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ORCIDTim Oates 0001
Timothy Oates 0001
Person information
- affiliation: University of Maryland Baltimore County, Baltimore, MD, USA
- affiliation: Synaptiq, USA
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2020 – today
- 2024
[c158]Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection. AISTATS 2024: 4042-4050- [c157]Bharat Prakash, Tim Oates, Tinoosh Mohsenin:
Using LLMs for Augmenting Hierarchical Agents with Common Sense Priors. FLAIRS 2024 - [c156]Khondoker Murad Hossain, Tim Oates:
Ten-Guard: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks. ICASSP 2024: 7080-7084 - [c155]Khondoker Murad Hossain, Tim Oates:
Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks. ICC 2024: 740-745 - [i44]Khondoker Murad Hossain, Tim Oates:
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks. CoRR abs/2401.05432 (2024) - [i43]Khondoker Murad Hossain, Tim Oates:
Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks. CoRR abs/2403.08208 (2024) - [i42]Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection. CoRR abs/2403.17978 (2024) - 2023
- [c154]Khondoker Murad Hossain, Tim Oates:
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks. SafeAI@AAAI 2023 - [c153]Sourajit Saha, Shaswati Saha, Md. Osman Gani, Tim Oates, David Chapman:
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget (Student Abstract). AAAI 2023: 16314-16315 - [c152]Baoluo Meng, Joyanta Debnath, Sarat Chandra Varanasi, Emmanuel Manoloios, Michael Durling, Saswata Paul, Daniel Prince, Saif Alsabbagh, Richard Haadsma, Craig McMillan, Chi Zhang, Tim Oates:
Towards a Correct-by-Construction Design of Integrated Modular Avionics. FMCAD 2023: 221-227 - [c151]Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Recasting Self-Attention with Holographic Reduced Representations. ICML 2023: 490-507 - [c150]Corey J. Nolet, Divye Gala, Alexandre Fender, Mahesh Doijade, Joe Eaton, Edward Raff, John Zedlewski, Brad Rees, Tim Oates:
cuSLINK: Single-Linkage Agglomerative Clustering on the GPU. ECML/PKDD (1) 2023: 711-726 - [i41]Sourajit Saha, Shaswati Saha, Md. Osman Gani, Tim Oates, David Chapman:
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget. CoRR abs/2302.06134 (2023) - [i40]Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt:
Recasting Self-Attention with Holographic Reduced Representations. CoRR abs/2305.19534 (2023) - [i39]Corey J. Nolet, Divye Gala, Alexandre Fender, Mahesh Doijade, Joe Eaton, Edward Raff, John Zedlewski, Brad Rees, Tim Oates:
cuSLINK: Single-linkage Agglomerative Clustering on the GPU. CoRR abs/2306.16354 (2023) - [i38]Bharat Prakash, Tim Oates, Tinoosh Mohsenin:
LLM Augmented Hierarchical Agents. CoRR abs/2311.05596 (2023) - [i37]Mohammad Mahmudul Alam, Edward Raff, Tim Oates, Cynthia Matuszek:
DDxT: Deep Generative Transformer Models for Differential Diagnosis. CoRR abs/2312.01242 (2023) - [i36]Mohammad Mahmudul Alam, Edward Raff, Tim Oates:
Towards Generalization in Subitizing with Neuro-Symbolic Loss using Holographic Reduced Representations. CoRR abs/2312.15310 (2023) - 2022
- [j24]Aidin Shiri
, Uttej Kallakuri, Hasib-Al Rashid, Bharat Prakash, Nicholas R. Waytowich, Tim Oates, Tinoosh Mohsenin:
E2HRL: An Energy-efficient Hardware Accelerator for Hierarchical Deep Reinforcement Learning. ACM Trans. Design Autom. Electr. Syst. 27(5): 45:1-45:19 (2022) - [c149]Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz, Edward Raff, Tim Oates, James Holt:
Lempel-Ziv Networks. ICBINB 2022: 1-11 - [c148]Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt:
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations. ICML 2022: 367-393 - [c147]Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, Tim Oates:
GPU Semiring Primitives for Sparse Neighborhood Methods. MLSys 2022 - [i35]Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt:
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations. CoRR abs/2206.05893 (2022) - [i34]Bharat Prakash, Nicholas R. Waytowich, Tim Oates, Tinoosh Mohsenin:
Towards an Interpretable Hierarchical Agent Framework using Semantic Goals. CoRR abs/2210.08412 (2022) - [i33]Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz, Edward Raff, Tim Oates, James Holt:
Lempel-Ziv Networks. CoRR abs/2211.13250 (2022) - [i32]Khondoker Murad Hossain, Tim Oates:
Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks. CoRR abs/2212.08121 (2022) - 2021
- [c146]Corey J. Nolet, Victor Lafargue, Edward Raff, Thejaswi Nanditale, Tim Oates, John Zedlewski, Joshua Patterson:
Bringing UMAP Closer to the Speed of Light with GPU Acceleration. AAAI 2021: 418-426 - [c145]Ashwinkumar Ganesan, Francis Ferraro, Tim Oates:
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment. ACL/IJCNLP (Findings) 2021: 3132-3139 - [c144]Akshay Peshave, Ashwinkumar Ganesan, Tim Oates:
Predicting Network Threat Events Using HMM Ensembles. ADMA 2021: 229-240 - [c143]Aidin Shiri, Bharat Prakash, Arnab Neelim Mazumder, Nicholas R. Waytowich, Tim Oates, Tinoosh Mohsenin:
An Energy-Efficient Hardware Accelerator for Hierarchical Deep Reinforcement Learning. AICAS 2021: 1-4 - [c142]Sourav Mukherjee, J. J. Ben-Joseph, Marcelo Campos, Prashan Malla, Hieu Nguyen, Anh Pham, Tim Oates, Vasudevan Janarthanan:
Predicting Physiological Effects of Chemical Substances Using Natural Language Processing. CCECE 2021: 1-6 - [c141]Sourav Mukherjee, David Widmark, Vince DiMascio, Tim Oates:
Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions. ICDM (Workshops) 2021: 647-652 - [c140]Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean:
Learning with Holographic Reduced Representations. NeurIPS 2021: 25606-25620 - [i31]Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, John Zedlewski, Tim Oates:
Semiring Primitives for Sparse Neighborhood Methods on the GPU. CoRR abs/2104.06357 (2021) - [i30]Ashwinkumar Ganesan, Francis Ferraro, Tim Oates:
Learning a Reversible Embedding Mapping using Bi-Directional Manifold Alignment. CoRR abs/2107.00124 (2021) - [i29]Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean:
Learning with Holographic Reduced Representations. CoRR abs/2109.02157 (2021) - [i28]Sourav Mukherjee, David Widmark, Vince DiMascio, Tim Oates:
Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions. CoRR abs/2110.00078 (2021) - [i27]Bharat Prakash, Nicholas R. Waytowich, Tim Oates, Tinoosh Mohsenin:
Interactive Hierarchical Guidance using Language. CoRR abs/2110.04649 (2021) - [i26]Bharat Prakash, Nicholas R. Waytowich, Tinoosh Mohsenin, Tim Oates:
Automatic Goal Generation using Dynamical Distance Learning. CoRR abs/2111.04120 (2021) - 2020
- [c139]Bharat Prakash, Nicholas R. Waytowich, Ashwinkumar Ganesan, Tim Oates, Tinoosh Mohsenin:
Guiding Safe Reinforcement Learning Policies Using Structured Language Constraints. SafeAI@AAAI 2020: 153-161 - [c138]Sourav Mukherjee, Tim Oates, Vince DiMascio, Huguens Jean, Rob Ares, David Widmark, Jaclyn Harder:
Immigration Document Classification and Automated Response Generation. ICDM (Workshops) 2020: 782-789 - [i25]Ashwinkumar Ganesan, Frank Ferraro, Tim Oates:
Locality Preserving Loss to Align Vector Spaces. CoRR abs/2004.03734 (2020) - [i24]Corey J. Nolet, Victor Lafargue, Edward Raff, Thejaswi Nanditale, Tim Oates, John Zedlewski, Joshua Patterson:
Bringing UMAP Closer to the Speed of Light with GPU Acceleration. CoRR abs/2008.00325 (2020) - [i23]Sourav Mukherjee, Tim Oates, Vince DiMascio, Huguens Jean, Rob Ares, David Widmark, Jaclyn Harder:
Immigration Document Classification and Automated Response Generation. CoRR abs/2010.01997 (2020)
2010 – 2019
- 2019
- [j23]Ali Jafari, Ashwinkumar Ganesan, Chetan Sai Kumar Thalisetty, Varun Sivasubramanian, Tim Oates, Tinoosh Mohsenin:
SensorNet: A Scalable and Low-Power Deep Convolutional Neural Network for Multimodal Data Classification. IEEE Trans. Circuits Syst. I Regul. Pap. 66-I(1): 274-287 (2019) - [c137]Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas R. Waytowich:
Learning Behaviors from a Single Video Demonstration Using Human Feedback. AAMAS 2019: 1970-1972 - [c136]Bharat Prakash, Mark Horton, Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin:
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning. ACM Great Lakes Symposium on VLSI 2019: 507-512 - [c135]J. T. Turner, Michael W. Floyd, Kalyan Moy Gupta, Tim Oates:
NOD-CC: A Hybrid CBR-CNN Architecture for Novel Object Discovery. ICCBR 2019: 373-387 - [c134]Neil Bell, Brian Seipp, Tim Oates, Cynthia Matuszek:
Inferring Robot Morphology from Observation of Unscripted Movement. ICRA 2019: 9544-9551 - [c133]Komal Sharan, Ashwinkumar Ganesan, Tim Oates:
Improving Visual Reasoning with Attention Alignment. ISVC (1) 2019: 219-230 - [i22]Bharat Prakash, Mark Horton, Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin:
On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning. CoRR abs/1903.10404 (2019) - [i21]Ashwinkumar Ganesan, Pooja Parameshwarappa, Akshay Peshave, Zhiyuan Chen, Tim Oates:
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning. CoRR abs/1903.12101 (2019) - [i20]Isaac Mativo, Yelena Yesha, Michael A. Grasso, Tim Oates, Qian Zhu:
Hybrid Mortality Prediction using Multiple Source Systems. CoRR abs/1905.00752 (2019) - [i19]Sourav Mukherjee, Tim Oates, Ryan Wright:
