pwc-archive/papers-with-abstracts huggingface dataset:
NagE: Non-Abelian Group Embedding for Knowledge Graphs
Authors: Tong Yang, Long Sha, Pengyu Hong
Date: 2020-05-22 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs

We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for designing embedding models. Our theoretical analysis explores merely the intrinsic property of the embedding problem itself hence is model-independent. Motivated by the theoretical analysis, we have proposed a group theory-based knowledge graph embedding framework, in which relations are embedded as group elements, and entities are represented by vectors in group action spaces. We provide a generic recipe to construct embedding models associated with two instantiating examples: SO3E and SU2E, both of which apply a continuous non-Abelian group as the relation embedding. Empirical experiments using these two exampling models have shown state-of-the-art results on benchmark datasets.
Head2Head: Video-based Neural Head Synthesis
Authors: Mohammad Rami Koujan, Michail Christos Doukas, Anastasios Roussos, Stefanos Zafeiriou
Date: 2020-05-22 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we propose a novel machine learning architecture for facial reenactment. In particular, contrary to the model-based approaches or recent frame-based methods that use Deep Convolutional Neural Networks (DCNNs) to generate individual frames, we propose a novel method that (a) exploits the special structure of facial motion (paying particular attention to mouth motion) and (b) enforces temporal consistency. We demonstrate that the proposed method can transfer facial expressions, pose and gaze of a source actor to a target video in a photo-realistic fashion more accurately than state-of-the-art methods.
A Concise Review of Recent Few-shot Meta-learning Methods
Authors: Xiaoxu Li, Zhuo Sun, Jing-Hao Xue, Zhanyu Ma
Date: 2020-05-22 00:00:00
Tasks: Meta-Learning

Few-shot meta-learning has been recently reviving with expectations to mimic humanity's fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude this review with some vital current challenges and future prospects in few-shot meta-learning.
When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Data
Authors: Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe
Date: 2020-05-21 00:00:00
Tasks: Dictionary Learning

We present a new Deep Dictionary Learning and Coding Network (DDLCN) for image recognition tasks with limited data. The proposed DDLCN has most of the standard deep learning layers (e.g., input/output, pooling, fully connected, etc.), but the fundamental convolutional layers are replaced by our proposed compound dictionary learning and coding layers. The dictionary learning learns an over-complete dictionary for input training data. At the deep coding layer, a locality constraint is added to guarantee that the activated dictionary bases are close to each other. Then the activated dictionary atoms are assembled and passed to the compound dictionary learning and coding layers. In this way, the activated atoms in the first layer can be represented by the deeper atoms in the second dictionary. Intuitively, the second dictionary is designed to learn the fine-grained components shared among the input dictionary atoms, thus a more informative and discriminative low-level representation of the dictionary atoms can be obtained. We empirically compare DDLCN with several leading dictionary learning methods and deep learning models. Experimental results on five popular datasets show that DDLCN achieves competitive results compared with state-of-the-art methods when the training data is limited. Code is available at https://github.com/Ha0Tang/DDLCN.
LEAF: Latent Exploration Along the Frontier
Authors: Homanga Bharadhwaj, Animesh Garg, Florian Shkurti
Date: 2020-05-21 00:00:00

Self-supervised goal proposal and reaching is a key component for exploration and efficient policy learning algorithms. Such a self-supervised approach without access to any oracle goal sampling distribution requires deep exploration and commitment so that long horizon plans can be efficiently discovered. In this paper, we propose an exploration framework, which learns a dynamics-aware manifold of reachable states. For a goal, our proposed method deterministically visits a state at the current frontier of reachable states (commitment/reaching) and then stochastically explores to reach the goal (exploration). This allocates exploration budget near the frontier of the reachable region instead of its interior. We target the challenging problem of policy learning from initial and goal states specified as images, and do not assume any access to the underlying ground-truth states of the robot and the environment. To keep track of reachable latent states, we propose a distance-conditioned reachability network that is trained to infer whether one state is reachable from another within the specified latent space distance. Given an initial state, we obtain a frontier of reachable states from that state. By incorporating a curriculum for sampling easier goals (closer to the start state) before more difficult goals, we demonstrate that the proposed self-supervised exploration algorithm, superior performance compared to existing baselines on a set of challenging robotic environments.https://sites.google.com/view/leaf-exploration
Large scale evaluation of importance maps in automatic speech recognition
Authors: Viet Anh Trinh, Michael I Mandel
Date: 2020-05-21 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Structured Prediction

