AAAI

Deep Features Analysis with Attention Networks

Shipeng Xie, Da Chen, Rong Zhang, Hui Xue

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Towards Aggregating Weighted Feature Attributions

Umang Bhatt, Pradeep Ravikumar, Jose M. F. Moura

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift

Carles Gelada, Marc G. Bellemare

Comments AAAI 2019

Visualizing Semantic Structures of Sequential Data by Learning Temporal Dependencies

Kyoung-Woon On, Eun-Sol Kim, Yu-Jung Heo, Byoung-Tak Zhang

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples

Yinpeng Dong, Fan Bao, Hang Su, Jun Zhu

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Action Branching Architectures for Deep Reinforcement Learning

Arash Tavakoli, Fabio Pardo, Petar Kormushev

Comments AAAI 2018, NIPS 2017 Deep RL Symposium, code: https://github.com/atavakol/action-branching-agents

Journal ref AAAI 32: 4131-4138 (2018)

Quantifying Interpretability and Trust in Machine Learning Systems

Philipp Schmidt, Felix Biessmann

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Visualized Insights into the Optimization Landscape of Fully Convolutional Networks

Jianjie Lu, Kai-yu Tong

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Explainable Failure Predictions with RNN Classifiers based on Time Series Data

Ioana Giurgiu, Anika Schumann

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology

Jiseob Kim, Byoung-Tak Zhang

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

A Universal Logic Operator for Interpretable Deep Convolution Networks

KamWoh Ng, Lixin Fan, Chee Seng Chan

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

A tensorized logic programming language for large-scale data

Ryosuke Kojima, Taisuke Sato

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]

Yuxia Geng, Jiaoyan Chen, Ernesto Jimenez-Ruiz, Huajun Chen

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning

FANDA: A Novel Approach to Perform Follow-up Query Analysis

Qian Liu, Bei Chen, Jian-Guang Lou, Ge Jin, Dongmei Zhang

Comments Accepted by AAAI 2019

Sparse Compositional Metric Learning

Yuan Shi, Aurélien Bellet, Fei Sha

Comments 18 pages. To be published in Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI 2014)

Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)

Quanshi Zhang, Yu Yang, Ying Nian Wu

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning. arXiv admin note: substantial text overlap with arXiv:1805.07468

Partial Verification as a Substitute for Money

Sofia Ceppi, Ian Kash, Rafael Frongillo

Comments Extended Version of 'Partial Verification as a Substitute for Money', AAAI 2019

Inferring Concept Prerequisite Relations from Online Educational Resources

Sudeshna Roy, Meghana Madhyastha, Sheril Lawrence, Vaibhav Rajan

Comments Accepted at the AAAI Conference on Innovative Applications of Artificial Intelligence (IAAI-19)

Disentangled Variational Representation for Heterogeneous Face Recognition

Xiang Wu, Huaibo Huang, Vishal M. Patel, Ran He, Zhenan Sun

Comments AAAI 2019

Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering

Binghui Chen, Weihong Deng

Comments AAAI 2019, Spotlight

CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison

Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, Andrew Y. Ng

Comments Published in AAAI 2019

Network Transplanting (extended abstract)

Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu

Comments In AAAI-19 Workshop on Network Interpretability for Deep Learning. arXiv admin note: substantial text overlap with arXiv:1804.10272

Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

Dongliang He, Xiang Zhao, Jizhou Huang, Fu Li, Xiao Liu, Shilei Wen

Comments AAAI 2019

Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval

Zhaoqun Li, Cheng Xu, Biao Leng

Comments Accepted by AAAI 2019

Integrative Biological Simulation, Neuropsychology, and AI Safety

Gopal P. Sarma, Adam Safron, Nick J. Hay

Comments 5 pages

Journal ref Proceedings of the AAAI Workshop on Artificial Intelligence Safety 2019 co-located with the Thirty-Third AAAI Conference on Artificial Intelligence 2019 (AAAI 2019)

Algorithms for Estimating Trends in Global Temperature Volatility

Arash Khodadadi, Daniel J McDonald

Comments Published in AAAI-19

Computing Human-Understandable Strategies

Sam Ganzfried, Farzana Yusuf

Comments Earlier version appeared in Proceedings of the Workshop on Computer Poker and Imperfect Information Games at AAAI Conference on Artificial Intelligence, 2017

Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification

Youngmin Ro, Jongwon Choi, Dae Ung Jo, Byeongho Heo, Jongin Lim, Jin Young Choi

Comments Accepted to AAAI 2019

Theory of Minds: Understanding Behavior in Groups Through Inverse Planning

Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum

Comments published in AAAI 2019; Michael Shum and Max Kleiman-Weiner contributed equally

Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation

Qiuyuan Huang, Zhe Gan, Asli Celikyilmaz, Dapeng Wu, Jianfeng Wang, Xiaodong He

Comments Accepted to AAAI 2019