February 1, 2020

2959 words 14 mins read

Paper Group AWR 165

Deep Reinforcement Learning with Feedback-based Exploration. iCassava 2019 Fine-Grained Visual Categorization Challenge. CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data. Topic Modeling via Full Dependence Mixtures. Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variabl …

Deep Reinforcement Learning with Feedback-based Exploration


Title	Deep Reinforcement Learning with Feedback-based Exploration
Authors	Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober
Abstract	Deep Reinforcement Learning has enabled the control of increasingly complex and high-dimensional problems. However, the need of vast amounts of data before reasonable performance is attained prevents its widespread application. We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning. The uncertainty in the policy and the corrective feedback is combined directly in the action space as probabilistic conditional exploration. As a result, the greatest part of the otherwise ignorant learning process can be avoided. We demonstrate the proposed method, Predictive Probabilistic Merging of Policies (PPMP), in combination with DDPG. In experiments on continuous control problems of the OpenAI Gym, we achieve drastic improvements in sample efficiency, final performance, and robustness to erroneous feedback, both for human and synthetic feedback. Additionally, we show solutions beyond the demonstrated knowledge.
Tasks	Continuous Control
Published	2019-03-14
URL	http://arxiv.org/abs/1903.06151v1
PDF	http://arxiv.org/pdf/1903.06151v1.pdf
PWC	https://paperswithcode.com/paper/deep-reinforcement-learning-with-feedback
Repo	https://github.com/pemami4911/deep-rl
Framework	tf

iCassava 2019 Fine-Grained Visual Categorization Challenge


Title	iCassava 2019 Fine-Grained Visual Categorization Challenge
Authors	Ernest Mwebaze, Timnit Gebru, Andrea Frome, Solomon Nsumba, Jeremy Tusubira
Abstract	Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of carbohydrates in Africa.At least 80% of small-holder farmer households in Sub-Saharan Africa grow cassava. Since many of these farmers have smart phones, they can easily obtain photos of dis-eased and healthy cassava leaves in their farms, allowing the opportunity to use computer vision techniques to monitor the disease type and severity and increase yields. How-ever, annotating these images is extremely difficult as ex-perts who are able to distinguish between highly similar dis-eases need to be employed. We provide a dataset of labeled and unlabeled cassava leaves and formulate a Kaggle challenge to encourage participants to improve the performance of their algorithms using semi-supervised approaches. This paper describes our dataset and challenge which is part of the Fine-Grained Visual Categorization workshop at CVPR2019.
Tasks	Fine-Grained Visual Categorization
Published	2019-08-08
URL	https://arxiv.org/abs/1908.02900v2
PDF	https://arxiv.org/pdf/1908.02900v2.pdf
PWC	https://paperswithcode.com/paper/icassava-2019fine-grained-visual
Repo	https://github.com/mjvakili/cassava_disease_classification
Framework	none

CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data


Title	CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data
Authors	Markus Hanselmann, Thilo Strauss, Katharina Dormann, Holger Ulmer
Abstract	We propose a novel neural network architecture for detecting intrusions on the CAN bus. The Controller Area Network (CAN) is the standard communication method between the Electronic Control Units (ECUs) of automobiles. However, CAN lacks security mechanisms and it has recently been shown that it can be attacked remotely. Hence, it is desirable to monitor CAN traffic to detect intrusions. In order to detect both, known and unknown intrusion scenarios, we consider a novel unsupervised learning approach which we call CANet. To our knowledge, this is the first deep learning based intrusion detection system (IDS) that takes individual CAN messages with different IDs and evaluates them in the moment they are received. This is a significant advancement because messages with different IDs are typically sent at different times and with different frequencies. Our method is evaluated on real and synthetic CAN data. For reproducibility of the method, our synthetic data is publicly available. A comparison with previous machine learning based methods shows that CANet outperforms them by a significant margin.
Tasks	Intrusion Detection
Published	2019-06-06
URL	https://arxiv.org/abs/1906.02492v1
PDF	https://arxiv.org/pdf/1906.02492v1.pdf
PWC	https://paperswithcode.com/paper/canet-an-unsupervised-intrusion-detection
Repo	https://github.com/etas/SynCAN
Framework	none