In this paper, we propose a metric that we call the structured saliency benchmark (SSBM) to evaluate importance maps computed for automatic speech recognizers on individual utterances. These maps indicate time-frequency points of the utterance that are most important for correct recognition of a target word. Our evaluation technique is not only suitable for standard classification tasks, but is also appropriate for structured prediction tasks like sequence-to-sequence models. Additionally, we use this approach to perform a large scale comparison of the importance maps created by our previously introduced technique using "bubble noise" to identify important points through correlation with a baseline approach based on smoothed speech energy and forced alignment. Our results show that the bubble analysis approach is better at identifying important speech regions than this baseline on 100 sentences from the AMI corpus.
Correlated Mixed Membership Modeling of Somatic Mutations
Authors: Rahul Mehta, Muge Karaman
Date: 2020-05-21 00:00:00
Tasks: Variational Inference

Recent studies of cancer somatic mutation profiles seek to identify mutations for targeted therapy in personalized medicine. Analysis of profiles, however, is not trivial, as each profile is heterogeneous and there are multiple confounding factors that influence the cause-and-effect relationships between cancer genes such as cancer (sub)type, biological processes, total number of mutations, and non-linear mutation interactions. Moreover, cancer is biologically redundant, i.e., distinct mutations can result in the alteration of similar biological processes, so it is important to identify all possible combinatorial sets of mutations for effective patient treatment. To model this phenomena, we propose the correlated zero-inflated negative binomial process to infer the inherent structure of somatic mutation profiles through latent representations. This stochastic process takes into account different, yet correlated, co-occurring mutations using profile-specific negative binomial dispersion parameters that are mixed with a correlated beta-Bernoulli process and a probability parameter to model profile heterogeneity. These model parameters are inferred by iterative optimization via amortized and stochastic variational inference using the Pan Cancer dataset from The Cancer Genomic Archive (TCGA). By examining the the latent space, we identify biologically relevant correlations between somatic mutations.
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
Authors: Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun
Date: 2020-05-21 00:00:00
Tasks: model

There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e.g., intensive care units). However, in many other practical situations, we can only access data with much fewer feature channels in a poor-data environments (e.g., at home), which often results in predictive models with poor performance. How can we boost the performance of models learned from such poor-data environment by leveraging knowledge extracted from existing models trained using rich data in a related environment? To address this question, we develop a knowledge infusion framework named CHEER that can succinctly summarize such rich model into transferable representations, which can be incorporated into the poor model to improve its performance. The infused model is analyzed theoretically and evaluated empirically on several datasets. Our empirical results showed that CHEER outperformed baselines by 5.60% to 46.80% in terms of the macro-F1 score on multiple physiological datasets.
Succinct Trit-array Trie for Scalable Trajectory Similarity Search
Authors: Shunsuke Kanda, Koh Takeuchi, Keisuke Fujii, Yasuo Tabei
Date: 2020-05-21 00:00:00

Massive datasets of spatial trajectories representing the mobility of a diversity of moving objects are ubiquitous in research and industry. Similarity search of a large collection of trajectories is indispensable for turning these datasets into knowledge. Locality sensitive hashing (LSH) is a powerful technique for fast similarity searches. Recent methods employ LSH and attempt to realize an efficient similarity search of trajectories; however, those methods are inefficient in terms of search time and memory when applied to massive datasets. To address this problem, we present the trajectory-indexing succinct trit-array trie (tSTAT), which is a scalable method leveraging LSH for trajectory similarity searches. tSTAT quickly performs the search on a tree data structure called trie. We also present two novel techniques that enable to dramatically enhance the memory efficiency of tSTAT. One is a node reduction technique that substantially omits redundant trie nodes while maintaining the time performance. The other is a space-efficient representation that leverages the idea behind succinct data structures (i.e., a compressed data structure supporting fast data operations). We experimentally test tSTAT on its ability to retrieve similar trajectories for a query from large collections of trajectories and show that tSTAT performs superiorly in comparison to state-of-the-art similarity search methods.
Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning
Authors: Sourya Dipta Das, Soumil Mandal
Date: 2020-05-21 00:00:00
Tasks: Emotion Classification, General Classification, Multi-Task Learning, Sentiment Analysis, Sentiment Classification

