pwc-archive/papers-with-abstracts huggingface dataset:
Language Conditioned Imitation Learning over Unstructured Data
Authors: Corey Lynch, Pierre Sermanet
Date: 2020-05-15 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning, Instruction Following, Natural Language Understanding

Natural language is perhaps the most flexible and intuitive way for humans to communicate tasks to a robot. Prior work in imitation learning typically requires each task be specified with a task id or goal image -- something that is often impractical in open-world environments. On the other hand, previous approaches in instruction following allow agent behavior to be guided by language, but typically assume structure in the observations, actuators, or language that limit their applicability to complex settings like robotics. In this work, we present a method for incorporating free-form natural language conditioning into imitation learning. Our approach learns perception from pixels, natural language understanding, and multitask continuous control end-to-end as a single neural network. Unlike prior work in imitation learning, our method is able to incorporate unlabeled and unstructured demonstration data (i.e. no task or language labels). We show this dramatically improves language conditioned performance, while reducing the cost of language annotation to less than 1% of total data. At test time, a single language conditioned visuomotor policy trained with our method can perform a wide variety of robotic manipulation skills in a 3D environment, specified only with natural language descriptions of each task (e.g. "open the drawer...now pick up the block...now press the green button..."). To scale up the number of instructions an agent can follow, we propose combining text conditioned policies with large pretrained neural language models. We find this allows a policy to be robust to many out-of-distribution synonym instructions, without requiring new demonstrations. See videos of a human typing live text commands to our agent at language-play.github.io
Taskology: Utilizing Task Relations at Scale
Authors: Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon
Date: 2020-05-14 00:00:00
Proceeding: CVPR 2021 1
Tasks: Depth Estimation, Motion Estimation, Object Tracking, Scene Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Many computer vision tasks address the problem of scene understanding and are naturally interrelated e.g. object classification, detection, scene segmentation, depth estimation, etc. We show that we can leverage the inherent relationships among collections of tasks, as they are trained jointly, supervising each other through their known relationships via consistency losses. Furthermore, explicitly utilizing the relationships between tasks allows improving their performance while dramatically reducing the need for labeled data, and allows training with additional unsupervised or simulated data. We demonstrate a distributed joint training algorithm with task-level parallelism, which affords a high degree of asynchronicity and robustness. This allows learning across multiple tasks, or with large amounts of input data, at scale. We demonstrate our framework on subsets of the following collection of tasks: depth and normal prediction, semantic segmentation, 3D motion and ego-motion estimation, and object tracking and 3D detection in point clouds. We observe improved performance across these tasks, especially in the low-label regime.
On Bottleneck Features for Text-Dependent Speaker Verification Using X-vectors
Authors: Achintya Kumar Sarkar, Zheng-Hua Tan
Date: 2020-05-15 00:00:00
Tasks: Contrastive Learning, Speaker Verification, Text-Dependent Speaker Verification, Text-Independent Speaker Verification, Weakly-supervised Learning

Applying x-vectors for speaker verification has recently attracted great interest, with the focus being on text-independent speaker verification. In this paper, we study x-vectors for text-dependent speaker verification (TD-SV), which remains unexplored. We further investigate the impact of the different bottleneck (BN) features on the performance of x-vectors, including the recently-introduced time-contrastive-learning (TCL) BN features and phone-discriminant BN features. TCL is a weakly supervised learning approach that constructs training data by uniformly partitioning each utterance into a predefined number of segments and then assigning each segment a class label depending on their position in the utterance. We also compare TD-SV performance for different modeling techniques, including the Gaussian mixture models-universal background model (GMM-UBM), i-vector, and x-vector. Experiments are conducted on the RedDots 2016 challenge database. It is found that the type of features has a marginal impact on the performance of x-vectors with the TCL BN feature achieving the lowest equal error rate, while the impact of features is significant for i-vector and GMM-UBM. The fusion of x-vector and i-vector systems gives a large gain in performance. The GMM-UBM technique shows its advantage for TD-SV using short utterances.
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
Authors: Thomas M. Moerland, Anna Deichler, Simone Baldi, Joost Broekens, Catholijn M. Jonker
Date: 2020-05-15 00:00:00
Tasks: Decision Making, Reinforcement Learning (RL), Sequential Decision Making

Planning and reinforcement learning are two key approaches to sequential decision making. Multi-step approximate real-time dynamic programming, a recently successful algorithm class of which AlphaZero [Silver et al., 2018] is an example, combines both by nesting planning within a learning loop. However, the combination of planning and learning introduces a new question: how should we balance time spend on planning, learning and acting? The importance of this trade-off has not been explicitly studied before. We show that it is actually of key importance, with computational results indicating that we should neither plan too long nor too short. Conceptually, we identify a new spectrum of planning-learning algorithms which ranges from exhaustive search (long planning) to model-free RL (no planning), with optimal performance achieved midway.
MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling
Authors: Kelei He, Chunfeng Lian, Ehsan Adeli, Jing Huo, Yang Gao, Bing Zhang, Junfeng Zhang, Dinggang Shen
Date: 2020-05-15 00:00:00
Tasks: Metric Learning, Multi-Task Learning, Segmentation, Semantic Segmentation

Fully convolutional networks (FCNs), including UNet and VNet, are widely-used network architectures for semantic segmentation in recent studies. However, conventional FCN is typically trained by the cross-entropy or Dice loss, which only calculates the error between predictions and ground-truth labels for pixels individually. This often results in non-smooth neighborhoods in the predicted segmentation. To address this problem, we propose a two-stage framework, with the first stage to quickly localize the prostate region and the second stage to precisely segment the prostate by a multi-task UNet architecture. We introduce a novel online metric learning module through voxel-wise sampling in the multi-task network. Therefore, the proposed network has a dual-branch architecture that tackles two tasks: 1) a segmentation sub-network aiming to generate the prostate segmentation, and 2) a voxel-metric learning sub-network aiming to improve the quality of the learned feature space supervised by a metric loss. Specifically, the voxel-metric learning sub-network samples tuples (including triplets and pairs) in voxel-level through the intermediate feature maps. Unlike conventional deep metric learning methods that generate triplets or pairs in image-level before the training phase, our proposed voxel-wise tuples are sampled in an online manner and operated in an end-to-end fashion via multi-task learning. To evaluate the proposed method, we implement extensive experiments on a real CT image dataset consisting of 339 patients. The ablation studies show that our method can effectively learn more representative voxel-level features compared with the conventional learning methods with cross-entropy or Dice loss. And the comparisons show that the proposed method outperforms the state-of-the-art methods by a reasonable margin.
Temperate Fish Detection and Classification: a Deep Learning based Approach
Authors: Kristian Muri Knausgård, Arne Wiklund, Tonje Knutsen Sørdalen, Kim Halvorsen, Alf Ring Kleiven, Lei Jiao, Morten Goodwin
Date: 2020-05-14 00:00:00
Tasks: Classification, Deep Learning, Fish Detection, General Classification, Image Augmentation, object-detection, Object Detection, Transfer Learning

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27\% on the pre-training. The percentage values for accuracy on the post-training are good; 83.68\% and 87.74\% with and without image augmentation, respectively, indicating that the solution is viable with a more extensive dataset.
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors
Authors: Dongqi Han, Zhiliang Wang, Ying Zhong, Wenqi Chen, Jiahai Yang, Shuqiang Lu, Xingang Shi, Xia Yin
Date: 2020-05-15 00:00:00
Tasks: Adversarial Robustness, BIG-bench Machine Learning, Intrusion Detection, Network Intrusion Detection

Machine learning (ML), especially deep learning (DL) techniques have been increasingly used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has shown to be extremely vulnerable to adversarial attacks, especially in such security-sensitive systems. Many adversarial attacks have been proposed to evaluate the robustness of ML-based NIDSs. Unfortunately, existing attacks mostly focused on feature-space and/or white-box attacks, which make impractical assumptions in real-world scenarios, leaving the study on practical gray/black-box attacks largely unexplored. To bridge this gap, we conduct the first systematic study of the gray/black-box traffic-space adversarial attacks to evaluate the robustness of ML-based NIDSs. Our work outperforms previous ones in the following aspects: (i) practical-the proposed attack can automatically mutate original traffic with extremely limited knowledge and affordable overhead while preserving its functionality; (ii) generic-the proposed attack is effective for evaluating the robustness of various NIDSs using diverse ML/DL models and non-payload-based features; (iii) explainable-we propose an explanation method for the fragile robustness of ML-based NIDSs. Based on this, we also propose a defense scheme against adversarial attacks to improve system robustness. We extensively evaluate the robustness of various NIDSs using diverse feature sets and ML/DL models. Experimental results show our attack is effective (e.g., >97% evasion rate in half cases for Kitsune, a state-of-the-art NIDS) with affordable execution cost and the proposed defense method can effectively mitigate such attacks (evasion rate is reduced by >50% in most cases).
Siamese Neural Networks for Class Activity Detection
Authors: Hang Li, Zhiwei Wang, Jiliang Tang, Wenbiao Ding, Zitao Liu
Date: 2020-05-15 00:00:00
Tasks: Action Detection, Activity Detection

Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms. A CAD solution helps teachers get instant feedback on their pedagogical instructions. However, CAD is very challenging because (1) classroom conversations contain many conversational turn-taking overlaps between teachers and students; (2) the CAD model needs to be generalized well enough for different teachers and students; and (3) classroom recordings may be very noisy and low-quality. In this work, we address the above challenges by building a Siamese neural framework to automatically identify teacher and student utterances from classroom recordings. The proposed model is evaluated on real-world educational datasets. The results demonstrate that (1) our approach is superior on the prediction tasks for both online and offline classroom environments; and (2) our framework exhibits robustness and generalization ability on new teachers (i.e., teachers never appear in training data).
Accelerating drug repurposing for COVID-19 via modeling drug mechanism of action with large scale gene-expression profiles
Authors: Lu Han, G. C. Shan, B. F. Chu, H. Y. Wang, Z. J. Wang, S. Q. Gao, W. X. Zhou
Date: 2020-05-15 00:00:00

The novel coronavirus disease, named COVID-19, emerged in China in December 2019, and has rapidly spread around the world. It is clearly urgent to fight COVID-19 at global scale. The development of methods for identifying drug uses based on phenotypic data can improve the efficiency of drug development. However, there are still many difficulties in identifying drug applications based on cell picture data. This work reported one state-of-the-art machine learning method to identify drug uses based on the cell image features of 1024 drugs generated in the LINCS program. Because the multi-dimensional features of the image are affected by non-experimental factors, the characteristics of similar drugs vary greatly, and the current sample number is not enough to use deep learning and other methods are used for learning optimization. As a consequence, this study is based on the supervised ITML algorithm to convert the characteristics of drugs. The results show that the characteristics of ITML conversion are more conducive to the recognition of drug functions. The analysis of feature conversion shows that different features play important roles in identifying different drug functions. For the current COVID-19, Chloroquine and Hydroxychloroquine achieve antiviral effects by inhibiting endocytosis, etc., and were classified to the same community. And Clomiphene in the same community inibited the entry of Ebola Virus, indicated a similar MoAs that could be reflected by cell image.
Context-Dependent Acoustic Modeling without Explicit Phone Clustering
Authors: Tina Raissi, Eugen Beck, Ralf Schlüter, Hermann Ney
Date: 2020-05-15 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Clustering, speech-recognition, Speech Recognition

Phoneme-based acoustic modeling of large vocabulary automatic speech recognition takes advantage of phoneme context. The large number of context-dependent (CD) phonemes and their highly varying statistics require tying or smoothing to enable robust training. Usually, classification and regression trees are used for phonetic clustering, which is standard in hidden Markov model (HMM)-based systems. However, this solution introduces a secondary training objective and does not allow for end-to-end training. In this work, we address a direct phonetic context modeling for the hybrid deep neural network (DNN)/HMM, that does not build on any phone clustering algorithm for the determination of the HMM state inventory. By performing different decompositions of the joint probability of the center phoneme state and its left and right contexts, we obtain a factorized network consisting of different components, trained jointly. Moreover, the representation of the phonetic context for the network relies on phoneme embeddings. The recognition accuracy of our proposed models on the Switchboard task is comparable and outperforms slightly the hybrid model using the standard state-tying decision trees.
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Authors: Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G. Aberle, Shijing Sun, Xiaonan Wang, Yi Liu, Qianxiao Li, Senthilnath Jayavelu, Kedar Hippalgaonkar, Yousung Jung, Tonio Buonassisi
Date: 2020-05-15 00:00:00

Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties. However, state-of-the-art generative models tend to be limited to a specific composition or crystal structure. Herein, we present a framework capable of general inverse design (not limited to a given set of elements or crystal structures), featuring a generalized invertible representation that encodes crystals in both real and reciprocal space, and a property-structured latent space from a variational autoencoder (VAE). In three design cases, the framework generates 142 new crystals with user-defined formation energies, bandgap, thermoelectric (TE) power factor, and combinations thereof. These generated crystals, absent in the training database, are validated by first-principles calculations. The success rates (number of first-principles-validated target-satisfying crystals/number of designed crystals) ranges between 7.1% and 38.9%. These results represent a significant step toward property-driven general inverse design using generative models, although practical challenges remain when coupled with experimental synthesis.
An Auto Encoder For Audio Dolphin Communication
Authors: Daniel Kohlsdorf, Denise Herzing, Thad Starner
Date: 2020-05-15 00:00:00
Tasks: Clustering

Research in dolphin communication and cognition requires detailed inspection of audible dolphin signals. The manual analysis of these signals is cumbersome and time-consuming. We seek to automate parts of the analysis using modern deep learning methods. We propose to learn an autoencoder constructed from convolutional and recurrent layers trained in an unsupervised fashion. The resulting model embeds patterns in audible dolphin communication. In several experiments, we show that the embeddings can be used for clustering as well as signal detection and signal type classification.
Convolutional Neural Network for emotion recognition to assist psychiatrists and psychologists during the COVID-19 pandemic: experts opinion
Authors: Hugo Mitre-Hernandez, Rodolfo Ferro-Perez, Francisco Gonzalez-Hernandez
Date: 2020-05-15 00:00:00
Tasks: Computational Efficiency, Emotion Recognition, Facial Expression Recognition (FER), RTE

A web application with real-time emotion recognition for psychologists and psychiatrists is presented. Mental health effects during COVID-19 quarantine need to be handled because society is being emotionally impacted. The human micro-expressions can describe genuine emotions that can be captured by Convolutional Neural Networks (CNN) models. But the challenge is to implement it under the poor performance of a part of society computers and the low speed of internet connection, i.e., improve the computational efficiency and reduce the data transfer. To validate the computational efficiency premise, we compare CNN architectures results, collecting the floating-point operations per second (FLOPS), the Number of Parameters (NP) and accuracy from the MobileNet, PeleeNet, Extended Deep Neural Network (EDNN), Inception- Based Deep Neural Network (IDNN) and our proposed Residual mobile-based Network model (ResmoNet). Also, we compare the trained models results in terms of Main Memory Utilization (MMU) and Response Time to complete the Emotion (RTE) recognition. Besides, we design a data transfer that includes the raw data of emotions and the basic patient information. The web application was evaluated with the System Usability Scale (SUS) and a utility questionnaire by psychologists and psychiatrists. ResmoNet model generated the most reduced NP, FLOPS, and MMU results, only EDNN overcomes ResmoNet in 0.01sec in RTE. The optimizations to our model impacted the accuracy, therefore IDNN and EDNN are 0.02 and 0.05 more accurate than our model respectively. Finally, according to psychologists and psychiatrists, the web application has good usability (73.8 of 100) and utility (3.94 of 5).
DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling
Authors: Tegg Taekyong Sung, Jeongsoo Ha, Jeewoo Kim, Alex Yahja, Chae-Bong Sohn, Bo Ryu
Date: 2020-05-15 00:00:00
Tasks: Deep Reinforcement Learning, GPU, Graph Embedding, Scheduling

In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph. Traditionally, heuristic algorithms have been widely used for many resource scheduling domains, and Heterogeneous Earliest Finish Time (HEFT) has been a dominating state-of-the-art technique across a broad range of heterogeneous resource scheduling domains over many years. Despite their long-standing popularity, HEFT-like algorithms are known to be vulnerable to a small amount of noise added to the environment. Our Deep Reinforcement Learning (DRL)-based SoC Scheduler (DeepSoCS), capable of learning the "best" task ordering under dynamic environment changes, overcomes the brittleness of rule-based schedulers such as HEFT with significantly higher performance across different types of jobs. We~describe a DeepSoCS design process using a real-time heterogeneous SoC scheduling emulator, discuss major challenges, and present two novel neural network design features that lead to outperforming HEFT: (i) hierarchical job- and task-graph embedding; and (ii) efficient use of real-time task information in the state space. Furthermore, we~introduce effective techniques to address two fundamental challenges present in our environment: delayed consequences and joint actions. Through an extensive simulation study, we~show that our DeepSoCS exhibits the significantly higher performance of job execution time than that of HEFT with a higher level of robustness under realistic noise conditions. We~conclude with a discussion of the potential improvements for our DeepSoCS neural scheduler.