Graph Node Embeddings using Domain-Aware Biased Random Walks. CoRR abs/1908.02947 (2019) - [i18]Chi Zhang, Bryan Wilkinson, Ashwinkumar Ganesan, Tim Oates:
Determining the Scale of Impact from Denial-of-Service Attacks in Real Time Using Twitter. CoRR abs/1909.05890 (2019) - [i17]Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, Nicholas R. Waytowich:
Learning from Observations Using a Single Video Demonstration and Human Feedback. CoRR abs/1909.13392 (2019) - [i16]Hang Gao, Tim Oates:
Universal Adversarial Perturbation for Text Classification. CoRR abs/1910.04618 (2019) - [i15]Karan K. Budhraja, Hang Gao, Tim Oates:
Using Neural Networks for Programming by Demonstration. CoRR abs/1910.04724 (2019) - 2018
- [j22]Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein:
GrammarViz 3.0: Interactive Discovery of Variable-Length Time Series Patterns. ACM Trans. Knowl. Discov. Data 12(1): 10:1-10:28 (2018) - [c132]Sandeep Nair Narayanan, Ashwinkumar Ganesan, Karuna P. Joshi, Tim Oates, Anupam Joshi, Tim Finin:
Early Detection of Cybersecurity Threats Using Collaborative Cognition. CIC 2018: 354-363 - [c131]Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, W. David Hairston:
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks. PAKDD (3) 2018: 285-296 - [c130]Hang Gao, Tim Oates:
On Finer Control of Information Flow in LSTMs. ECML/PKDD (1) 2018: 527-540 - [c129]Brian Seipp, Karan Kumar Budhraja, Tim Oates:
Optimizing Transitions between Abstract ABM Demonstrations. SASO 2018: 100-109 - [c128]Karan Kumar Budhraja, Tim Oates:
Improved Reverse Mapping for Controlling Swarms by Visual Demonstration. FAS*W@SASO/ICAC 2018: 130-135 - [c127]Karan Kumar Budhraja, Tim Oates:
Implementing Feedback for Programming by Demonstration. SASO 2018: 162-167 - [c126]Hang Gao, Tim Oates:
Large Scale Taxonomy Classification using BiLSTM with Self-Attention. eCOM@SIGIR 2018 - [i14]Sandeep Nair Narayanan, Ashwinkumar Ganesan, Karuna P. Joshi, Tim Oates, Anupam Joshi, Tim Finin:
Cognitive Techniques for Early Detection of Cybersecurity Events. CoRR abs/1808.00116 (2018) - 2017
- [j21]Karan K. Budhraja, John Winder, Tim Oates:
Feature Construction for Controlling Swarms by Visual Demonstration. ACM Trans. Auton. Adapt. Syst. 12(2): 10:1-10:22 (2017) - [c125]Karan K. Budhraja, Tim Oates:
Neuroevolution-based Inverse Reinforcement Learning. CEC 2017: 67-76 - [c124]Kavita Krishnaswamy, Srinivas Moorthy, Tim Oates:
Preliminary Survey Analysis in Participatory Design: Repositioning, Transferring, and Personal Care Robots. HRI (Companion) 2017: 171-172 - [c123]Karan Kumar Budhraja, Tim Oates:
Dataset Selection for Controlling Swarms by Visual Demonstration. ICDM Workshops 2017: 932-941 - [c122]Neha Tilak, Sunil Gandhi, Tim Oates:
Visual entity linking. IJCNN 2017: 665-672 - [c121]Zhiguang Wang, Weizhong Yan, Tim Oates:
Time series classification from scratch with deep neural networks: A strong baseline. IJCNN 2017: 1578-1585 - [c120]Mandar Haldekar, Ashwinkumar Ganesan, Tim Oates:
Identifying spatial relations in images using convolutional neural networks. IJCNN 2017: 3593-3600 - [c119]Arjun Kumar, Tim Oates:
Connecting deep neural networks with symbolic knowledge. IJCNN 2017: 3601-3608 - [c118]Ali Jafari, Sunil Gandhi, Sri Harsha Konuru, W. David Hairston, Tim Oates, Tinoosh Mohsenin:
An EEG artifact identification embedded system using ICA and multi-instance learning. ISCAS 2017: 1-4 - [c117]Kavita Krishnaswamy, Srinivas Moorthy, Tim Oates:
Survey Data Analysis for Repositioning, Transferring, and Personal Care Robots. PETRA 2017: 45-51 - [c116]Crystal Chen, Arnold P. Boedihardjo, Brian S. Jenkins, Charlotte L. Ellison, Jessica Lin, Pavel Senin, Tim Oates:
STAVIS 2.0: Mining Spatial Trajectories via Motifs. SSTD 2017: 433-439 - [i13]Mandar Haldekar, Ashwinkumar Ganesan, Tim Oates:
Identifying Spatial Relations in Images using Convolutional Neural Networks. CoRR abs/1706.04215 (2017) - [i12]Prutha Date, Ashwinkumar Ganesan, Tim Oates:
Fashioning with Networks: Neural Style Transfer to Design Clothes. CoRR abs/1707.09899 (2017) - [i11]J. T. Turner, Adam Page, Tinoosh Mohsenin, Tim Oates:
Deep Belief Networks used on High Resolution Multichannel Electroencephalography Data for Seizure Detection. CoRR abs/1708.08430 (2017) - [i10]Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi:
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning. CoRR abs/1709.04305 (2017) - 2016
- [j20]Rémi Eyraud, Jean-Christophe Janodet, Tim Oates, Frédéric Papadopoulos:
Designing and Learning Substitutable Plane Graph Grammars. Fundam. Informaticae 146(4): 403-430 (2016) - [j19]Kavita Krishnaswamy, Ravi Kuber, Tim Oates:
Developing a limb repositioning robotic interface for persons with severe physical disabilities. Univers. Access Inf. Soc. 15(4): 609-627 (2016) - [c115]Zhiguang Wang, Tim Oates, James Lo:
Adaptive Normalized Risk-Averting Training for Deep Neural Networks. AAAI 2016: 2201-2207 - [c114]Xing Wang, Jessica Lin, Pavel Senin, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein:
RPM: Representative Pattern Mining for Efficient Time Series Classification. EDBT 2016: 185-196 - [c113]Nicholay Topin, Karan K. Budhraja, Tim Oates:
Feature Selection in Environments with Limited Voluntary Information Sharing. ICDM Workshops 2016: 576-583 - [c112]Bhavani Thuraisingham, Murat Kantarcioglu, Kevin W. Hamlen, Latifur Khan, Tim Finin, Anupam Joshi, Tim Oates, Elisa Bertino:
A Data Driven Approach for the Science of Cyber Security: Challenges and Directions. IRI 2016: 1-10 - [c111]Bryan Wilkinson, Tim Oates:
A Gold Standard for Scalar Adjectives. LREC 2016 - [c110]Karan K. Budhraja, Tim Oates:
Controlling Swarms by Visual Demonstration. SASO 2016: 1-10 - [c109]Hang Gao, Tim Oates:
MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification. SemEval@NAACL-HLT 2016: 139-144 - [i9]Karan Kumar Budhraja, Tim Oates:
Neuroevolution-Based Inverse Reinforcement Learning. CoRR abs/1608.02971 (2016) - [i8]Ashwinkumar Ganesan, Tim Oates, Matt Schmill:
Finding Representative Points in Multivariate Data Using PCA. CoRR abs/1610.05819 (2016) - [i7]Zhiguang Wang, Weizhong Yan, Tim Oates:
Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline. CoRR abs/1611.06455 (2016) - 2015
- [j18]Adam Page, Chris Sagedy, Emily Smith, Nasrin Attaran, Tim Oates, Tinoosh Mohsenin:
A Flexible Multichannel EEG Feature Extractor and Classifier for Seizure Detection. IEEE Trans. Circuits Syst. II Express Briefs 62-II(2): 109-113 (2015) - [c108]Sunil Gandhi, Tim Oates, Arnold P. Boedihardjo, Crystal Chen, Jessica Lin, Pavel Senin, Susan Frankenstein, Xing Wang:
A Generative Model For Time Series Discretization Based On Multiple Normal Distributions. PIKM@CIKM 2015: 19-25 - [c107]Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein:
Time series anomaly discovery with grammar-based compression. EDBT 2015: 481-492 - [c106]Adam Page, Siddharth Pramod, Tim Oates, Tinoosh Mohsenin:
An ultra low power feature extraction and classification system for wearable seizure detection. EMBC 2015: 7111-7114 - [c105]Zhiguang Wang, Tim Oates:
Pooling SAX-BoP Approaches with Boosting to Classify Multivariate Synchronous Physiological Time Series Data. FLAIRS 2015: 335-341 - [c104]Karan Kumar Budhraja, Tim Oates:
Adversarial Feature Selection. ICDM Workshops 2015: 288-294 - [c103]Zhiguang Wang, Tim Oates:
Imaging Time-Series to Improve Classification and Imputation. IJCAI 2015: 3939-3945 - [i6]Zhiguang Wang, Tim Oates:
Imaging Time-Series to Improve Classification and Imputation. CoRR abs/1506.00327 (2015) - [i5]Zhiguang Wang, Tim Oates, James Lo:
Adaptive Normalized Risk-Averting Training For Deep Neural Networks. CoRR abs/1506.02690 (2015) - [i4]Zhiguang Wang, Tim Oates:
Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks. CoRR abs/1509.07481 (2015) - 2014
- [j17]Xianshu Zhu, Tim Oates:
Finding story chains in newswire articles using random walks. Inf. Syst. Frontiers 16(5): 753-769 (2014) - [j16]Jeffrey Heinz, Colin de la Higuera, Tim Oates:
Introduction to the Special Issue on Grammatical Inference. Mach. Learn. 96(1-2): 1-3 (2014) - [c102]J. T. Turner, Adam Page, Tinoosh Mohsenin, Tim Oates:
Deep Belief Networks Used on High Resolution Multichannel Electroencephalography Data for Seizure Detection. AAAI Spring Symposia 2014 - [c101]Adam Page, J. T. Turner, Tinoosh Mohsenin, Tim Oates:
Comparing Raw Data and Feature Extraction for Seizure Detection with Deep Learning Methods. FLAIRS 2014 - [c100]Terry H. Tsai, Niels Kasch, Craig Pfeifer, Tim Oates:
Text Mining for Hypotheses and Results in Translational Medicine Studies. ICDM Workshops 2014: 127-132 - [c99]Rakesh Deivachilai, Tim Oates:
On-Line Signature Verification Using Symbolic Aggregate Approximation (SAX) and Sequential Mining Optimization (SMO). ICMLA 2014: 195-200 - [c98]Zhiguang Wang, Tim Oates:
Time Warping Symbolic Aggregation Approximation with Bag-of-Patterns Representation for Time Series Classification. ICMLA 2014: 270-275 - [c97]Pavel Senin, Jessica Lin, Xing Wang, Tim Oates, Sunil Gandhi, Arnold P. Boedihardjo, Crystal Chen, Susan Frankenstein, Manfred Lerner:
GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series. ECML/PKDD (3) 2014: 468-472 - 2013
- [c96]Tim Oates, Arnold P. Boedihardjo, Jessica Lin, Crystal Chen, Susan Frankenstein, Sunil Gandhi:
