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ORCIDLaura Isabel Galindez Olascoaga
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2020 – today
- 2023
[c14]Alisha Menon, Laura Isabel Galindez Olascoaga, Vamshi Balanaga, Anirudh Natarajan, Jennifer Ruffing, Ryan Ardalan, Jan M. Rabaey:
Shared Control of Assistive Robots through User-intent Prediction and Hyperdimensional Recall of Reactive Behavior. ICRA 2023: 12638-12644- 2022
- [j2]Nimish Shah
, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst:
DPU: DAG Processing Unit for Irregular Graphs With Precision-Scalable Posit Arithmetic in 28 nm. IEEE J. Solid State Circuits 57(8): 2586-2596 (2022) - [c13]Alisha Menon, Laura Isabel Galindez Olascoaga, Niki Shakouri, Jennifer Ruffing, Vamshi Balanaga, Jan M. Rabaey:
Brain-inspired Multi-level Control of an Assistive Prosthetic Hand through EMG Task Recognition. BioCAS 2022: 384-388 - [c12]Alisha Menon, Anirudh Natarajan, Laura Isabel Galindez Olascoaga, Youbin Kim, Braeden C. Benedict, Jan M. Rabaey:
On the Role of Hyperdimensional Computing for Behavioral Prioritization in Reactive Robot Navigation Tasks. ICRA 2022: 7335-7341 - 2021
- [c11]Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst:
9.4 PIU: A 248GOPS/W Stream-Based Processor for Irregular Probabilistic Inference Networks Using Precision-Scalable Posit Arithmetic in 28nm. ISSCC 2021: 150-152 - [i3]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
ProbLP: A framework for low-precision probabilistic inference. CoRR abs/2103.00216 (2021) - [i2]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
Acceleration of probabilistic reasoning through custom processor architecture. CoRR abs/2103.00266 (2021) - [i1]Nimish Shah, Laura Isabel Galindez Olascoaga, Shirui Zhao, Wannes Meert, Marian Verhelst:
DPU: DAG Processing Unit for Irregular Graphs with Precision-Scalable Posit Arithmetic in 28nm. CoRR abs/2112.05660 (2021) - 2020
- [c10]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
Acceleration of probabilistic reasoning through custom processor architecture. DATE 2020: 322-325 - [c9]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst:
Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams. IDA 2020: 184-196 - [c8]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst:
Dynamic Complexity Tuning for Hardware-Aware Probabilistic Circuits. IoT Streams/ITEM@PKDD/ECML 2020: 283-295
2010 – 2019
- 2019
- [c7]Nimish Shah, Laura Isabel Galindez Olascoaga, Wannes Meert, Marian Verhelst:
ProbLP: A framework for low-precision probabilistic inference. DAC 2019: 190 - [c6]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Guy Van den Broeck, Marian Verhelst:
On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications. EMC2@NeurIPS 2019: 66-70 - [c5]Laura Isabel Galindez Olascoaga, Wannes Meert, Nimish Shah, Marian Verhelst, Guy Van den Broeck:
Towards Hardware-Aware Tractable Learning of Probabilistic Models. NeurIPS 2019: 13726-13736 - 2018
- [j1]Laura Isabel Galindez Olascoaga, Komail M. H. Badami, Jonas Vlasselaer, Wannes Meert, Marian Verhelst:
Dynamic Sensor-Frontend Tuning for Resource Efficient Embedded Classification. IEEE J. Emerg. Sel. Topics Circuits Syst. 8(4): 858-872 (2018) - [c4]Laura Isabel Galindez Olascoaga, Jonas Vlasselaer, Wannes Meert, Marian Verhelst:
Feature noise tuning for resource efficient Bayesian Network Classifiers. ESANN 2018 - [c3]Martin Andraud, Laura Isabel Galindez Olascoaga, Yichuan Lu, Yiorgos Makris, Marian Verhelst:
On the use of Bayesian Networks for Resource-Efficient Self-Calibration of Analog/RF ICs. ITC 2018: 1-10 - 2016
- [c2]Laura Isabel Galindez Olascoaga, Komail M. H. Badami, V. Rajesh Pamula, Steven Lauwereins, Wannes Meert, Marian Verhelst:
Exploiting system configurability towards dynamic accuracy-power trade-offs in sensor front-ends. ACSSC 2016: 1027-1031 - [c1]Laura Isabel Galindez Olascoaga, Wannes Meert, Herman Bruyninckx, Marian Verhelst:
Extending Naive Bayes with Precision-tunable Feature Variables for Resource-efficient Sensor Fusion. AI-IoT@ECAI 2016: 23-30
Coauthor Index
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