In this article, we describe the system that we used for the memotion analysis challenge, which is Task 8 of SemEval-2020. This challenge had three subtasks where affect based sentiment classification of the memes was required along with intensities. The system we proposed combines the three tasks into a single one by representing it as multi-label hierarchical classification problem.Here,Multi-Task learning or Joint learning Procedure is used to train our model.We have used dual channels to extract text and image based features from separate Deep Neural Network Backbone and aggregate them to create task specific features. These task specific aggregated feature vectors ware then passed on to smaller networks with dense layers, each one assigned for predicting one type of fine grain sentiment label. Our Proposed method show the superiority of this system in few tasks to other best models from the challenge.
Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality
Authors: Prudhvi Thirumalaraju, Manoj Kumar Kanakasabapathy, Charles L Bormann, Raghav Gupta, Rohan Pooniwala, Hemanth Kandula, Irene Souter, Irene Dimitriadis, Hadi Shafiee
Date: 2020-05-21 00:00:00

A critical factor that influences the success of an in-vitro fertilization (IVF) procedure is the quality of the transferred embryo. Embryo morphology assessments, conventionally performed through manual microscopic analysis suffer from disparities in practice, selection criteria, and subjectivity due to the experience of the embryologist. Convolutional neural networks (CNNs) are powerful, promising algorithms with significant potential for accurate classifications across many object categories. Network architectures and hyper-parameters affect the efficiency of CNNs for any given task. Here, we evaluate multi-layered CNNs developed from scratch and popular deep-learning architectures such as Inception v3, ResNET, Inception-ResNET-v2, and Xception in differentiating between embryos based on their morphological quality at 113 hours post insemination (hpi). Xception performed the best in differentiating between the embryos based on their morphological quality.
Joint Detection and Tracking in Videos with Identification Features
Authors: Bharti Munjal, Abdul Rafey Aftab, Sikandar Amin, Meltem D. Brandlmaier, Federico Tombari, Fabio Galasso
Date: 2020-05-21 00:00:00
Tasks: object-detection, Object Detection

Recent works have shown that combining object detection and tracking tasks, in the case of video data, results in higher performance for both tasks, but they require a high frame-rate as a strict requirement for performance. This is assumption is often violated in real-world applications, when models run on embedded devices, often at only a few frames per second. Videos at low frame-rate suffer from large object displacements. Here re-identification features may support to match large-displaced object detections, but current joint detection and re-identification formulations degrade the detector performance, as these two are contrasting tasks. In the real-world application having separate detector and re-id models is often not feasible, as both the memory and runtime effectively double. Towards robust long-term tracking applicable to reduced-computational-power devices, we propose the first joint optimization of detection, tracking and re-identification features for videos. Notably, our joint optimization maintains the detector performance, a typical multi-task challenge. At inference time, we leverage detections for tracking (tracking-by-detection) when the objects are visible, detectable and slowly moving in the image. We leverage instead re-identification features to match objects which disappeared (e.g. due to occlusion) for several frames or were not tracked due to fast motion (or low-frame-rate videos). Our proposed method reaches the state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge among online trackers, and 3rd overall.
Solving a steady-state PDE using spiking networks and neuromorphic hardware
Authors: J. Darby Smith, William Severa, Aaron J. Hill, Leah Reeder, Brian Franke, Richard B. Lehoucq, Ojas D. Parekh, James B. Aimone
Date: 2020-05-21 00:00:00
Tasks: BIG-bench Machine Learning, Position