Topic Modeling via Full Dependence Mixtures


Title	Topic Modeling via Full Dependence Mixtures
Authors	Dan Fisher, Mark Kozdoba, Shie Mannor
Abstract	In this paper we introduce a new approach to topic modelling that scales to large datasets by using a compact representation of the data and by leveraging the GPU architecture. In this approach, topics are learned directly from the co-occurrence data of the corpus. In particular, we introduce a novel mixture model which we term the Full Dependence Mixture (FDM) model. FDMs model second moment under general generative assumptions on the data. While there is previous work on topic modeling using second moments, we develop a direct stochastic optimization procedure for fitting an FDM with a single Kullback Leibler objective. Moment methods in general have the benefit that an iteration no longer needs to scale with the size of the corpus. Our approach allows us to leverage standard optimizers and GPUs for the problem of topic modeling. In particular, we evaluate the approach on two large datasets, NeurIPS papers and a Twitter corpus, with a large number of topics, and show that the approach performs comparably or better than the the standard benchmarks.
Tasks	Stochastic Optimization
Published	2019-06-13
URL	https://arxiv.org/abs/1906.06181v3
PDF	https://arxiv.org/pdf/1906.06181v3.pdf
PWC	https://paperswithcode.com/paper/topic-modeling-via-full-dependence-mixtures
Repo	https://github.com/fisherd3/fdm
Framework	tf

Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models


Title	Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
Authors	Tiancheng Zhao, Kaige Xie, Maxine Eskenazi
Abstract	Defining action spaces for conversational agents and optimizing their decision-making process with reinforcement learning is an enduring challenge. Common practice has been to use handcrafted dialog acts, or the output vocabulary, e.g. in neural encoder decoders, as the action spaces. Both have their own limitations. This paper proposes a novel latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods in order to induce its own action space from the data. Comprehensive experiments are conducted examining both continuous and discrete action types and two different optimization methods based on stochastic variational inference. Results show that the proposed latent actions achieve superior empirical performance improvement over previous word-level policy gradient methods on both DealOrNoDeal and MultiWoz dialogs. Our detailed analysis also provides insights about various latent variable approaches for policy learning and can serve as a foundation for developing better latent actions in future research.
Tasks	Decision Making, Dialogue Generation, Dialogue Management, Goal-Oriented Dialogue Systems, Latent Variable Models, Policy Gradient Methods
Published	2019-02-23
URL	http://arxiv.org/abs/1902.08858v2
PDF	http://arxiv.org/pdf/1902.08858v2.pdf
PWC	https://paperswithcode.com/paper/rethinking-action-spaces-for-reinforcement
Repo	https://github.com/snakeztc/NeuralDialog-LaRL
Framework	pytorch

Modeling Multi-Action Policy for Task-Oriented Dialogues


Title	Modeling Multi-Action Policy for Task-Oriented Dialogues
Authors	Lei Shu, Hu Xu, Bing Liu, Piero Molino
Abstract	Dialogue management (DM) plays a key role in the quality of the interaction with the user in a task-oriented dialogue system. In most existing approaches, the agent predicts only one DM policy action per turn. This significantly limits the expressive power of the conversational agent and introduces unwanted turns of interactions that may challenge users’ patience. Longer conversations also lead to more errors and the system needs to be more robust to handle them. In this paper, we compare the performance of several models on the task of predicting multiple acts for each turn. A novel policy model is proposed based on a recurrent cell called gated Continue-Act-Slots (gCAS) that overcomes the limitations of the existing models. Experimental results show that gCAS outperforms other approaches. The code is available at https://leishu02.github.io/
Tasks	Dialogue Management
Published	2019-08-30
URL	https://arxiv.org/abs/1908.11546v1
PDF	https://arxiv.org/pdf/1908.11546v1.pdf
PWC	https://paperswithcode.com/paper/modeling-multi-action-policy-for-task
Repo	https://github.com/leishu02/EMNLP2019_gCAS
Framework	pytorch

RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools


Title	RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
Authors	Cristina Cornelio, Veronika Thost
Abstract	Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form. However, rule formulation is a complex process that requires deep domain expertise,and is further challenged by today’s often large, heterogeneous, and incomplete knowledge graphs. Several approaches for learning rules automatically, given a set of input example facts,have been proposed over time, including, more recently, neural systems. Yet, the area is missing adequate datasets and evaluation approaches: existing datasets often resemble toy examples that neither cover the various kinds of dependencies between rules nor allow for testing scalability. We present a tool for generating different kinds of datasets and for evaluating rule learning systems, including new performance measures.
Tasks	Knowledge Graphs
Published	2019-09-16
URL	https://arxiv.org/abs/1909.07095v2
PDF	https://arxiv.org/pdf/1909.07095v2.pdf
PWC	https://paperswithcode.com/paper/rudas-synthetic-datasets-for-rule-learning
Repo	https://github.com/IBM/RuDaS
Framework	none

Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition


Title	Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition
Authors	Julian Salazar, Katrin Kirchhoff, Zhiheng Huang
Abstract	The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully self-attentional network for CTC, and show it is tractable and competitive for end-to-end speech recognition. SAN-CTC trains quickly and outperforms existing CTC models and most encoder-decoder models, with character error rates (CERs) of 4.7% in 1 day on WSJ eval92 and 2.8% in 1 week on LibriSpeech test-clean, with a fixed architecture and one GPU. Similar improvements hold for WERs after LM decoding. We motivate the architecture for speech, evaluate position and downsampling approaches, and explore how label alphabets (character, phoneme, subword) affect attention heads and performance.
Tasks	End-To-End Speech Recognition, Speech Recognition
Published	2019-01-22
URL	http://arxiv.org/abs/1901.10055v2
PDF	http://arxiv.org/pdf/1901.10055v2.pdf
PWC	https://paperswithcode.com/paper/self-attention-networks-for-connectionist
Repo	https://github.com/aaaceo890/Attention
Framework	pytorch

Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity


Title	Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Authors	Yu Shi, Jiaming Shen, Yuchen Li, Naijing Zhang, Xinwei He, Zhengzhi Lou, Qi Zhu, Matthew Walker, Myunghwan Kim, Jiawei Han
Abstract	Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the core of many knowledge graphs and benefits many downstream applications. Existing methods of hypernymy discovery either leverage textual patterns to extract explicitly mentioned hypernym-hyponym pairs, or learn a distributional representation for each term of interest based its context. These approaches rely on statistical signals from the textual corpus, and their effectiveness would therefore be hindered when the signals from the corpus are not sufficient for all terms of interest. In this work, we propose to discover hypernymy in text-rich HINs, which can introduce additional high-quality signals. We develop a new framework, named HyperMine, that exploits multi-granular contexts and combines signals from both text and network without human labeled data. HyperMine extends the definition of context to the scenario of text-rich HIN. For example, we can define typed nodes and communities as contexts. These contexts encode signals of different granularities and we feed them into a hypernymy inference model. HyperMine learns this model using weak supervision acquired based on high-precision textual patterns. Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity. We further show a case study that a high-quality taxonomy can be generated solely based on the hypernymy discovered by HyperMine.
Tasks	Knowledge Graphs
Published	2019-09-04
URL	https://arxiv.org/abs/1909.01584v1
PDF	https://arxiv.org/pdf/1909.01584v1.pdf
PWC	https://paperswithcode.com/paper/discovering-hypernymy-in-text-rich
Repo	https://github.com/ysyushi/HyperMine
Framework	pytorch

