Learning Low-precision Neural Networks without Straight-Through Estimator STE. Parametric Manifold Learning of Gaussian Mixture Models. Multi-Objective Generalized Linear Bandits. Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning. E²GAN: End-to-End Generative Adversarial Network for Multivariate Time Series Imputation. Weakly Supervised Multi-Label Learning via Label Enhancement.

AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition. Monte Carlo Tree Search for Policy Optimization. Coarse-to-Fine Image Inpainting via Region-wise Convolutions and Non-Local Correlation. On Principled Entropy Exploration in Policy Optimization. Anytime Bottom-Up Rule Learning for Knowledge Graph Completion.

Christian Meilicke, Melisachew Wudage Chekol, Daniel Ruffinelli, Heiner Stuckenschmidt. Unsupervised Hierarchical Temporal Abstraction by Simultaneously Learning Expectations and Representations. Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems.

Robust Flexible Feature Selection via Exclusive L21 Regularization. Advantage Amplification in Slowly Evolving Latent-State Environments. Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control. DyAt Nets: Dynamic Attention Networks for State Forecasting in Cyber-Physical Systems. Outlier-Robust Multi-Aspect Streaming Tensor Completion and Factorization. Incremental Learning of Planning Actions in Model-Based Reinforcement Learning.

Group LASSO with Asymmetric Structure Estimation for Multi-Task Learning. Hill Climbing on Value Estimates for Search-control in Dyna. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks. Improving Cross-lingual Entity Alignment via Optimal Transport. Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks.

Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxin Ning, Kunfeng Lai, Philip S. A Practical Semi-Parametric Contextual Bandit. Yi Peng, Miao Xie, Jiahao Liu, Xuying Meng, Nan Li, Cheng Yang, Tao Yao, Rong Jin. An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents. Felipe Petroski Such, Vashisht Madhavan, Rosanne Liu, Rui Wang, Pablo Samuel Castro, Yulun Li, Jiale Zhi, Ludwig Schubert, Marc G.

Bellemare, Jeff Clune, Joel Lehman. Improving representation learning in autoencoders via multidimensional interpolation and dual regularizations. Scalable Bayesian Non-linear Matrix Completion. Noise-Resilient Similarity Preserving Network Embedding for Social Networks. Automated Machine Learning with Monte-Carlo Tree Search. Successor Options: An Option Discovery Framework for Reinforcement Learning.

Unifying the Stochastic and the Adversarial Bandits with Knapsack. Label distribution learning with label-specific features. Label Distribution Learning with Label Correlations via Low-Rank Approximation. Closed-Loop Memory GAN for Continual Learning. Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay.

Discovering Regularities from Traditional Chinese Medicine Prescriptions via Bipartite Embedding Model. A Degeneracy Framework for Scalable Graph Autoencoders. Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents.

SynthNet: Learning to Synthesize Music End-to-End. Weakly Supervised Multi-task Learning for Semantic Parsing. Community Detection and Link Prediction via Cluster-driven Low-rank Matrix Completion.

On the Effectiveness of Low Frequency Perturbations. A Part Power Set Model for Scale-Free Person Retrieval. Yunhang Shen, Rongrong Ji, Xiaopeng Hong, Feng Zheng, Xiaowei Guo, Yongjian Wu, Feiyue Huang. Rapid Performance Gain through Active Model Reuse. A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification. Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization.

Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning. Gradient Boosting with Piece-Wise Linear Regression Trees. The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning. A Principled Approach for Learning Task Similarity in Multitask Learning.

Changjian Shui, Mahdieh Abbasi, Louis-Émile Robitaille, Boyu Wang, Christian Gagné. Structure Learning for Safe Policy Improvement. Play and Prune: Adaptive Filter Pruning for Deep Model Compression. Solving Continual Combinatorial Selection via Deep Reinforcement Learning.

Hyungseok Song, Hyeryung Jang, Hai H. Tran, Se-eun Yoon, Kyunghwan Son, Donggyu Yun, Hyoju Chung, Yung Yi. Playing FPS Games With Environment-Aware Hierarchical Reinforcement Learning. Parallel Wasserstein Generative Adversarial Nets with Multiple Discriminators. Finding Statistically Significant Interactions between Continuous Features.

Fast and Robust Multi-View Multi-Task Learning via Group Sparsity. Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning. Adversarial Imitation Learning from Incomplete Demonstrations. Heavy-ball Algorithms Always Escape Saddle Points. MEGAN: A Generative Adversarial Network for Multi-View Network Embedding. Metric Learning on Healthcare Data with Incomplete Modalities. Deeply-learned Hybrid Representations for Facial Age Estimation.