The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine learning tasks, the space of appropriate applications is wide and continually expanding. Here, we leverage the parallel and event-driven structure to solve a steady state heat equation using a random walk method. The random walk can be executed fully within a spiking neural network using stochastic neuron behavior, and we provide results from both IBM TrueNorth and Intel Loihi implementations. Additionally, we position this algorithm as a potential scalable benchmark for neuromorphic systems.
SpotFast Networks with Memory Augmented Lateral Transformers for Lipreading
Authors: Peratham Wiriyathammabhum
Date: 2020-05-21 00:00:00
Tasks: Action Recognition, Lipreading

This paper presents a novel deep learning architecture for word-level lipreading. Previous works suggest a potential for incorporating a pretrained deep 3D Convolutional Neural Networks as a front-end feature extractor. We introduce a SpotFast networks, a variant of the state-of-the-art SlowFast networks for action recognition, which utilizes a temporal window as a spot pathway and all frames as a fast pathway. We further incorporate memory augmented lateral transformers to learn sequential features for classification. We evaluate the proposed model on the LRW dataset. The experiments show that our proposed model outperforms various state-of-the-art models and incorporating the memory augmented lateral transformers makes a 3.7% improvement to the SpotFast networks.
Global Optimization of Gaussian processes
Authors: Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, Alexander Mitsos
Date: 2020-05-21 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, global-optimization

Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems. These optimization problems are nonconvex and global optimization is desired. However, previous literature observed computational burdens limiting deterministic global optimization to Gaussian processes trained on few data points. We propose a reduced-space formulation for deterministic global optimization with trained Gaussian processes embedded. For optimization, the branch-and-bound solver branches only on the degrees of freedom and McCormick relaxations are propagated through explicit Gaussian process models. The approach also leads to significantly smaller and computationally cheaper subproblems for lower and upper bounding. To further accelerate convergence, we derive envelopes of common covariance functions for GPs and tight relaxations of acquisition functions used in Bayesian optimization including expected improvement, probability of improvement, and lower confidence bound. In total, we reduce computational time by orders of magnitude compared to state-of-the-art methods, thus overcoming previous computational burdens. We demonstrate the performance and scaling of the proposed method and apply it to Bayesian optimization with global optimization of the acquisition function and chance-constrained programming. The Gaussian process models, acquisition functions, and training scripts are available open-source within the "MeLOn - Machine Learning Models for Optimization" toolbox~(https://git.rwth-aachen.de/avt.svt/public/MeLOn).
Extracting Daily Dosage from Medication Instructions in EHRs: An Automated Approach and Lessons Learned
Authors: Diwakar Mahajan, Jennifer J. Liang, Ching-Huei Tsou
Date: 2020-05-21 00:00:00

Medication timelines have been shown to be effective in helping physicians visualize complex patient medication information. A key feature in many such designs is a longitudinal representation of a medication's daily dosage and its changes over time. However, daily dosage as a discrete value is generally not provided and needs to be derived from free text instructions (Sig). Existing works in daily dosage extraction are narrow in scope, targeting dosage extraction for a single drug from clinical notes. Here, we present an automated approach to calculate daily dosage for all medications, combining deep learning-based named entity extractor with lexicon dictionaries and regular expressions, achieving 0.98 precision and 0.95 recall on an expert-generated dataset of 1,000 Sigs. We also analyze our expert-generated dataset, discuss the challenges in understanding the complex information contained in Sigs, and provide insights to guide future work in the general-purpose daily dosage calculation task.
COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
Authors: Jim Samuel, G. G. Md. Nawaz Ali, Md. Mokhlesur Rahman, Ek Esawi, Yana Samuel
Date: 2020-05-21 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Descriptive, General Classification, Sentiment Analysis

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
Evaluating Neural Morphological Taggers for Sanskrit
Authors: Ashim Gupta, Amrith Krishna, Pawan Goyal, Oliver Hellwig
Date: 2020-05-21 00:00:00
Proceeding: WS 2020 7
Tasks: Morphological Tagging

Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training. We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism.