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ORCIDDaniel L. Silver
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- affiliation: Acadia University, Jodrey School of Computer Science
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2020 – today
- 2023
[c48]Daniel L. Silver, Tom M. Mitchell:
The Roles of Symbols in Neural-based AI: They are Not What You Think! NeSy 2023: 420-421- [p2]Daniel L. Silver, Tom M. Mitchell:
The Roles of Symbols in Neural-Based AI: They Are Not What You Think! Compendium of Neurosymbolic Artificial Intelligence 2023: 1-28 - [i6]Daniel L. Silver, Rinda Digamarthi:
Forecasting COVID-19 Case Counts Based on 2020 Ontario Data. CoRR abs/2303.10294 (2023) - [i5]Daniel L. Silver, Tom M. Mitchell:
The Roles of Symbols in Neural-based AI: They are Not What You Think! CoRR abs/2304.13626 (2023) - 2022
- [c47]Meetkumar Patel, Daniel L. Silver:
Vehicle Traffic Estimation Using Deep Learning. FLAIRS 2022 - 2021
- [c46]Tao Yang, Daniel L. Silver:
The Disadvantage of CNN versus DBN Image Classification Under Adversarial Conditions. Canadian AI 2021 - [c45]Daniel L. Silver, Ahmed Galila:
Learning Arithmetic from Handwritten Images with the Aid of Symbols. NeSy 2021: 154-164 - 2020
- [c44]Daniel L. Silver, Sazia Mahfuz:
Generating Accurate Pseudo Examples for Continual Learning. CVPR Workshops 2020: 1035-1042 - [i4]Daniel L. Silver, Jabun Nasa:
Estimating Grape Yield on the Vine from Multiple Images. CoRR abs/2004.04278 (2020)
2010 – 2019
- 2019
- [c43]Daniel L. Silver, Tanya Monga:
In Vino Veritas: Estimating Vineyard Grape Yield from Images Using Deep Learning. Canadian AI 2019: 212-224 - [i3]Hugo Jair Escalante, Wei-Wei Tu, Isabelle Guyon, Daniel L. Silver, Evelyne Viegas, Yuqiang Chen, Wenyuan Dai, Qiang Yang:
AutoML @ NeurIPS 2018 challenge: Design and Results. CoRR abs/1903.05263 (2019) - 2017
- [c42]Xiang Jiang, Erico N. de Souza, Ahmad Pesaranghader, Baifan Hu, Daniel L. Silver, Stan Matwin:
TrajectoryNet: an embedded GPS trajectory representation for point-based classification using recurrent neural networks. CASCON 2017: 192-200 - [c41]Xiang Jiang, Erico N. de Souza, Xuan Liu, Behrouz Haji Soleimani, Xiaoguang Wang, Daniel L. Silver, Stan Matwin:
Partition-wise Recurrent Neural Networks for Point-based AIS Trajectory Classification. ESANN 2017 - [c40]Xiang Jiang, Xuan Liu, Erico N. de Souza, Baifan Hu, Daniel L. Silver, Stan Matwin
:
Improving point-based AIS trajectory classification with partition-wise gated recurrent units. IJCNN 2017: 4044-4051 - [i2]Xiang Jiang, Erico N. de Souza, Ahmad Pesaranghader, Baifan Hu, Daniel L. Silver, Stan Matwin:
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks. CoRR abs/1705.02636 (2017) - 2016
- [c39]Xiang Jiang, Daniel L. Silver, Baifan Hu, Erico N. de Souza, Stan Matwin:
Fishing Activity Detection from AIS Data Using Autoencoders. Canadian AI 2016: 33-39 - [c38]Mohammed Shameer Iqbal, Daniel L. Silver:
A Scalable Unsupervised Deep Multimodal Learning System. FLAIRS 2016: 50-55 - [c37]Özgür Yilmaz, Artur S. d'Avila Garcez, Daniel L. Silver:
A Proposal for Common Dataset in Neural-Symbolic Reasoning Studies. NeSy@HLAI 2016 - 2015
- [c36]Artur S. d'Avila Garcez, Tarek R. Besold, Luc De Raedt, Peter Földiák, Pascal Hitzler, Thomas Icard, Kai-Uwe Kühnberger, Luís C. Lamb, Risto Miikkulainen, Daniel L. Silver:
Neural-Symbolic Learning and Reasoning: Contributions and Challenges. AAAI Spring Symposia 2015 - [c35]Ti Wang, Daniel L. Silver:
Learning Paired-Associate Images with an Unsupervised Deep Learning Architecture. Canadian AI 2015: 250-263 - [c34]Daniel L. Silver, Geoffrey Mason, Lubna Eljabu:
Consolidation Using Sweep Task Rehearsal: Overcoming the Stability-Plasticity Problem. Canadian AI 2015: 307-322 - [c33]Jane E. Gomes, Daniel L. Silver:
Learning to reason in a Probably Approximately Correct manner. CCECE 2015: 1475-1478 - 2013
- [c32]Daniel L. Silver:
The Consolidation of Task Knowledge for Lifelong Machine Learning. AAAI Spring Symposium: Lifelong Machine Learning 2013 - [c31]Daniel L. Silver, Qiang Yang, Lianghao Li:
Lifelong Machine Learning Systems: Beyond Learning Algorithms. AAAI Spring Symposium: Lifelong Machine Learning 2013 - [c30]Ti Wang, Mohammed Shameer Iqbal, Daniel L. Silver:
An Unsupervised Deep-Learning Architecture That Can Reconstruct Paired Images. RSFDGrC 2013: 388-396 - [i1]Ti Wang, Daniel L. Silver:
Learning Paired-associate Images with An Unsupervised Deep Learning Architecture. CoRR abs/1312.6171 (2013) - 2012
- [j6]Isabelle Guyon, Gideon Dror, Vincent Lemaire, Daniel L. Silver, Graham W. Taylor, David W. Aha:
Analysis of the IJCNN 2011 UTL challenge. Neural Networks 32: 174-178 (2012) - [c29]Daniel L. Silver, Liangliang Tu:
Image Morphing: Transfer Learning between Tasks That Have Multiple Outputs. Canadian AI 2012: 194-205 - [c28]Daryl H. Hepting, Gerry Donaldson, Peter R. King, Daniel L. Silver:
CS/IT outreach from a Canadian perspective. SIGITE Conference 2012: 251-252 - [c27]Daniel L. Silver, Isabelle Guyon, Graham W. Taylor, Gideon Dror, Vincent Lemaire:
ICML2011 Unsupervised and Transfer Learning Workshop. ICML Unsupervised and Transfer Learning 2012: 1-16 - [e1]Isabelle Guyon, Gideon Dror, Vincent Lemaire, Graham W. Taylor, Daniel L. Silver:
Unsupervised and Transfer Learning - Workshop held at ICML 2011, Bellevue, Washington, USA, July 2, 2011. JMLR Proceedings 27, JMLR.org 2012 [contents] - 2011
- [c26]Daniel L. Silver:
Machine Lifelong Learning: Challenges and Benefits for Artificial General Intelligence. AGI 2011: 370-375 - [c25]Benjamin Fowler, Daniel L. Silver:
Consolidation Using Context-Sensitive Multiple Task Learning. Canadian AI 2011: 128-139 - 2010
- [j5]Zhongxu Ma, Daniel L. Silver, Elhadi M. Shakshuki:
User profile management: reference model and web services implementation. Int. J. Web Grid Serv. 6(1): 1-34 (2010) - [c24]Liangliang Tu, Benjamin Fowler, Daniel L. Silver:
CsMTL MLP For WEKA: Neural Network Learning with Inductive Transfer. FLAIRS 2010
2000 – 2009
- 2008
- [j4]Elhadi M. Shakshuki, Hsiang-Hwa Koo, Darcy G. Benoit, Daniel L. Silver:
A distributed multi-agent meeting scheduler. J. Comput. Syst. Sci. 74(2): 279-296 (2008) - [j3]Daniel L. Silver, Kristin P. Bennett:
Guest editor's introduction: special issue on inductive transfer learning. Mach. Learn. 73(3): 215-220 (2008) - [j2]Daniel L. Silver, Ryan Poirier, Duane Currie:
Inductive transfer with context-sensitive neural networks. Mach. Learn. 73(3): 313-336 (2008) - [c23]Daniel L. Silver, Liangliang Tu:
Image Transformation: Inductive Transfer between Multiple Tasks Having Multiple Outputs. Canadian AI 2008: 296-307 - [c22]Sajid Hussain, Richard Peters, Daniel L. Silver:
Using received signal strength variation for surveillance in residential areas. Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2008: 69730L - 2007
- [c21]Xiaoyan Peng, Daniel L. Silver:
Interface Adaptation Based on User Expectation. AINA Workshops (2) 2007: 264-269 - [c20]André Trudel, Daniel L. Silver:
A Guide for Establishing and Managing a Computer Science Industrial Advisory Board. FECS 2007: 275-280 - [c19]Daniel L. Silver, Ryan Poirier:
Context-Sensitive MTL Networks for Machine Lifelong Learning. FLAIRS 2007: 628-633 - [c18]F. Hu, Daniel L. Silver, André Trudel:
LonelyDog@Home. Web Intelligence/IAT Workshops 2007: 333-337 - [c17]Daniel L. Silver, Robert E. Mercer:
Sequential Inductive Transfer for Coronary Artery Disease Diagnosis. IJCNN 2007: 2635-2641 - [c16]Daniel L. Silver, Ryan Poirier:
Requirements for Machine Lifelong Learning. IWINAC (1) 2007: 313-319 - [c15]Daniel L. Silver, Lisa Gaudette, Ian Spooner:
Inductive transfer applied to stream discharge modeling. SMC 2007: 528-534 - 2006
- [c14]Daniel L. Silver, Ryan Poirier:
Machine Life-Long Learning with csMTL Networks. AAAI 2006 - [c13]Daniel L. Silver, Adam Biggs:
Keystroke and Eye-Tracking Biometrics for User Identification. IC-AI 2006: 344-348 - 2005
- [c12]André Trudel, Daniel L. Silver:
Establishing and Managing a Computer Science Industrial Advisory Board. FECS 2005: 108-112 - [c11]Daniel L. Silver, Richard Alisch:
A Measure of Relatedness for Selecting Consolidated Task Knowledge. FLAIRS 2005: 399-404 - [c10]James Blustein, Ching-Lung Fu, Daniel L. Silver:
Information visualization for an intrusion detection system. Hypertext 2005: 278-279 - [c9]James Blustein, Daniel L. Silver, Ching-Lung Fu:
Information Visualization for Intrusion Detection. PST 2005 - [c8]Xiaoyan Peng, Daniel L. Silver:
User Control over User Adaptation: A Case Study. User Modeling 2005: 443-447 - 2004
- [c7]Daniel L. Silver, Ryan Poirier:
Sequential Consolidation of Learned Task Knowledge. Canadian AI 2004: 217-232 - [c6]Ching-Lung Fu, Daniel L. Silver:
Time-Sensitive Sampling for Spam Filtering. Canadian AI 2004: 551-553 - 2003
- [c5]Daniel L. Silver, Peter McCracken:
Selective Transfer of Task Knowledge Using Stochastic Noise. AI 2003: 190-205 - [c4]Daniel L. Silver, Peter McCracken:
The Consolidation of Neural Network Task Knowledge. ICMLA 2003: 185-192 - 2002
- [c3]Daniel L. Silver, Robert E. Mercer:
The Task Rehearsal Method of Life-Long Learning: Overcoming Impoverished Data. AI 2002: 90-101 - [c2]Daniel L. Silver, Robert E. Mercer:
Life-long Learning through Task Rehersal and Selective Transfer. ICMLA 2002: 173-179
1990 – 1999
- 1998
- [p1]Daniel L. Silver, Robert E. Mercer:
The Parallel Transfer of Task Knowledge Using Dynamic Learning Rates Based on a Measure of Relatedness. Learning to Learn 1998: 213-233 - 1996
- [j1]Daniel L. Silver:
The Parallel Transfer of Task Knowledge Using Dynamic Learning Rates Based on a Measure of Relatedness. Connect. Sci. 8(2): 277-294 (1996) - 1994
- [c1]Daniel L. Silver, James W. Hong, Michael A. Bauer:
X-500 Directory Schema Management. ICDE 1994: 393-400
Coauthor Index
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