AugBoost: Gradient Boosting Enhanced with Step-Wise Feature Augmentation. Adversarial Graph Embedding for Ensemble Clustering. Hierarchical Inter-Attention Network for Document Classification with Multi-Task Learning. Image Captioning with Compositional Neural Module Networks. Imitation Learning from Video by Leveraging Proprioception.

Exchangeability and Kernel Invariance in Trained MLPs. Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning. Object Detection based Deep Unsupervised Hashing. Ensemble-based Ultrahigh-dimensional Variable Screening. Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian. Learning to Interpret Satellite Images using Wikipedia. Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon.

DeepCU: Integrating both Common and Unique Latent Information for Multimodal Sentiment Analysis. Interpolation Consistency Training for Semi-supervised Learning. Sharing Experience in Multitask Reinforcement Learning. Tung-Long Vuong, Do-Van Nguyen, Tai-Long Nguyen, Cong-Minh Bui, Hai-Dang Kieu, Viet-Cuong Ta, Quoc-Long Tran, Thanh-Ha Le. Recurrent Existence Determination Through Policy Optimization.

Boundary Perception Guidance: A Scribble-Supervised Semantic Segmentation Approach. Bin Wang, Guojun Qi, Sheng Tang, Tianzhu Zhang, Yunchao Wei, Linghui Li, Yongdong Zhang. Attributed Graph Clustering: A Deep Attentional Embedding Approach.

Spectral Perturbation Meets Incomplete Multi-view Data. Measuring Structural Similarities in Finite MDPs. Discriminative and Correlative Partial Multi-Label Learning.

DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation. CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets. Classification with Label Distribution Learning. Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi. Discrete Binary Coding based Label Distribution Learning. Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. MUSICAL: Multi-Scale Image Contextual Attention Learning for Inpainting.

Partial Label Learning with Unlabeled Data. Heterogeneous Graph Matching Networks for Unknown Malware Detection. Shen Wang, Zhengzhang Chen, Xiao Yu, Ding Li, Jingchao Ni, Lu-An Tang, Jiaping Gui, Zhichun Li, Haifeng Chen, Philip S. Modeling Multi-Purpose Sessions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks. Multi-view Clustering via Late Fusion Alignment Maximization.

Siwei Wang, Xinwang Liu, En Zhu, Chang Tang, Jiyuan Liu, Jingtao Hu, Jingyuan Xia, Jianping Yin. COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning.

Position Focused Attention Network for Image-Text Matching. Tag2Gauss: Learning Tag Representations via Gaussian Distribution in Tagged Networks. Weak Supervision Enhanced Generative Network for Question Generation. Unified Embedding Model over Heterogeneous Information Network for Personalized Recommendation.

Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human and Agent Demonstrations. Hierarchical Diffusion Attention Network. Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking. Nirandika Wanigasekara, Yuxuan Liang, Siong Thye Goh, Ye Liu, Joseph Jay Williams, David S.

Learning for Tail Label Data: A Label-Specific Feature Approach. Bayesian Uncertainty Matching for Unsupervised Domain Adaptation. RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering.

Neural News Recommendation with Attentive Multi-View Learning. PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation. Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. Multi-View Multi-Label Learning with View-Specific Information Extraction. Xuan Wu, Qing-Guo Chen, Yao Hu, Dengbao Wang, Xiaodong Chang, Xiaobo Wang, Min-Ling Zhang.

Trend-Aware Tensor Factorization for Job Skill Demand Analysis. Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference. BPAM: Recommendation Based on BP Neural Network with Attention Mechanism. Incremental Few-Shot Learning for Pedestrian Attribute Recognition. Reparameterizable Subset Sampling via Continuous Relaxations. CFM: Convolutional Factorization Machines for Context-Aware Recommendation. Adversarial Incomplete Multi-view Clustering.

Graph Contextualized Self-Attention Network for Session-based Recommendation. Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S.

Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, Xiaofang Zhou. Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs. Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective.

Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin. MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions. Commit Message Generation for Source Code Changes.

Latent Semantics Encoding for Label Distribution Learning. Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation. On the Convergence of Stochastic Gradient Descent with Extrapolation for Non-Convex Minimization.

Transfer of Temporal Logic Formulas in Reinforcement Learning. Deep Correlated Predictive Subspace Learning for Incomplete Multi-View Semi-Supervised Classification. Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks.

Learning Strictly Orthogonal p-Order Nonnegative Laplacian Embedding via Smoothed Iterative Reweighted Method. Low-Bit Quantization for Attributed Network Representation Learning. Topology Optimization based Graph Convolutional Network. Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology.