Motif discovery in spatial trajectories using grammar inference. CIKM 2013: 1465-1468 - [c95]Adrian Rosebrock, Tim Oates, Jesus J. Caban:
Ecosembles: A Rapidly Deployable Image Classification System Using Feature-Views. ICMLA (1) 2013: 1-8 - [c94]Tongchun Du, Michael T. Cox, Don Perlis, Jared Shamwell, Tim Oates:
From Robots to Reinforcement Learning. ICTAI 2013: 540-545 - [c93]Paul McNamee, James Mayfield, Tim Finin, Tim Oates, Dawn J. Lawrie, Tan Xu, Douglas W. Oard:
KELVIN: a tool for automated knowledge base construction. HLT-NAACL 2013: 32-35 - [c92]Xianshu Zhu, Tim Oates:
Finding news story chains based on multi-dimensional event profiles. OAIR 2013: 157-164 - [i3]Sarah Finney, Natalia Gardiol, Leslie Pack Kaelbling, Tim Oates:
The Thing That We Tried Didn't Work Very Well : Deictic Representation in Reinforcement Learning. CoRR abs/1301.0567 (2013) - [i2]Nicholas R. Magliocca, Erle C. Ellis, Tim Oates, Matthew D. Schmill:
Contextualizing the global relevance of local land change observations. CoRR abs/1307.6889 (2013) - 2012
- [j15]Patricia Ordóñez, Tim Oates, Michael Lombardi, Genaro Hernández, Kathryn W. Holmes, Jim Fackler, Christoph U. Lehmann:
Visualization of multivariate time-series data in a neonatal ICU. IBM J. Res. Dev. 56(5): 7 (2012) - [c91]Niyati Chhaya, Tim Oates:
Joint inference of soft biometric features. ICB 2012: 466-471 - [c90]Preeti Bhargava, Michael T. Cox, Tim Oates, Uran Oh, Matthew Paisner, Donald Perlis, Jared Shamwell:
The robot baby and massive metacognition: Future vision. ICDL-EPIROB 2012: 1-2 - [c89]Jared Shamwell, Tim Oates, Preeti Bhargava, Michael T. Cox, Uran Oh, Matthew Paisner, Donald Perlis:
The robot baby and massive metacognition: Early steps via growing neural gas. ICDL-EPIROB 2012: 1-2 - [c88]Tim Oates, Colin F. Mackenzie, Lynn G. Stansbury, Bizhan Aarabi, Deborah M. Stein, Peter Fu-Ming Hu:
Predicting Patient Outcomes from a Few Hours of High Resolution Vital Signs Data. ICMLA (2) 2012: 192-197 - [c87]Tim Oates, Colin F. Mackenzie, Deborah M. Stein, Lynn G. Stansbury, Joseph Dubose, Bizhan Aarabi, Peter Fu-Ming Hu:
Exploiting Representational Diversity for Time Series Classification. ICMLA (2) 2012: 538-544 - [c86]Niels Kasch, Tim Oates:
Commonsense Knowledge in Weblogs: Mining Script-Like Structures from the Web. ICDM (Poster and Industry Proceedings) 2012: 1-15 - [c85]Niels Kasch, Tim Oates:
Commonsense Knowledge in Weblogs: Mining Script-Like Structures from the Web. ICDM (Workshops) 2012: 31-45 - [c84]Xianshu Zhu, Tim Oates:
Finding story chains in newswire articles. IRI 2012: 93-100 - [c83]Veselin Stoyanov, James Mayfield, Tan Xu, Douglas W. Oard, Dawn J. Lawrie, Tim Oates, Tim Finin:
A Context-Aware Approach to Entity Linking. AKBC-WEKEX@NAACL-HLT 2012: 62-67 - [c82]Yuan Li, Jessica Lin, Tim Oates:
Visualizing Variable-Length Time Series Motifs. SDM 2012: 895-906 - [c81]Jeffrey Heinz, Colin de la Higuera, Tim Oates:
Preface. ICGI 2012: 1-3 - [c80]Rémi Eyraud, Jean-Christophe Janodet, Tim Oates:
Learning Substitutable Binary Plane Graph Grammars. ICGI 2012: 114-128 - [e3]Jeffrey Heinz, Colin de la Higuera, Tim Oates:
Proceedings of the Eleventh International Conference on Grammatical Inference, ICGI 2012, University of Maryland, College Park, USA, September 5-8, 2012. JMLR Proceedings 21, JMLR.org 2012 [contents] - [i1]Paul McNamee, Veselin Stoyanov, James Mayfield, Tim Finin, Tim Oates, Tan Xu, Douglas W. Oard, Dawn J. Lawrie:
HLTCOE Participation at TAC 2012: Entity Linking and Cold Start Knowledge Base Construction. TAC 2012 - 2011
- [j14]Soumi Ray, Tim Oates:
Discovering and Characterizing Hidden Variables Using a Novel Neural Network Architecture: LO-Net. J. Robotics 2011: 193146:1-193146:16 (2011) - [c79]Michael T. Cox, Tim Oates, Donald Perlis:
Toward an Integrated Metacognitive Architecture. AAAI Fall Symposium: Advances in Cognitive Systems 2011 - [c78]Todd W. Neller, Marie desJardins, Tim Oates, Matthew E. Taylor:
Model AI Assignments 2011. EAAI 2011: 1746 - [c77]Tom Armstrong, Tim Oates:
An Architecture for Bootstrapping Lexical Semantics and Grammatical Structures. Web Intelligence/IAT Workshops 2011: 155-158 - [c76]Patricia Ordóñez, Tom Armstrong, Tim Oates, Jim Fackler:
Using Modified Multivariate Bag-of-Words Models to Classify Physiological Data. ICDM Workshops 2011: 534-539 - [c75]Patricia Ordóñez, Tom Armstrong, Tim Oates, Jim Fackler:
Classification of Patients Using Novel Multivariate Time Series Representations of Physiological Data. ICMLA (2) 2011: 172-179 - [c74]Soumi Ray, Tim Oates:
Improving the Discovery and Characterization of Hidden Variables by Regularizing the LO-net. ICMLA (1) 2011: 442-447 - [c73]Matthew D. Schmill, Tim Oates:
Managing Uncertainty in Text-to-Sketch Tracking Problems. ICTAI 2011: 430-437 - [c72]Delip Rao, Michael J. Paul, Clayton Fink, David Yarowsky, Timothy Oates, Glen Coppersmith:
Hierarchical Bayesian Models for Latent Attribute Detection in Social Media. ICWSM 2011 - [c71]Kavita Krishnaswamy, Jennifer Sleeman, Tim Oates:
Real-time path planning for a robotic arm. PETRA 2011: 11 - [c70]Niyati Chhaya, Tim Oates:
Robust Face Detection in Patient Triage Images. VISAPP 2011: 326-333 - [p1]Matthew D. Schmill, Michael L. Anderson, Scott Fults, Darsana P. Josyula, Tim Oates, Don Perlis, Hamid Haidarian Shahri, Shomir Wilson, Dean Wright:
The Metacognitive Loop and Reasoning about Anomalies. Metareasoning 2011: 183-198 - 2010
- [c69]Darsana P. Josyula, Bette J. Donahue, Matthew McCaslin, Michelle Snowden, Michael L. Anderson, Tim Oates, Matthew D. Schmill, Donald Perlis:
Metacognition for Detecting and Resolving Conflicts in Operational Policies. Metacognition for Robust Social Systems 2010 - [c68]Hamid Haidarian Shahri, Wikum Dinalankara, Scott Fults, Shomir Wilson, Donald Perlis, Matt Schmill, Tim Oates, Darsana P. Josyula, Michael L. Anderson:
The Metacognitive Loop: An Architecture for Building Robust Intelligent Systems. AAAI Fall Symposium: Commonsense Knowledge 2010 - [c67]Mark Dredze, Tim Oates, Christine D. Piatko:
We're Not in Kansas Anymore: Detecting Domain Changes in Streams. EMNLP 2010: 585-595 - [c66]Joshua Jones, Tim Oates:
Learning Deterministic Finite Automata from Interleaved Strings. ICGI 2010: 80-93 - [c65]Soumi Ray, Tim Oates:
Discovering and Characterizing Hidden Variables in Streaming Multivariate Time Series. ICMLA 2010: 913-916 - [c64]Namita Sapre, Mohamed F. Younis, Tim Oates:
Energy efficient node engagement strategies for achieving data fidelity in wireless sensor networks. LCN 2010: 723-729 - [c63]Sourav Mukherjee, Tim Oates:
Inferring Probability Distributions of Graph Size and Node Degree from Stochastic Graph Grammars. SDM 2010: 490-501
2000 – 2009
- 2009
- [j13]Jie Bao, Uldis Bojars, Tanzeem Choudhury, Li Ding, Mark Greaves, Ashish Kapoor, Sandy Louchart, Manish Mehta, Bernhard Nebel, Sergei Nirenburg, Tim Oates, David L. Roberts, Antonio Sanfilippo, Nenad Stojanovic, Kristen Stubbs, Andrea Lockerd Thomaz, Katherine M. Tsui, Stefan Wölfl:
Reports of the AAAI 2009 Spring Symposia. AI Mag. 30(3): 89-95 (2009) - [c62]Sergei Nirenburg, Tim Oates:
Organizing Committee. AAAI Spring Symposium: Learning by Reading and Learning to Read 2009 - [c61]Sergei Nirenburg, Tim Oates:
Preface. AAAI Spring Symposium: Learning by Reading and Learning to Read 2009 - [c60]Deepak Chinavle, Pranam Kolari, Tim Oates, Tim Finin:
Ensembles in adversarial classification for spam. CIKM 2009: 2015-2018 - [c59]Fusun Yaman, Tim Oates, Mark H. Burstein:
A Context Driven Approach for Workflow Mining. IJCAI 2009: 1798-1803 - 2008
- [j12]Colin de la Higuera, Tim Oates, Menno van Zaanen:
Introduction: Special Issue on Applications of Grammatical Inference. Appl. Artif. Intell. 22(1&2): 1-3 (2008) - [j11]Jerry T. Ball, Chris Arney, Samuel G. Collins, Mitchell Marcus, Sergei Nirenburg, Antonio Chella, Kai Goebel, Jason H. Li, Margaret Lyell, Brian Magerko, Riccardo Manzotti, Clayton T. Morrison, Tim Oates, Mark O. Riedl, Goran Trajkovski, Walt Truszkowski, N. Serdar Uckun:
AAAI Fall Symposium Reports. AI Mag. 29(1): 99-104 (2008) - [j10]Michael L. Anderson, Scott Fults, Darsana P. Josyula, Tim Oates, Donald Perlis, Shomir Wilson, Dean Wright:
A Self-Help Guide For Autonomous Systems. AI Mag. 29(2): 67-73 (2008) - [j9]Tim Oates:
A Too-Clever Ranking Method. AI Mag. 29(2): 74-76 (2008) - [c58]Tom Armstrong, Tim Oates:
Lexical and Grammatical Inference. AAAI 2008: 1772-1773 - [c57]Marc Pickett, Don Miner, Tim Oates:
Essential Phenomena of General Intelligence. AGI 2008: 268-274 - [c56]Tom Armstrong, Tim Oates:
Learning in the Lexical-Grammatical Interface. FLAIRS 2008: 23-28 - [c55]Tom Armstrong, Tim Oates:
Which Came First, the Grammar or the Lexicon?. ICGI 2008: 283-285 - [c54]Sourav Mukherjee, Tim Oates:
Estimating Graph Parameters Using Graph Grammars. ICGI 2008: 292-294 - 2007
- [j8]Michael L. Anderson, Tim Oates:
A Review of Recent Research in Metareasoning and Metalearning. AI Mag. 28(1): 12-16 (2007) - [c53]Beenish Bhatia, Tim Oates, Yan Xiao, Peter Fu-Ming Hu:
Real-Time Identification of Operating Room State from Video. AAAI 2007: 1761-1766 - [c52]Tom Armstrong, Tim Oates:
UNDERTOW: Multi-Level Segmentation of Real-Valued Time Series. AAAI 2007: 1842-1843 - [c51]Marc Pickett, Tim Oates:
The Marchitecture: A Cognitive Architecture for a Robot Baby. AAAI 2007: 1896-1897 - [c50]Michael L. Anderson, Matthew D. Schmill, Tim Oates, Donald Perlis, Darsana P. Josyula, Dean Wright, Shomir Wilson:
Toward Domain-Neutral Human-Level Metacognition. AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning 2007: 1-6 - [c49]Akshay Java, Pranam Kolari, Tim Finin, Anupam Joshi, Tim Oates:
Feeds That Matter: A Study of Bloglines Subscriptions. ICWSM 2007 - [c48]Sandor Dornbush, Anupam Joshi, Zary Segall, Tim Oates:
A Human Activity Aware Learning Mobile Music Player. AITamI@IJCAI (best papers) 2007: 107-122 - [c47]Aarti Gupta, Tim Oates:
Using Ontologies and the Web to Learn Lexical Semantics. IJCAI 2007: 1618-1623 - [c46]Sergei Nirenburg, Tim Oates, Jesse English:
Learning by Reading by Learning to Read. ICSC 2007: 694-701 - [e2]Clayton T. Morrison, Tim Oates:
Computational Approaches to Representation Change during Learning and Development, Papers from the 2007 AAAI Fall Symposium, Arlington, Virginia, USA, November 9-11, 2007. AAAI Technical Report FS-07-03, AAAI Press 2007 [contents] - 2006
- [j7]Tim Oates, Waiyian Chong:
Marcus Hutter, Universal Artificial Intelligence, Springer (2004). Artif. Intell. 170(18): 1222-1226 (2006) - [j6]Michael L. Anderson, Tim Oates, Waiyian Chong, Donald Perlis:
The metacognitive loop I: Enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance||. J. Exp. Theor. Artif. Intell. 18(3): 387-411 (2006) - [c45]Pranam Kolari, Akshay Java, Tim Finin, Tim Oates, Anupam Joshi:
Detecting Spam Blogs: A Machine Learning Approach. AAAI 2006: 1351-1356 - [c44]Jim Blythe, Mithila Patwardhan, Tim Oates, Marie desJardins, Penny Rheingans:
Visualization Support for Fusing Relational, Spatio-Temporal Data: Building Career Histories. FUSION 2006: 1-7 - [c43]Tim Oates, Tom Armstrong, Leonor Becerra-Bonache, Mike Atamas:
Inferring Grammars for Mildly Context Sensitive Languages in Polynomial-Time. ICGI 2006: 137-147 - [c42]Joe Catalano, Tom Armstrong, Tim Oates:
Discovering Patterns in Real-Valued Time Series. PKDD 2006: 462-469 - 2005
- [j5]Tim Oates:
R. Siegwart and I. Nourbakhsh, Introduction to Autonomous Mobile Robots, MIT Press (2004). Artif. Intell. 169(2): 146-149 (2005) - [j4]Michael L. Anderson, Thomas Barkowsky, Pauline Berry, Douglas S. Blank, Timothy Chklovski, Pedro M. Domingos, Marek J. Druzdzel, Christian Freksa, John Gersh, Mary Hegarty, Tze-Yun Leong, Henry Lieberman, Ric K. Lowe, Susann LuperFoy, Rada Mihalcea, Lisa Meeden, David P. Miller, Tim Oates, Robert L. Popp, Daniel G. Shapiro, Nathan Schurr, Push Singh, John Yen:
Reports on the 2005 AAAI Spring Symposium Series. AI Mag. 26(2): 87-92 (2005) - [c41]Thomas Briggs, Tim Oates:
Discovering Domain-Specific Composite Kernels. AAAI 2005: 732-738 - [c40]William Krueger, Tim Oates, Tom Armstrong, Paul R. Cohen, Carole R. Beal:
Transfer in Learning by Doing. IJCAI 2005: 1618-1619 - [c39]Marc Pickett, Tim Oates:
The Cruncher: Automatic Concept Formation Using Minimum Description Length. SARA 2005: 282-289 - [e1]Mike Anderson, Tim Oates:
2005 AAAI Spring Symposium on Metacognition in Computation, March 21-23 2005, Stanford, California, USA. AAAI Technical Report SS-05-04, AAAI Press 2005, ISBN 1-57735-230-0 [contents] - 2004
- [j3]Lola Cañamero, Zachary Dodds, Lloyd G. Greenwald, James P. Gunderson, Ayanna M. Howard, Eva Hudlicka, Cheryl Martin, Lynn Parker, Tim Oates, Terry R. Payne, Yan Qu, Craig Schlenoff, James G. Shanahan, Sheila Tejada, Jerry B. Weinberg, Janyce Wiebe:
The 2004 AAAI Spring Symposium Series. AI Mag. 25(4): 95-100 (2004) - [j2]Vinay Bhat, Tim Oates, Vishal Shanbhag, Charles K. Nicholas:
Finding aliases on the web using latent semantic analysis. Data Knowl. Eng. 49(2): 129-143 (2004) - [c38]Tim Oates, Tom Armstrong, Justin Harris, Mark Nejman:
On the Relationship between Lexical Semantics and Syntax for the Inference of Context-Free Grammars. AAAI 2004: 431-436 - [c37]Vivek Tawde, Tim Oates, Eric J. Glover:
Generating Web Graphs with Embedded Communities. WAW 2004: 80-91 - 2003
- [c36]William Krueger, Jonathan Nilsson, Tim Oates, Timothy W. Finin:
Automatically Generated DAML Markup for Semistructured Documents. AMKM 2003: 276-287 - [c35]Tim Oates, Tom Armstrong, Justin Harris, Mark Nejman:
Leveraging Lexical Semantics to Infer Context-Free Grammars. ECML Workshop on Learning Contex-Free Grammars 2003: 65-76 - [c34]Tim Oates, Shailesh Doshi, Fang Huang:
Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars. ILP 2003: 281-298 - 2002
- [c33]Paul R. Cohen, Tim Oates, Carole R. Beal, Niall M. Adams:
