pwc-archive/papers-with-abstracts huggingface dataset:
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
Authors: Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Čeperić, Marin Soljačić
Date: 2019-12-10 00:00:00
Tasks: Explainable Models, regression, scientific discovery, Symbolic Regression

Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the training data set. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing tasks, but are often seen as black-box models that are difficult to interpret and typically extrapolate poorly. Here we use a neural network-based architecture for symbolic regression called the Equation Learner (EQL) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation. To demonstrate the power of such systems, we study their performance on several substantially different tasks. First, we show that the neural network can perform symbolic regression and learn the form of several functions. Next, we present an MNIST arithmetic task where a separate part of the neural network extracts the digits. Finally, we demonstrate prediction of dynamical systems where an unknown parameter is extracted through an encoder. We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared to a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery.
libmolgrid: GPU Accelerated Molecular Gridding for Deep Learning Applications
Authors: Jocelyn Sunseri, David Ryan Koes
Date: 2019-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Data Augmentation, Deep Learning, GPU

There are many ways to represent a molecule as input to a machine learning model and each is associated with loss and retention of certain kinds of information. In the interest of preserving three-dimensional spatial information, including bond angles and torsions, we have developed libmolgrid, a general-purpose library for representing three-dimensional molecules using multidimensional arrays. This library also provides functionality for composing batches of data suited to machine learning workflows, including data augmentation, class balancing, and example stratification according to a regression variable or data subgroup, and it further supports temporal and spatial recurrences over that data to facilitate work with recurrent neural networks, dynamical data, and size extensive modeling. It was designed for seamless integration with popular deep learning frameworks, including Caffe, PyTorch, and Keras, providing good performance by leveraging graphical processing units (GPUs) for computationally-intensive tasks and efficient memory usage through the use of memory views over preallocated buffers. libmolgrid is a free and open source project that is actively supported, serving the growing need in the molecular modeling community for tools that streamline the process of data ingestion, representation construction, and principled machine learning model development.
Qualitative Numeric Planning: Reductions and Complexity
Authors: Blai Bonet, Hector Geffner
Date: 2019-12-10 00:00:00

Qualitative numerical planning is classical planning extended with non-negative real variables that can be increased or decreased "qualitatively", i.e., by positive indeterminate amounts. While deterministic planning with numerical variables is undecidable in general, qualitative numerical planning is decidable and provides a convenient abstract model for generalized planning. The solutions to qualitative numerical problems (QNPs) were shown to correspond to the strong cyclic solutions of an associated fully observable non-deterministic (FOND) problem that terminate. This leads to a generate-and-test algorithm for solving QNPs where solutions to a FOND problem are generated one by one and tested for termination. The computational shortcomings of this approach for solving QNPs, however, are that it is not simple to amend FOND planners to generate all solutions, and that the number of solutions to check can be doubly exponential in the number of variables. In this work we address these limitations while providing additional insights on QNPs. More precisely, we introduce two polynomial-time reductions, one from QNPs to FOND problems and the other from FOND problems to QNPs both of which do not involve termination tests. A result of these reductions is that QNPs are shown to have the same expressive power and the same complexity as FOND problems.
Detection of Collision-Prone Vehicle Behavior at Intersections using Siamese Interaction LSTM
Authors: Debaditya Roy, Tetsuhiro Ishizaka, Krishna Mohan C., Atsushi Fukuda
Date: 2019-12-10 00:00:00

As a large proportion of road accidents occur at intersections, monitoring traffic safety of intersections is important. Existing approaches are designed to investigate accidents in lane-based traffic. However, such approaches are not suitable in a lane-less mixed-traffic environment where vehicles often ply very close to each other. Hence, we propose an approach called Siamese Interaction Long Short-Term Memory network (SILSTM) to detect collision prone vehicle behavior. The SILSTM network learns the interaction trajectory of a vehicle that describes the interactions of a vehicle with its neighbors at an intersection. Among the hundreds of interactions for every vehicle, there maybe only some interactions which may be unsafe and hence, a temporal attention layer is used in the SILSTM network. Furthermore, the comparison of interaction trajectories requires labeling the trajectories as either unsafe or safe, but such a distinction is highly subjective, especially in lane-less traffic. Hence, in this work, we compute the characteristics of interaction trajectories involved in accidents using the collision energy model. The interaction trajectories that match accident characteristics are labeled as unsafe while the rest are considered safe. Finally, there is no existing dataset that allows us to monitor a particular intersection for a long duration. Therefore, we introduce the SkyEye dataset that contains 1 hour of continuous aerial footage from each of the 4 chosen intersections in the city of Ahmedabad in India. A detailed evaluation of SILSTM on the SkyEye dataset shows that unsafe (collision-prone) interaction trajectories can be effectively detected at different intersections.
Learning Depth-Guided Convolutions for Monocular 3D Object Detection
Authors: Mingyu Ding, Yuqi Huo, Hongwei Yi, Zhe Wang, Jianping Shi, Zhiwu Lu, Ping Luo
Date: 2019-12-10 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Object Detection, Monocular 3D Object Detection, Object, object-detection, Object Detection, Vehicle Pose Estimation