Very Deep Self-Attention Networks for End-to-End Speech Recognition


Title	Very Deep Self-Attention Networks for End-to-End Speech Recognition
Authors	Ngoc-Quan Pham, Thai-Son Nguyen, Jan Niehues, Markus Müller, Sebastian Stüker, Alexander Waibel
Abstract	Recently, end-to-end sequence-to-sequence models for speech recognition have gained significant interest in the research community. While previous architecture choices revolve around time-delay neural networks (TDNN) and long short-term memory (LSTM) recurrent neural networks, we propose to use self-attention via the Transformer architecture as an alternative. Our analysis shows that deep Transformer networks with high learning capacity are able to exceed performance from previous end-to-end approaches and even match the conventional hybrid systems. Moreover, we trained very deep models with up to 48 Transformer layers for both encoder and decoders combined with stochastic residual connections, which greatly improve generalizability and training efficiency. The resulting models outperform all previous end-to-end ASR approaches on the Switchboard benchmark. An ensemble of these models achieve 9.9% and 17.7% WER on Switchboard and CallHome test sets respectively. This finding brings our end-to-end models to competitive levels with previous hybrid systems. Further, with model ensembling the Transformers can outperform certain hybrid systems, which are more complicated in terms of both structure and training procedure.
Tasks	End-To-End Speech Recognition, Speech Recognition
Published	2019-04-30
URL	https://arxiv.org/abs/1904.13377v2
PDF	https://arxiv.org/pdf/1904.13377v2.pdf
PWC	https://paperswithcode.com/paper/very-deep-self-attention-networks-for-end-to
Repo	https://github.com/quanpn90/NMTGMinor
Framework	pytorch

Learning to Evolve


Title	Learning to Evolve
Authors	Jan Schuchardt, Vladimir Golkov, Daniel Cremers
Abstract	Evolution and learning are two of the fundamental mechanisms by which life adapts in order to survive and to transcend limitations. These biological phenomena inspired successful computational methods such as evolutionary algorithms and deep learning. Evolution relies on random mutations and on random genetic recombination. Here we show that learning to evolve, i.e. learning to mutate and recombine better than at random, improves the result of evolution in terms of fitness increase per generation and even in terms of attainable fitness. We use deep reinforcement learning to learn to dynamically adjust the strategy of evolutionary algorithms to varying circumstances. Our methods outperform classical evolutionary algorithms on combinatorial and continuous optimization problems.
Tasks
Published	2019-05-08
URL	https://arxiv.org/abs/1905.03389v1
PDF	https://arxiv.org/pdf/1905.03389v1.pdf
PWC	https://paperswithcode.com/paper/190503389
Repo	https://github.com/jan-schuchardt/learning-to-evolve
Framework	pytorch

Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile & Pervasive Computing


Title	Dynamic Service Composition Orchestrated by Cognitive Agents in Mobile & Pervasive Computing
Authors	Oscar J. Romero
Abstract	Automatic service composition in mobile and pervasive computing faces many challenges due to the complex nature of the environment. Common approaches address service composition from optimization perspectives which are not feasible in practice due to the intractability of the problem, limited computational resources of smart devices, service host’s mobility, and time constraints. Our main contribution is the development of a cognitively-inspired agent-based service composition model focused on bounded rationality rather than optimality, which allows the system to compensate for limited resources by selectively filtering out continuous streams of data. The evaluation of our approach shows promising results when compared against state-of-the-art service composition models.
Tasks
Published	2019-05-31
URL	https://arxiv.org/abs/1906.00772v1
PDF	https://arxiv.org/pdf/1906.00772v1.pdf
PWC	https://paperswithcode.com/paper/190600772
Repo	https://github.com/ojrlopez27/copernic
Framework	none