Deep Multi-Task Learning with Adversarial-and-Cooperative Nets. Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network. Comprehensive Semi-Supervised Multi-Modal Learning. SPAGAN: Shortest Path Graph Attention Network. On the Estimation of Treatment Effect with Text Covariates. Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. Quanming Yao, Xiawei Guo, James Kwok, Weiwei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang.

Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers. A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment. Distributed Collaborative Feature Selection Based on Intermediate Representation. Out-of-sample Node Representation Learning for Heterogeneous Graph in Real-time Android Malware Detection.

Yanfang Ye, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao. Neural Network based Continuous Conditional Random Field for Fine-grained Crime Prediction. BN-invariant Sharpness Regularizes the Training Model to Better Generalization.

Geometric Understanding for Unsupervised Subspace Learning. Belief Propagation Network for Hard Inductive Semi-Supervised Learning. Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control.

Semi-supervised Three-dimensional Reconstruction Framework with GAN. Interpreting and Evaluating Neural Network Robustness.

VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation.

Progressive Transfer Learning for Person Re-identification. Zhengxu Yu, Zhongming Jin, Long Wei, Jishun Guo, Jianqiang Huang, Deng Cai, Xiaofei He, Xian-Sheng Hua. DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns. KCNN: Kernel-wise Quantization to Remarkably Decrease Multiplications in Convolutional Neural Network.

Positive and Unlabeled Learning with Label Disambiguation. Generalized Majorization-Minimization for Non-Convex Optimization.

STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems. Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation.

ProNE: Fast and Scalable Network Representation Learning. Towards Robust ResNet: A Small Step but a Giant Leap. High Dimensional Bayesian Optimization via Supervised Dimension Reduction. Efficient Non-parametric Bayesian Hawkes Processes. Inferring Substitutable Products with Deep Network Embedding. Shijie Zhang, Hongzhi Yin, Qinyong Wang, Tong Chen, Hongxu Chen, Quoc Viet Hung Nguyen.

Quaternion Collaborative Filtering for Recommendation. Feature-level Deeper Self-Attention Network for Sequential Recommendation. Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, Xiaofang Zhou. Attributed Graph Clustering via Adaptive Graph Convolution.

InteractionNN: A Neural Network for Learning Hidden Features in Sparse Prediction. Multi-Group Encoder-Decoder Networks to Fuse Heterogeneous Data for Next-Day Air Quality Prediction. Yawen Zhang, Qin Lv, Duanfeng Gao, Si Shen, Robert Dick, Michael Hannigan, Qi Liu.

Taming the Noisy Gradient: Train Deep Neural Networks with Small Batch Sizes. Accelerated Inference Framework of Sparse Neural Network Based on Nested Bitmask Structure. ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling. Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning. Open-Ended Long-Form Video Question Answering via Hierarchical Convolutional Self-Attention Networks.

Localizing Unseen Activities in Video via Image Query. Multi-Prototype Networks for Unconstrained Set-based Face Recognition. Jian Zhao, Jianshu Li, Xiaoguang Tu, Fang Zhao, Yuan Xin, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng.

Large Scale Evolving Graphs with Burst Detection. AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN.

Metadata-driven Task Relation Discovery for Multi-task Learning. BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series. Latent Distribution Preserving Deep Subspace Clustering. Reinforcement Learning Experience Reuse with Policy Residual Representation. WenJi Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, Zhi-Hua Zhou. Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems.

One-Shot Texture Retrieval with Global Context Metric. HDI-Forest: Highest Density Interval Regression Forest. Prediction of Mild Cognitive Impairment Conversion Using Auxiliary Information. Simultaneous Representation Learning and Clustering for Incomplete Multi-view Data.

Persistence Bag-of-Words for Topological Data Analysis. Bartosz Zieliński, Michał Lipiński, Mateusz Juda, Matthias Zeppelzauer, Paweł Dłotko. Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains. Machine Learning Applications. Predicting the Visual Focus of Attention in Multi-Person Discussion Videos. Chongyang Bai, Srijan Kumar, Jure Leskovec, Miriam Metzger, Jay F. Nunamaker, V. A Quantum-inspired Classical Algorithm for Separable Non-negative Matrix Factorization.

MLRDA: A Multi-Task Semi-Supervised Learning Framework for Drug-Drug Interaction Prediction. Combining ADMM and the Augmented Lagrangian Method for Efficiently Handling Many Constraints.

Playing Card-Based RTS Games with Deep Reinforcement Learning. FSM: A Fast Similarity Measurement for Gene Regulatory Networks via Genes' Influence Power. Pseudo Supervised Matrix Factorization in Discriminative Subspace. Representation Learning-Assisted Click-Through Rate Prediction. Dynamic Electronic Toll Collection via Multi-Agent Deep Reinforcement Learning with Edge-Based Graph Convolutional Networks. FireCast: Leveraging Deep Learning to Predict Wildfire Spread.

Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent. Randomized Adversarial Imitation Learning for Autonomous Driving. Scaling Fine-grained Modularity Clustering for Massive Graphs. Medical Concept Embedding with Multiple Ontological Representations. Lihong Song, Chin Wang Cheong, Kejing Yin, William K. Cheung, Benjamin C. Fung, Jonathan Poon. Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation.

Dual-Path in Dual-Path Network for Single Image Dehazing. Disparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation.

Multidisciplinary Topics and Applications. Predicting dominance in multi-person videos. Chongyang Bai, Maksim Bolonkin, Srijan Kumar, Jure Leskovec, Judee Burgoon, Norah Dunbar, V. Procedural Generation of Initial States of Sokoban. DeepInspect: A Black-box Trojan Detection and Mitigation Framework for Deep Neural Networks.

VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities. Xu Duan, Jingzheng Wu, Shouling Ji, Zhiqing Rui, Tianyue Luo, Mutian Yang, Yanjun Wu. Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach. Model-Agnostic Adversarial Detection by Random Perturbations. Musical Composition Style Transfer via Disentangled Timbre Representations. Multiple Policy Value Monte Carlo Tree Search. Robustra: Training Provable Robust Neural Networks over Reference Adversarial Space.

Dilated Convolution with Dilated GRU for Music Source Separation. Locate-Then-Detect: Real-time Web Attack Detection via Attention-based Deep Neural Networks. Data Poisoning against Differentially-Private Learners: Attacks and Defenses. LogAnomaly: Unsupervised Detection of Sequential and Quantitative Anomalies in Unstructured Logs.

Weibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, Rong Zhou. Decidability of Model Checking Multi-Agent Systems with Regular Expressions against Epistemic HS Specifications.

Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness. NhatHai Phan, Minh N. Vu, Yang Liu, Ruoming Jin, Dejing Dou, Xintao Wu, My T. Demystifying the Combination of Dynamic Slicing and Spectrum-based Fault Localization. Equally-Guided Discriminative Hashing for Cross-modal Retrieval. A Privacy Preserving Collusion Secure DCOP Algorithm. Two-Stage Generative Models of Simulating Training Data at The Voxel Level for Large-Scale Microscopy Bioimage Segmentation.

Lower Bound of Locally Differentially Private Sparse Covariance Matrix Estimation. Principal Component Analysis in the Local Differential Privacy Model. Binarized Collaborative Filtering with Distilling Graph Convolutional Network. Novel Collaborative Filtering Recommender Friendly to Privacy Protection. Adversarial Examples for Graph Data: Deep Insights into Attack and Defense. FABA: An Algorithm for Fast Aggregation against Byzantine Attacks in Distributed Neural Networks.

BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference. Toward Efficient Navigation of Massive-Scale Geo-Textual Streams. Temporal Pyramid Pooling Convolutional Neural Network for Cover Song Identification. Data Poisoning Attack against Knowledge Graph Embedding. Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, Kui Ren.

On Privacy Protection of Latent Dirichlet Allocation Model Training. K-Core Maximization: An Edge Addition Approach. Pivotal Relationship Identification: The K-Truss Minimization Problem. Early Discovery of Emerging Entities in Microblogs. Neural Program Induction for KBQA Without Gold Programs or Query Annotations. Ghulam Ahmed Ansari, Amrita Saha, Vishwajeet Kumar, Mohan Bhambhani, Karthik Sankaranarayanan, Soumen Chakrabarti.

Medical Concept Representation Learning from Multi-source Data. Multi-Domain Sentiment Classification Based on Domain-Aware Embedding and Attention. A Latent Variable Model for Learning Distributional Relation Vectors. Generating Multiple Diverse Responses with Multi-Mapping and Posterior Mapping Selection. Sentiment-Controllable Chinese Poetry Generation. From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots.

Learning towards Abstractive Timeline Summarization. Coreference Aware Representation Learning for Neural Named Entity Recognition. Learning Assistance from an Adversarial Critic for Multi-Outputs Prediction. End-to-End Multi-Perspective Matching for Entity Resolution. Difficulty Controllable Generation of Reading Comprehension Questions.

Modeling Source Syntax and Semantics for Neural AMR Parsing. CNN-Based Chinese NER with Lexicon Rethinking. Dual Visual Attention Network for Visual Dialog.

AmazonQA: A Review-Based Question Answering Task. Mansi Gupta, Nitish Kulkarni, Raghuveer Chanda, Anirudha Rayasam, Zachary C. Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts. Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, Michael Katz. GSN: A Graph-Structured Network for Multi-Party Dialogues. Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization.

