicit learnability to show how queries can be computed against (implicitly) learned first-order background knowledge.
Remote Estimation of Free-Flow Speeds
Authors: Weilian Song, Tawfiq Salem, Hunter Blanton, Nathan Jacobs
Date: 2019-06-24 00:00:00

We propose an automated method to estimate a road segment's free-flow speed from overhead imagery and road metadata. The free-flow speed of a road segment is the average observed vehicle speed in ideal conditions, without congestion or adverse weather. Standard practice for estimating free-flow speeds depends on several road attributes, including grade, curve, and width of the right of way. Unfortunately, many of these fine-grained labels are not always readily available and are costly to manually annotate. To compensate, our model uses a small, easy to obtain subset of road features along with aerial imagery to directly estimate free-flow speed with a deep convolutional neural network (CNN). We evaluate our approach on a large dataset, and demonstrate that using imagery alone performs nearly as well as the road features and that the combination of imagery with road features leads to the highest accuracy.
LMVP: Video Predictor with Leaked Motion Information
Authors: Dong Wang, Yitong Li, Wei Cao, Liqun Chen, Qi Wei, Lawrence Carin
Date: 2019-06-24 00:00:00

We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by capturing the spatial and temporal dependencies from given inputs. The motion is modeled by a newly proposed component, motion guider, which plays the role of both learner and teacher. Specifically, it {\em learns} the temporal features from real data and {\em guides} the generator to predict future frames. The spatial consistency in video is modeled by an adaptive filtering network. To further ensure the spatio-temporal consistency of the prediction, a discriminator is also adopted to distinguish the real and generated frames. Further, the discriminator leaks information to the motion guider and the generator to help the learning of motion. The proposed LMVP can effectively learn the static and temporal features in videos without the need for human labeling. Experiments on synthetic and real data demonstrate that LMVP can yield state-of-the-art results.
Audio-Visual Kinship Verification
Authors: Xiaoting Wu, Eric Granger, Xiaoyi Feng
Date: 2019-06-24 00:00:00
Tasks: Face Verification, Kinship Verification

Visual kinship verification entails confirming whether or not two individuals in a given pair of images or videos share a hypothesized kin relation. As a generalized face verification task, visual kinship verification is particularly difficult with low-quality found Internet data. Due to uncontrolled variations in background, pose, facial expression, blur, illumination and occlusion, state-of-the-art methods fail to provide high level of recognition accuracy. As with many other visual recognition tasks, kinship verification may benefit from combining visual and audio signals. However, voice-based kinship verification has received very little prior attention. We hypothesize that the human voice contains kin-related cues that are complementary to visual cues. In this paper, we address, for the first time, the use of audio-visual information from face and voice modalities to perform kinship verification. We first propose a new multi-modal kinship dataset, called TALking KINship (TALKIN), that contains several pairs of Internet-quality video sequences. Using TALKIN, we then study the utility of various kinship verification methods including traditional local feature based methods, statistical methods and more recent deep learning approaches. Then, early and late fusion methods are evaluated on the TALKIN dataset for the study of kinship verification with both face and voice modalities. Finally, we propose a deep Siamese fusion network with contrastive loss for multi-modal fusion of kinship relations. Extensive experiments on the TALKIN dataset indicate that by combining face and voice modalities, the proposed Siamese network can provide a significantly higher level of accuracy compared to baseline uni-modal and multi-modal fusion techniques. Experimental results also indicate that audio (vocal) information is complementary (to facial information) and useful for kinship verification.
An Empirical Comparison of FAISS and FENSHSES for Nearest Neighbor Search in Hamming Space
Authors: Cun Mu, Binwei Yang, Zheng Yan
Date: 2019-06-24 00:00:00

In this paper, we compare the performances of FAISS and FENSHSES on nearest neighbor search in Hamming space--a fundamental task with ubiquitous applications in nowadays eCommerce. Comprehensive evaluations are made in terms of indexing speed, search latency and RAM consumption. This comparison is conducted towards a better understanding on trade-offs between nearest neighbor search systems implemented in main memory and the ones implemented in secondary memory, which is largely unaddressed in literature.
Density-based Clustering with Best-scored Random Forest
Authors: Hanyuan Hang, Yuchao Cai, Hanfang Yang
Date: 2019-06-24 00:00:00
Tasks: Clustering

Single-level density-based approach has long been widely acknowledged to be a conceptually and mathematically convincing clustering method. In this paper, we propose an algorithm called "best-scored clustering forest" that can obtain the optimal level and determine corresponding clusters. The terminology "best-scored" means to select one random tree with the best empirical performance out of a certain number of purely random tree candidates. From the theoretical perspective, we first show that consistency of our proposed algorithm can be guaranteed. Moreover, under certain mild restrictions on the underlying density functions and target clusters, even fast convergence rates can be achieved. Last but not least, comparisons with other state-of-the-art clustering methods in the numerical experiments demonstrate accuracy of our algorithm on both synthetic data and several benchmark real data sets.
Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
Authors: Mohammad Eslami, Solale Tabarestani, Shadi Albarqouni, Ehsan Adeli, Nassir Navab, Malek Adjouadi
Date: 2019-06-24 00:00:00
Tasks: Decision Making, Generative Adversarial Network, Organ Segmentation, Translation

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks. The architectural design of this model, which relies on a conditional generative adversarial network, reveals the process on how the well-established pix2pix network (image-to-image network) is modified to fit the need for multitasking and extending it to the new image-to-images architecture. The developed source code of this multitask model is shared publicly on Github as the first attempt for providing the two-task pix2pix extension, a supervised/paired/aligned/registered image-to-images translation which would be useful in many multitask applications. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. The comparison with state-of-the-art algorithms along with ablation study and a demonstration video are provided to evaluate efficacy and gauge the merits of the proposed approach.
Analyzing CART
Authors: Jason M. Klusowski
Date: 2019-06-24 00:00:00
Tasks: Binary Classification, regression

Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For binary classification and regression models, this approach recursively divides the data into two near-homogenous daughter nodes according to a split point that maximizes the reduction in sum of squares error (the impurity) along a particular variable. This paper aims to study the bias and adaptive properties of regression trees constructed with CART. In doing so, we derive an interesting connection between the bias and the mean decrease in impurity (MDI) measure of variable importance---a tool widely used for model interpretability---defined as the sum of impurity reductions over all non-terminal nodes in the tree. In particular, we show that the probability content of a terminal subnode for a variable is small when the MDI for that variable is large and that this relationship is exponential---confirming theoretically that decision trees with CART have small bias and are adaptive to signal strength and direction. Finally, we apply these individual tree bounds to tree ensembles and show consistency of Breiman's random forests. The context is surprisingly general and applies to a wide variety of multivariable data generating distributions and regression functions. The main technical tool is an exact characterization of the conditional probability content of the daughter nodes arising from an optimal split, in terms of the partial dependence function and reduction in impurity.