Automatic alignment of surgical videos using kinematic data


Title	Automatic alignment of surgical videos using kinematic data
Authors	Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, François Petitjean, Lhassane Idoumghar, Pierre-Alain Muller
Abstract	Over the past one hundred years, the classic teaching methodology of “see one, do one, teach one” has governed the surgical education systems worldwide. With the advent of Operation Room 2.0, recording video, kinematic and many other types of data during the surgery became an easy task, thus allowing artificial intelligence systems to be deployed and used in surgical and medical practice. Recently, surgical videos has been shown to provide a structure for peer coaching enabling novice trainees to learn from experienced surgeons by replaying those videos. However, the high inter-operator variability in surgical gesture duration and execution renders learning from comparing novice to expert surgical videos a very difficult task. In this paper, we propose a novel technique to align multiple videos based on the alignment of their corresponding kinematic multivariate time series data. By leveraging the Dynamic Time Warping measure, our algorithm synchronizes a set of videos in order to show the same gesture being performed at different speed. We believe that the proposed approach is a valuable addition to the existing learning tools for surgery.
Tasks	Time Series
Published	2019-04-03
URL	http://arxiv.org/abs/1904.07302v2
PDF	http://arxiv.org/pdf/1904.07302v2.pdf
PWC	https://paperswithcode.com/paper/190407302
Repo	https://github.com/hfawaz/aime19
Framework	tf

End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model


Title	End-To-End Speech Recognition Using A High Rank LSTM-CTC Based Model
Authors	Yangyang Shi, Mei-Yuh Hwang, Xin Lei
Abstract	Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based end-to-end models are widely used in speech recognition due to its simplicity in training and efficiency in decoding. In conventional LSTM-CTC based models, a bottleneck projection matrix maps the hidden feature vectors obtained from LSTM to softmax output layer. In this paper, we propose to use a high rank projection layer to replace the projection matrix. The output from the high rank projection layer is a weighted combination of vectors that are projected from the hidden feature vectors via different projection matrices and non-linear activation function. The high rank projection layer is able to improve the expressiveness of LSTM-CTC models. The experimental results show that on Wall Street Journal (WSJ) corpus and LibriSpeech data set, the proposed method achieves 4%-6% relative word error rate (WER) reduction over the baseline CTC system. They outperform other published CTC based end-to-end (E2E) models under the condition that no external data or data augmentation is applied. Code has been made available at https://github.com/mobvoi/lstm_ctc.
Tasks	Data Augmentation, End-To-End Speech Recognition, Speech Recognition
Published	2019-03-12
URL	http://arxiv.org/abs/1903.05261v1
PDF	http://arxiv.org/pdf/1903.05261v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-speech-recognition-using-a-high
Repo	https://github.com/mobvoi/lstm_ctc
Framework	tf

ELI5: Long Form Question Answering


Title	ELI5: Long Form Question Answering
Authors	Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, Michael Auli
Abstract	We introduce the first large-scale corpus for long-form question answering, a task requiring elaborate and in-depth answers to open-ended questions. The dataset comprises 270K threads from the Reddit forum ``Explain Like I’m Five’’ (ELI5) where an online community provides answers to questions which are comprehensible by five year olds. Compared to existing datasets, ELI5 comprises diverse questions requiring multi-sentence answers. We provide a large set of web documents to help answer the question. Automatic and human evaluations show that an abstractive model trained with a multi-task objective outperforms conventional Seq2Seq, language modeling, as well as a strong extractive baseline. However, our best model is still far from human performance since raters prefer gold responses in over 86% of cases, leaving ample opportunity for future improvement. \|
Tasks	Language Modelling, Question Answering
Published	2019-07-22
URL	https://arxiv.org/abs/1907.09190v1
PDF	https://arxiv.org/pdf/1907.09190v1.pdf
PWC	https://paperswithcode.com/paper/eli5-long-form-question-answering
Repo	https://github.com/facebookresearch/ELI5
Framework	pytorch