Relation Extraction Using Supervision from Topic Knowledge of Relation Labels. Haiyun Jiang, Li Cui, Zhe Xu, Deqing Yang, Jindong Chen, Chenguang Li, Jingping Liu, Jiaqing Liang, Chao Wang, Yanghua Xiao, Wei Wang. Representation Learning with Weighted Inner Product for Universal Approximation of General Similarities. Incorporating Structural Information for Better Coreference Resolution.

Knowledge Base Question Answering with Topic Units. Adversarial Transfer for Named Entity Boundary Detection with Pointer Networks. Towards Discriminative Representation Learning for Speech Emotion Recognition. Self-attentive Biaffine Dependency Parsing. Reading selectively via Binary Input Gated Recurrent Unit.

Learning to Select Knowledge for Response Generation in Dialog Systems. Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Sentiment Classification.

Exploring and Distilling Cross-Modal Information for Image Captioning. Network Embedding with Dual Generation Tasks. Building Personalized Simulator for Interactive Search. Qianlong Liu, Baoliang Cui, Zhongyu Wei, Baolin Peng, Haikuan Huang, Hongbo Deng, Jianye Hao, Xuanjing Huang, Kam-Fai Wong. A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer. Unsupervised Neural Aspect Extraction with Sememes.

Randomized Greedy Search for Structured Prediction: Amortized Inference and Learning. Chao Ma, F A Rezaur Rahman Chowdhury, Aryan Deshwal, Md Rakibul Islam, Janardhan Rao Doppa, Dan Roth. Aspect-Based Sentiment Classification with Attentive Neural Turing Machines. Qianren Mao, Jianxin Li, Senzhang Wang, Yuanning Zhang, Hao Peng, Min He, Lihong Wang. Learning Task-Specific Representation for Novel Words in Sequence Labeling.

Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control. Learn to Select via Hierarchical Gate Mechanism for Aspect-Based Sentiment Analysis.

Aligning Learning Outcomes to Learning Resources: A Lexico-Semantic Spatial Approach. Swarnadeep Saha, Malolan Chetlur, Tejas Indulal Dhamecha, W M Gayathri K Wijayarathna, Red Mendoza, Paul Gagnon, Nabil Zary, Shantanu Godbole. A Deep Generative Model for Code Switched Text. Bidisha Samanta, Sharmila Reddy, Hussain Jagirdar, Niloy Ganguly, Soumen Chakrabarti. Knowledge Aware Semantic Concept Expansion for Image-Text Matching.

Exploiting Persona Information for Diverse Generation of Conversational Responses. Cold-Start Aware Deep Memory Network for Multi-Entity Aspect-Based Sentiment Analysis. GANs for Semi-Supervised Opinion Spam Detection. PRoFET: Predicting the Risk of Firms from Event Transcripts. Unsupervised Embedding Enhancements of Knowledge Graphs using Textual Associations. Swell-and-Shrink: Decomposing Image Captioning by Transformation and Summarization. T-CVAE: Transformer-Based Conditioned Variational Autoencoder for Story Completion.

Robust Embedding with Multi-Level Structures for Link Prediction. Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation. Correct-and-Memorize: Learning to Translate from Interactive Revisions. Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach. Mask and Infill: Applying Masked Language Model for Sentiment Transfer.

Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs. RTHN: A RNN-Transformer Hierarchical Network for Emotion Cause Extraction. Sharing Attention Weights for Fast Transformer. A Goal-Driven Tree-Structured Neural Model for Math Word Problems. Dual-View Variational Autoencoders for Semi-Supervised Text Matching. Earlier Attention? Aspect-Aware LSTM for Aspect-Based Sentiment Analysis.

Bowen Xing, Lejian Liao, Dandan Song, Jingang Wang, Fuzheng Zhang, Zhongyuan Wang, Heyan Huang. Polygon-Net: A General Framework for Jointly Boosting Multiple Unsupervised Neural Machine Translation Models.

Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning. Mengge Xue, Weiming Cai, Jinsong Su, Linfeng Song, Yubin Ge, Yubao Liu, Bin Wang. Robust Audio Adversarial Example for a Physical Attack. HorNet: A Hierarchical Offshoot Recurrent Network for Improving Person Re-ID via Image Captioning.

Knowledge-enhanced Hierarchical Attention for Community Question Answering with Multi-task and Adaptive Learning. Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling. Triplet Enhanced AutoEncoder: Model-free Discriminative Network Embedding. Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax. Yinfei Yang, Gustavo Hernandez Abrego, Steve Yuan, Mandy Guo, Qinlan Shen, Daniel Cer, Yun-hsuan Sung, Brian Strope, Ray Kurzweil.