3D object detection from a single image without LiDAR is a challenging task due to the lack of accurate depth information. Conventional 2D convolutions are unsuitable for this task because they fail to capture local object and its scale information, which are vital for 3D object detection. To better represent 3D structure, prior arts typically transform depth maps estimated from 2D images into a pseudo-LiDAR representation, and then apply existing 3D point-cloud based object detectors. However, their results depend heavily on the accuracy of the estimated depth maps, resulting in suboptimal performance. In this work, instead of using pseudo-LiDAR representation, we improve the fundamental 2D fully convolutions by proposing a new local convolutional network (LCN), termed Depth-guided Dynamic-Depthwise-Dilated LCN (D$^4$LCN), where the filters and their receptive fields can be automatically learned from image-based depth maps, making different pixels of different images have different filters. D$^4$LCN overcomes the limitation of conventional 2D convolutions and narrows the gap between image representation and 3D point cloud representation. Extensive experiments show that D$^4$LCN outperforms existing works by large margins. For example, the relative improvement of D$^4$LCN against the state-of-the-art on KITTI is 9.1\% in the moderate setting. The code is available at https://github.com/dingmyu/D4LCN.
Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering
Authors: Mohsen Ghassemi Parsa, Hadi Zare, Mehdi Ghatee
Date: 2019-12-10 00:00:00
Tasks: Clustering, feature selection, Representation Learning

Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also captures the discriminative information based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state of the art methods.
Unsupervised Transfer Learning via BERT Neuron Selection
Authors: Mehrdad Valipour, En-Shiun Annie Lee, Jaime R. Jamacaro, Carolina Bessega
Date: 2019-12-10 00:00:00
Tasks: Natural Language Inference, Sentence, Sentence Similarity, Sentiment Analysis, Transfer Learning

Recent advancements in language representation models such as BERT have led to a rapid improvement in numerous natural language processing tasks. However, language models usually consist of a few hundred million trainable parameters with embedding space distributed across multiple layers, thus making them challenging to be fine-tuned for a specific task or to be transferred to a new domain. To determine whether there are task-specific neurons that can be exploited for unsupervised transfer learning, we introduce a method for selecting the most important neurons to solve a specific classification task. This algorithm is further extended to multi-source transfer learning by computing the importance of neurons for several single-source transfer learning scenarios between different subsets of data sources. Besides, a task-specific fingerprint for each data source is obtained based on the percentage of the selected neurons in each layer. We perform extensive experiments in unsupervised transfer learning for sentiment analysis, natural language inference and sentence similarity, and compare our results with the existing literature and baselines. Significantly, we found that the source and target data sources with higher degrees of similarity between their task-specific fingerprints demonstrate a better transferability property. We conclude that our method can lead to better performance using just a few hundred task-specific and interpretable neurons.
Training Provably Robust Models by Polyhedral Envelope Regularization
Authors: Chen Liu, Mathieu Salzmann, Sabine Süsstrunk
Date: 2019-12-10 00:00:00