Contentful Mental States for Robot Baby. AAAI/IAAI 2002: 126-131 - [c32]Tim Oates:
PERUSE: An Unsupervised Algorithm for Finding Recurrig Patterns in Time Series. ICDM 2002: 330-337 - [c31]Tim Oates, Brent Heeringa:
Estimating Grammar Parameters Using Bounded Memory. ICGI 2002: 185-198 - [c30]Tim Oates, Devina Desai, Vinay Bhat:
Learning k-Reversible Context-Free Grammars from Positive Structural Examples. ICML 2002: 459-465 - [c29]Sarah Finney, Natalia Gardiol, Leslie Pack Kaelbling, Tim Oates:
The Thing that we Tried Didn't Work very Well: Deictic Representation in Reinforcement Learning. UAI 2002: 154-161 - [c28]Tim Oates, Vinay Bhat, Vishal Shanbhag:
Using latent semantic analysis to find different names for the same entity in free text. WIDM 2002: 31-35 - 2001
- [c27]Gary W. King, Tim Oates:
The Importance of Being Discrete: Learning Classes of Actions and Outcomes through Interaction. AI 2001: 236-245 - [c26]Paul R. Cohen, Tim Oates, Niall M. Adams, Carole R. Beal:
Robot Baby 2001. ALT 2001: 32-56 - [c25]Paul R. Cohen, Tim Oates, Niall M. Adams, Carole R. Beal:
Robot Baby 2001. Discovery Science 2001: 29 - [c24]Tim Oates, Laura Firoiu, Paul R. Cohen:
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series. Sequence Learning 2001: 35-52 - 2000
- [c23]Tim Oates, Matthew D. Schmill, Paul R. Cohen:
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments. AAAI/IAAI 2000: 846-851 - [c22]Tim Oates, Matthew D. Schmill, Paul R. Cohen:
Identifying qualitatively different outcomes of actions: gaining autonomy through learning. Agents 2000: 110-111 - [c21]Tim Oates, Zachary Eyler-Walker, Paul R. Cohen:
Toward natural language interfaces for robotic agents: grounding linguistic meaning in sensors. Agents 2000: 227-228 - [c20]Matthew D. Schmill, Tim Oates, Paul R. Cohen:
Learning Planning Operators in Real-World, Partially Observable Environments. AIPS 2000: 246-253
1990 – 1999
- 1999
- [c19]Tim Oates, David D. Jensen:
Toward a Theoretical Understanding of Why and When Decision Tree Pruning Algorithms Fail. AAAI/IAAI 1999: 372-378 - [c18]Tim Oates, Matthew D. Schmill, Paul R. Cohen, Casey Durfee:
Efficient mining of statistical dependencies. AISTATS 1999 - [c17]Matthew D. Schmill, Tim Oates, Paul R. Cohen:
Learned models for continuous planning. AISTATS 1999 - [c16]Tim Oates, Matthew D. Schmill, Paul R. Cohen:
Efficient Mining of Statistical Dependencies. IJCAI 1999: 794-799 - [c15]Foster J. Provost, David D. Jensen, Tim Oates:
Efficient Progressive Sampling. KDD 1999: 23-32 - [c14]Tim Oates:
Identifying Distinctive Subsequences in Multivariate Time Series by Clustering. KDD 1999: 322-326 - 1998
- [c13]Laura Firoiu, Tim Oates, Paul R. Cohen:
Learning Deterministic Finite Automaton with a Recurrent Neural Network. ICGI 1998: 90-101 - [c12]Tim Oates, David D. Jensen:
Large Datasets Lead to Overly Complex Models: An Explanation and a Solution. KDD 1998: 294-298 - 1997
- [j1]Tim Oates, M. V. Nagendra Prasad, Victor R. Lesser:
Cooperative information-gathering: a distributed problem-solving approach. IEE Proc. Softw. Eng. 144(1): 72-88 (1997) - [c11]Paul R. Cohen, Marc S. Atkin, Tim Oates, Carole R. Beal:
Neo: Learning Conceptual Knowledge by Sensorimotor Interaction with an Environment. Agents 1997: 170-177 - [c10]Tim Oates, Matthew D. Schmill, David D. Jensen, Paul R. Cohen:
A Family of Algorithms for Finding Temporal Structure in Data. AISTATS 1997: 371-378 - [c9]Tim Oates, David D. Jensen:
The Effects of Training Set Size on Decision Tree Complexity. AISTATS 1997: 379-390 - [c8]Tim Oates, Matthew D. Schmill, Paul R. Cohen:
Parallel and Distributed Search for Structure in Multivariate Time Series. ECML 1997: 191-198 - [c7]Tim Oates, David D. Jensen:
The Effects of Training Set Size on Decision Tree Complexity. ICML 1997: 254-262 - [c6]David D. Jensen, Tim Oates, Paul R. Cohen:
Building Simple Models: A Case Study with Decision Trees. IDA 1997: 211-222 - 1996
- [c5]Tim Oates, Paul R. Cohen:
Searching for Planning Operators with Context-Dependent and Probabilistic Effects. AAAI/IAAI, Vol. 1 1996: 863-868 - [c4]Tim Oates, Paul R. Cohen:
Searching for Structure in Multiple Streams of Data. ICML 1996: 346-354 - 1995
- [c3]Tim Oates, Matthew D. Schmill, Dawn E. Gregory, Paul R. Cohen:
Detecting Complex Dependencies in Categorical Data. AISTATS 1995: 185-195 - [c2]Matthew D. Schmill, Tim Oates, Paul R. Cohen:
Tools for detecting dependencies in AI systems. ICTAI 1995: 148-155 - 1994
- [c1]Tim Oates, Paul R. Cohen:
Toward a Plan Steering Agent: Experiments with Schedule Maintenance. AIPS 1994: 134-139
Coauthor Index
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