Utilizing Non-Parallel Text for Style Transfer by Making Partial Comparisons. Refining Word Representations by Manifold Learning. Beyond Word Attention: Using Segment Attention in Neural Relation Extraction. Adapting BERT for Target-Oriented Multimodal Sentiment Classification.

Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations. Dong Zhang, Liangqing Wu, Changlong Sun, Shoushan Li, Qiaoming Zhu, Guodong Zhou. Extracting Entities and Events as a Single Task Using a Transition-Based Neural Model. Multi-view Knowledge Graph Embedding for Entity Alignment. Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis. A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots.

Recurrent Neural Network for Text Classification with Hierarchical Multiscale Dense Connections. RLTM: An Efficient Neural IR Framework for Long Documents. Dynamically Route Hierarchical Structure Representation to Attentive Capsule for Text Classification.

Sequence Generation: From Both Sides to the Middle. Getting in Shape: Word Embedding SubSpaces. A Span-based Joint Model for Opinion Target Extraction and Target Sentiment Classification.

Earliest-Completion Scheduling of Contract Algorithms with End Guarantees. Finding Optimal Solutions in HTN Planning - A SAT-based Approach. Faster Dynamic Controllability Checking in Temporal Networks with Integer Bounds.

Regular Decision Processes: A Model for Non-Markovian Domains. Strong Fully Observable Non-Deterministic Planning with LTL and LTLf Goals.

Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks. Steven Carr, Nils Jansen, Ralf Wimmer, Alexandru Serban, Bernd Becker, Ufuk Topcu. Influence of State-Variable Constraints on Partially Observable Monte Carlo Planning. Online Probabilistic Goal Recognition over Nominal Models.

Generalized Potential Heuristics for Classical Planning. Subgoal-Based Temporal Abstraction in Monte-Carlo Tree Search. Fair Online Allocation of Perishable Goods and its Application to Electric Vehicle Charging. Enrico H. Gerding, Alvaro Perez-Diaz, Haris Aziz, Serge Gaspers, Antonia Marcu, Nicholas Mattei, Toby Walsh.

Dynamic logic of parallel propositional assignments and its applications to planning. Approximability of Constant-horizon Constrained POMDP. Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations. Partitioning Techniques in LTLf Synthesis. Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning. Thomy Phan, Thomas Gabor, Robert Müller, Christoph Roch, Claudia Linnhoff-Popien. A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition.

Pattern Selection for Optimal Classical Planning with Saturated Cost Partitioning. Scheduling Jobs with Stochastic Processing Time on Parallel Identical Machines.

On Computational Complexity of Pickup-and-Delivery Problems with Precedence Constraints or Time Windows. Merge-and-Shrink Task Reformulation for Classical Planning. Steady-State Policy Synthesis for Verifiable Control. Energy-Efficient Slithering Gait Exploration for a Snake-Like Robot Based on Reinforcement Learning.

The Parameterized Complexity of Motion Planning for Snake-Like Robots. Unsupervised Learning of Monocular Depth and Ego-Motion using Conditional PatchGANs. Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos. Statistical Guarantees for the Robustness of Bayesian Neural Networks. Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, Matthew Wicker. Lifted Message Passing for Hybrid Probabilistic Inference.

Bayesian Parameter Estimation for Nonlinear Dynamics Using Sensitivity Analysis. Thompson Sampling on Symmetric Alpha-Stable Bandits. On Constrained Open-World Probabilistic Databases. An End-to-End Community Detection Model: Integrating LDA into Markov Random Field via Factor Graph. Exact Bernoulli Scan Statistics using Binary Decision Diagrams. Hyper-parameter Tuning under a Budget Constraint. Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models.

ISLF: Interest Shift and Latent Factors Combination Model for Session-based Recommendation. DiffChaser: Detecting Disagreements for Deep Neural Networks. AI for Improving Human Well-being.

SparseSense: Human Activity Recognition from Highly Sparse Sensor Data-streams Using Set-based Neural Networks. Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour. Decision Making for Improving Maritime Traffic Safety Using Constraint Programming. Evaluating the Interpretability of the Knowledge Compilation Map: Communicating Logical Statements Effectively.

AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion. Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization under Uncertainty. Risk Assessment for Networked-guarantee Loans Using High-order Graph Attention Representation.

PI-Bully: Personalized Cyberbullying Detection with Peer Influence. The Price of Local Fairness in Multistage Selection. Vitalii Emelianov, George Arvanitakis, Nicolas Gast, Krishna Gummadi, Patrick Loiseau. Enhancing Stock Movement Prediction with Adversarial Training.