Training certifiable neural networks enables one to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to bound the adversary-free region in the neighborhood of the input data by a polyhedral envelope, which yields finer-grained certified robustness. We further introduce polyhedral envelope regularization (PER) to encourage larger polyhedral envelopes and thus improve the provable robustness of the models. We demonstrate the flexibility and effectiveness of our framework on standard benchmarks; it applies to networks of different architectures and general activation functions. Compared with the state-of-the-art methods, PER has very little computational overhead and better robustness guarantees without over-regularizing the model.
edBB: Biometrics and Behavior for Assessing Remote Education
Authors: Javier Hernandez-Ortega, Roberto Daza, Aythami Morales, Julian Fierrez, Javier Ortega-Garcia
Date: 2019-12-10 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We present a platform for student monitoring in remote education consisting of a collection of sensors and software that capture biometric and behavioral data. We define a collection of tasks to acquire behavioral data that can be useful for facing the existing challenges in automatic student monitoring during remote evaluation. Additionally, we release an initial database including data from 20 different users completing these tasks with a set of basic sensors: webcam, microphone, mouse, and keyboard; and also from more advanced sensors: NIR camera, smartwatch, additional RGB cameras, and an EEG band. Information from the computer (e.g. system logs, MAC, IP, or web browsing history) is also stored. During each acquisition session each user completed three different types of tasks generating data of different nature: mouse and keystroke dynamics, face data, and audio data among others. The tasks have been designed with two main goals in mind: i) analyse the capacity of such biometric and behavioral data for detecting anomalies during remote evaluation, and ii) study the capability of these data, i.e. EEG, ECG, or NIR video, for estimating other information about the users such as their attention level, the presence of stress, or their pulse rate.
A Novel Topology for End-to-end Temporal Classification and Segmentation with Recurrent Neural Network
Authors: Taiyang Zhao
Date: 2019-12-10 00:00:00
Tasks: Classification, General Classification, Segmentation, speech-recognition, Speech Recognition

Connectionist temporal classification (CTC) has matured as an alignment free to sequence transduction and shows competitive for end-to-end speech recognition. In the CTC topology, the blank symbol occupies more than half of the state trellis, which results the spike phenomenon of the non-blank symbols. For classification task, the spikes work quite well, but as to the segmentation task it does not provide boundaries information. In this paper, a novel topology is introduced to combine the temporal classification and segmentation ability in one framework.
Frivolous Units: Wider Networks Are Not Really That Wide
Authors: Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman
Date: 2019-12-10 00:00:00

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network's width is increased. Recent evidence suggests that developing compressible representations is key for adjusting the complexity of large networks to the learning task at hand. However, these compressible representations are poorly understood. A promising strand of research inspired from biology is understanding representations at the unit level as it offers a more granular and intuitive interpretation of the neural mechanisms. In order to better understand what facilitates increases in width without decreases in accuracy, we ask: Are there mechanisms at the unit level by which networks control their effective complexity as their width is increased? If so, how do these depend on the architecture, dataset, and training parameters? We identify two distinct types of "frivolous" units that proliferate when the network's width is increased: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others. These units imply complexity constraints as the function the network represents could be expressed by a network without them. We also identify how the development of these units can be influenced by architecture and a number of training factors. Together, these results help to explain why the accuracy of DNNs does not degrade when width is increased and highlight the importance of frivolous units toward understanding implicit regularization in DNNs.
GeBioToolkit: Automatic Extraction of Gender-Balanced Multilingual Corpus of Wikipedia Biographies
Authors: Marta R. Costa-jussà, Pau Li Lin, Cristina España-Bonet
Date: 2019-12-10 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentence

We introduce GeBioToolkit, a tool for extracting multilingual parallel corpora at sentence level, with document and gender information from Wikipedia biographies. Despite thegender inequalitiespresent in Wikipedia, the toolkit has been designed to extract corpus balanced in gender. While our toolkit is customizable to any number of languages (and different domains), in this work we present a corpus of 2,000 sentences in English, Spanish and Catalan, which has been post-edited by native speakers to become a high-quality dataset for machinetranslation evaluation. While GeBioCorpus aims at being one of the first non-synthetic gender-balanced test datasets, GeBioToolkit aims at paving the path to standardize procedures to produce gender-balanced datasets
Deep Latent Factor Model for Collaborative Filtering
Authors: Aanchal Mongia, Neha Jhamb, Emilie Chouzenoux, Angshul Majumdar
Date: 2019-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Collaborative Filtering, Deep Learning, model, Recommendation Systems

Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques.
Efficient Differentiable Neural Architecture Search with Meta Kernels
Authors: Shoufa Chen, Yunpeng Chen, Shuicheng Yan, Jiashi Feng
Date: 2019-12-10 00:00:00
Tasks: Neural Architecture Search

