Hardware-Aware Probabilistic Circuits

[1]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[2]  Guy Van den Broeck,et al.  Learning Logistic Circuits , 2019, AAAI.

[3]  Pedro M. Domingos,et al.  Discriminative Learning of Sum-Product Networks , 2012, NIPS.

[4]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[5]  Tom Minka,et al.  Principled Hybrids of Generative and Discriminative Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[6]  David Haussler,et al.  Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.

[7]  Guy Van den Broeck,et al.  Towards Hardware-Aware Tractable Learning of Probabilistic Models , 2019, NeurIPS.

[8]  Katharina Morik,et al.  Integer undirected graphical models for resource-constrained systems , 2016, Neurocomputing.

[9]  David D. Wentzloff,et al.  Hardware Accelerator for Probabilistic Inference in 65-nm CMOS , 2016, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[10]  Pierre Marquis,et al.  A Knowledge Compilation Map , 2002, J. Artif. Intell. Res..

[11]  Marian Verhelst,et al.  PROBLP: A framework for Iow-precision probabilistic inference , 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

[12]  Guy Van den Broeck,et al.  Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams , 2020, IDA.

[13]  Sebastian Tschiatschek,et al.  On Bayesian Network Classifiers with Reduced Precision Parameters , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Craig Boutilier,et al.  Context-Specific Independence in Bayesian Networks , 1996, UAI.

[15]  Luc De Raedt,et al.  TP-Compilation for inference in probabilistic logic programs , 2016, Int. J. Approx. Reason..

[16]  Adnan Darwiche,et al.  Structured Features in Naive Bayes Classification , 2016, AAAI.

[17]  Guy Van den Broeck,et al.  Learning the Structure of Probabilistic Sentential Decision Diagrams , 2017, UAI.

[18]  Ole J. Mengshoel,et al.  Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems , 2015 .

[19]  Guy Van den Broeck,et al.  What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features , 2019, IJCAI.

[20]  Mark Horowitz,et al.  1.1 Computing's energy problem (and what we can do about it) , 2014, 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC).

[21]  Alvaro H. C. Correia,et al.  Towards Scalable and Robust Sum-Product Networks , 2019, SUM.

[22]  Guy Van den Broeck,et al.  Symbolic Exact Inference for Discrete Probabilistic Programs , 2019, ArXiv.

[23]  Michael I. Jordan,et al.  Thin Junction Trees , 2001, NIPS.

[24]  Jesse Davis,et al.  Learning Markov Network Structure with Decision Trees , 2010, 2010 IEEE International Conference on Data Mining.

[25]  Jean-Philippe Diguet,et al.  Bayesian network-based framework for the design of reconfigurable health management monitors , 2015, 2015 NASA/ESA Conference on Adaptive Hardware and Systems (AHS).

[26]  Guy Van den Broeck,et al.  Probabilistic Sentential Decision Diagrams , 2014, KR.

[27]  Matti Siekkinen,et al.  Smartphone Energy Consumption: Modeling and Optimization , 2014 .

[28]  Adnan Darwiche,et al.  Modeling and Reasoning with Bayesian Networks , 2009 .

[29]  Guy Van den Broeck,et al.  Tractable Learning for Complex Probability Queries , 2015, NIPS.

[30]  Daniel Lowd,et al.  Learning Markov Networks With Arithmetic Circuits , 2013, AISTATS.

[31]  Kristian Kersting,et al.  Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning , 2019, UAI.

[32]  Ryutaro Tateishi,et al.  A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees , 2013 .

[33]  David Buchfuhrer,et al.  The Complexity of Boolean Formula Minimization , 2008, ICALP.

[34]  Carsten Binnig,et al.  Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators , 2018, 2018 IEEE 36th International Conference on Computer Design (ICCD).

[35]  Adnan Darwiche,et al.  Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence SDD: A New Canonical Representation of Propositional Knowledge Bases , 2022 .

[36]  Pedro M. Domingos,et al.  Sum-product networks: A new deep architecture , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[37]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[38]  R. Pace,et al.  Sparse spatial autoregressions , 1997 .

[39]  Pedro M. Domingos,et al.  Learning Arithmetic Circuits , 2008, UAI.

[40]  Daniel Lowd,et al.  Discriminative Structure Learning of Arithmetic Circuits , 2016, AISTATS.

[41]  Guy Van den Broeck,et al.  Scaling exact inference for discrete probabilistic programs , 2020, Proc. ACM Program. Lang..

[42]  Davide Anguita,et al.  A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.

[43]  Guy Van den Broeck,et al.  On Tractable Computation of Expected Predictions , 2019, NeurIPS.