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One of Josh's first memories is of playing Quake 2 on the family computer when he was much too young to be doing that, and he's been irreparably game-brained ever since. His writing has been featured in Vice, Fanbyte, and the Financial Times. He'll play pretty much anything, and has written far too much on everything from visual novels to Assassin's Creed. His most profound loves are for CRPGs, immersive sims, and any game whose ambition outstrips its budget. He thinks you're all far too mean about Deus Ex: Invisible War.

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Microsoft Excel is a spreadsheet developed by Microsoft for Windows , macOS , Android and iOS. It features calculation or computation capabilities, graphing tools, pivot tables , and a macro programming language called Visual Basic for Applications VBA. Excel forms part of the Microsoft Office suite of software. Microsoft Excel has the basic features of all spreadsheets, [7] using a grid of cells arranged in numbered rows and letter-named columns to organize data manipulations like arithmetic operations.

It has a battery of supplied functions to answer statistical, engineering, and financial needs. In addition, it can display data as line graphs, histograms and charts, and with a very limited three-dimensional graphical display.

It allows sectioning of data to view its dependencies on various factors for different perspectives using pivot tables and the scenario manager. It does this by simplifying large data sets via PivotTable fields. It has a programming aspect, Visual Basic for Applications , allowing the user to employ a wide variety of numerical methods, for example, for solving differential equations of mathematical physics, [9] [10] and then reporting the results back to the spreadsheet.

It also has a variety of interactive features allowing user interfaces that can completely hide the spreadsheet from the user, so the spreadsheet presents itself as a so-called application , or decision support system DSS , via a custom-designed user interface, for example, a stock analyzer, [11] or in general, as a design tool that asks the user questions and provides answers and reports.

Excel was not designed to be used as a database. Microsoft allows for a number of optional command-line switches to control the manner in which Excel starts. Excel has functions. Microsoft classifies these functions in 14 categories. Of the current functions, may be called from VBA as methods of the object "WorksheetFunction" [17] and 44 have the same names as VBA functions.

With the introduction of LAMBDA, Excel will become Turing complete. The Windows version of Excel supports programming through Microsoft's Visual Basic for Applications VBA , which is a dialect of Visual Basic. Programming with VBA allows spreadsheet manipulation that is awkward or impossible with standard spreadsheet techniques. Programmers may write code directly using the Visual Basic Editor VBE , which includes a window for writing code, debugging code, and code module organization environment.

The user can implement numerical methods as well as automating tasks such as formatting or data organization in VBA [20] and guide the calculation using any desired intermediate results reported back to the spreadsheet. VBA was removed from Mac Excel , as the developers did not believe that a timely release would allow porting the VBA engine natively to Mac OS X.

VBA was restored in the next version, Mac Excel , [21] although the build lacks support for ActiveX objects, impacting some high level developer tools. A common and easy way to generate VBA code is by using the Macro Recorder. These actions can then be repeated automatically by running the macro. The macros can also be linked to different trigger types like keyboard shortcuts, a command button or a graphic.

The actions in the macro can be executed from these trigger types or from the generic toolbar options. The VBA code of the macro can also be edited in the VBE. Certain features such as loop functions and screen prompt by their own properties, and some graphical display items, cannot be recorded but must be entered into the VBA module directly by the programmer.

Advanced users can employ user prompts to create an interactive program, or react to events such as sheets being loaded or changed. Macro Recorded code may not be compatible with Excel versions. Some code that is used in Excel cannot be used in Excel Making a Macro that changes the cell colors and making changes to other aspects of cells may not be backward compatible.

VBA code interacts with the spreadsheet through the Excel Object Model , [24] a vocabulary identifying spreadsheet objects, and a set of supplied functions or methods that enable reading and writing to the spreadsheet and interaction with its users for example, through custom toolbars or command bars and message boxes. User-created VBA subroutines execute these actions and operate like macros generated using the macro recorder, but are more flexible and efficient.

From its first version Excel supported end-user programming of macros automation of repetitive tasks and user-defined functions extension of Excel's built-in function library. In early versions of Excel, these programs were written in a macro language whose statements had formula syntax and resided in the cells of special-purpose macro sheets stored with file extension. XLM in Windows. XLM was the default macro language for Excel through Excel 4.

After version 5. All versions of Excel, including Excel are capable of running an XLM macro, though Microsoft discourages their use. Excel supports charts , graphs , or histograms generated from specified groups of cells. It also supports Pivot Charts that allow for a chart to be linked directly to a Pivot table. This allows the chart to be refreshed with the Pivot Table. The generated graphic component can either be embedded within the current sheet or added as a separate object. These displays are dynamically updated if the content of cells changes.