The searching procedure of neural architecture search (NAS) is notoriously time consuming and cost prohibitive.To make the search space continuous, most existing gradient-based NAS methods relax the categorical choice of a particular operation to a softmax over all possible operations and calculate the weighted sum of multiple features, resulting in a large memory requirement and a huge computation burden. In this work, we propose an efficient and novel search strategy with meta kernels. We directly encode the supernet from the perspective on convolution kernels and "shrink" multiple convolution kernel candidates into a single one before these candidates operate on the input feature. In this way, only a single feature is generated between two intermediate nodes. The memory for storing intermediate features and the resource budget for conducting convolution operations are both reduced remarkably. Despite high efficiency, our search strategy can search in a more fine-grained way than existing works and increases the capacity for representing possible networks. We demonstrate the effectiveness of our search strategy by conducting extensive experiments. Specifically, our method achieves 77.0% top-1 accuracy on ImageNet benchmark dataset with merely 357M FLOPs, outperforming both EfficientNet and MobileNetV3 under the same FLOPs constraints. Compared to models discovered by the start-of-the-art NAS method, our method achieves the same (sometimes even better) performance, while faster by three orders of magnitude.
Fraud detection in telephone conversations for financial services using linguistic features
Authors: Nikesh Bajaj, Tracy Goodluck Constance, Marvin Rajwadi, Julie Wall, Mansour Moniri, Cornelius Glackin, Nigel Cannings, Chris Woodruff, James Laird
Date: 2019-12-10 00:00:00
Tasks: Fraud Detection

Detecting the elements of deception in a conversation is one of the most challenging problems for the AI community. It becomes even more difficult to design a transparent system, which is fully explainable and satisfies the need for financial and legal services to be deployed. This paper presents an approach for fraud detection in transcribed telephone conversations using linguistic features. The proposed approach exploits the syntactic and semantic information of the transcription to extract both the linguistic markers and the sentiment of the customer's response. We demonstrate the results on real-world financial services data using simple, robust and explainable classifiers such as Naive Bayes, Decision Tree, Nearest Neighbours, and Support Vector Machines.
Transformed Subspace Clustering
Authors: Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux
Date: 2019-12-10 00:00:00
Tasks: Benchmarking, Clustering, Image Clustering

Subspace clustering assumes that the data is sepa-rable into separate subspaces. Such a simple as-sumption, does not always hold. We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces. To achieve the intended goal, we embed subspace clustering techniques (locally linear manifold clustering, sparse sub-space clustering and low rank representation) into transform learning. The entire formulation is jointly learnt; giving rise to a new class of meth-ods called transformed subspace clustering (TSC). In order to account for non-linearity, ker-nelized extensions of TSC are also proposed. To test the performance of the proposed techniques, benchmarking is performed on image clustering and document clustering datasets. Comparison with state-of-the-art clustering techniques shows that our formulation improves upon them.
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
Authors: Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury
Date: 2019-12-10 00:00:00
Tasks: Information Retrieval, Re-Ranking, Retrieval

In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results. We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval results and inspect the inner workings and fine-grained results of neural re-ranking models. The explorer includes a categorized overview of the available queries, as well as an individual query result view with various options to highlight semantic connections between query-document pairs. The Neural-IR-Explorer is available at: https://neural-ir-explorer.ec.tuwien.ac.at/
End-to-end facial and physiological model for Affective Computing and applications
Authors: Joaquim Comas, Decky Aspandi, Xavier Binefa
Date: 2019-12-10 00:00:00
Tasks: Arousal Estimation, Deep Learning, Emotion Recognition

In recent years, Affective Computing and its applications have become a fast-growing research topic. Furthermore, the rise of Deep Learning has introduced significant improvements in the emotion recognition system compared to classical methods. In this work, we propose a multi-modal emotion recognition model based on deep learning techniques using the combination of peripheral physiological signals and facial expressions. Moreover, we present an improvement to proposed models by introducing latent features extracted from our internal Bio Auto-Encoder (BAE). Both models are trained and evaluated on AMIGOS datasets reporting valence, arousal, and emotion state classification. Finally, to demonstrate a possible medical application in affective computing using deep learning techniques, we applied the proposed method to the assessment of anxiety therapy. To this purpose, a reduced multi-modal database has been collected by recording facial expressions and peripheral signals such as Electrocardiogram (ECG) and Galvanic Skin Response (GSR) of each patient. Valence and arousal estimation was extracted using the proposed model from the beginning until the end of the therapy, with successful evaluation to the different emotional changes in the temporal domain.