For example, suppose that the important design requirements are displayed visually; then, in response to a user's change in trial values for parameters, the curves describing the design change shape, and their points of intersection shift, assisting the selection of the best design.

Additional features are available using add-ins. Several are provided with Excel, including:. Versions of Excel up to 7. Versions 8. Version Microsoft Excel up until version used a proprietary binary file format called Excel Binary File Format. XLS as its primary format.

Although supporting and encouraging the use of new XML-based formats as replacements, Excel remained backwards-compatible with the traditional, binary formats. In addition, most versions of Microsoft Excel can read CSV , DBF , SYLK , DIF , and other legacy formats.

Support for some older file formats was removed in Excel org has created documentation of the Excel format. Two epochs of the format exist: the OLE format, and the older stream format. The XML Spreadsheet format introduced in Excel [29] is a simple, XML based format missing some more advanced features like storage of VBA macros. Though the intended file extension for this format is. xml , the program also correctly handles XML files with. xls extension. This feature is widely used by third-party applications e.

MySQL Query Browser to offer "export to Excel" capabilities without implementing binary file format. The following example will be correctly opened by Excel if saved either as Book1.

xml or Book1. xls :. Microsoft Excel , along with the other products in the Microsoft Office suite, introduced new file formats. The first of these. xlsx is defined in the Office Open XML OOXML specification.

Windows applications such as Microsoft Access and Microsoft Word , as well as Excel can communicate with each other and use each other's capabilities.

The most common are Dynamic Data Exchange : although strongly deprecated by Microsoft, this is a common method to send data between applications running on Windows, with official MS publications referring to it as "the protocol from hell". It is very common in financial markets, being used to connect to important financial data services such as Bloomberg and Reuters.

OLE Object Linking and Embedding allows a Windows application to control another to enable it to format or calculate data. This may take on the form of "embedding" where an application uses another to handle a task that it is more suited to, for example a PowerPoint presentation may be embedded in an Excel spreadsheet or vice versa.

Excel users can access external data sources via Microsoft Office features such as for example. odc connections built with the Office Data Connection file format. Excel files themselves may be updated using a Microsoft supplied ODBC driver. Excel can accept data in real-time through several programming interfaces, which allow it to communicate with many data sources such as Bloomberg and Reuters through addins such as Power Plus Pro.

Alternatively, Microsoft Query provides ODBC-based browsing within Microsoft Excel. Programmers have produced APIs to open Excel spreadsheets in a variety of applications and environments other than Microsoft Excel. These include opening Excel documents on the web using either ActiveX controls, or plugins like the Adobe Flash Player.

The Apache POI opensource project provides Java libraries for reading and writing Excel spreadsheet files. All passwords except password to open a document can be removed instantly regardless of the Microsoft Excel version used to create the document. These types of passwords are used primarily for shared work on a document. Such password-protected documents are not encrypted , and a data sources from a set password is saved in a document's header. The only type of password that can prevent a trespasser from gaining access to a document is password to open a document.

The cryptographic strength of this kind of protection depends strongly on the Microsoft Excel version that was used to create the document. In Microsoft Excel 95 and earlier versions, the password to open is converted to a bit key that can be instantly cracked.

As regards services that use rainbow tables e. Password-Find , it takes up to several seconds to remove protection. In addition, password-cracking programs can brute-force attack passwords at a rate of hundreds of thousands of passwords a second, which not only lets them decrypt a document but also find the original password.

Due to the CSP, an Excel file can't be decrypted, and thus the password to open can't be removed, though the brute-force attack speed remains quite high. Therefore, users who do not change the default settings lack reliable protection of their documents. The situation changed fundamentally in Excel , where the modern AES algorithm with a key of bits started being used for decryption, and a 50,fold use of the hash function SHA1 reduced the speed of brute-force attacks down to hundreds of passwords per second.

In Excel , the strength of the protection by the default was increased two times due to the use of a ,fold SHA1 to convert a password to a key. Excel Mobile is a spreadsheet program that can edit XLSX files. It can edit and format text in cells, calculate formulas, search within the spreadsheet, sort rows and columns, freeze panes, filter the columns, add comments, and create charts.

It can't add columns or rows except at the edge of the document, rearrange columns or rows, delete rows or columns, or add spreadsheet tabs.

September 13, Automatic Verification of FSA Strategies via Counterexample-Guided Local Search for Invariants. The increased transparency brought about by Open Banking brings a vast array of additional benefits, such as helping fraud detection companies better monitor customer accounts and identify problems much earlier. The CFPB may be facing its most significant legal threat yet. Included in Office , this is the next major version after v

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