update links

Author: jmduarte

projects.html

@@ -51,17 +51,17 @@ <h3> Papers </h3>
 				<li> Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster, <a href="https://arxiv.org/abs/2011.07371">arXiv:2011.07371</a>. </li>
 				<li> A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC, <a href="https://10.1109/TNS.2021.3087100">IEEE Trans. Nucl. Sci. 68, 2179 (2021)</a>. </li>
 				<li> Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider, <a href="https://arxiv.org/abs/2108.03986">arXiv:2108.03986</a>. </li>
-				<li> Accelerating Recurrent Neural Networks for Gravitational Wave Experiments, <a href="https://arxiv.org/abs/2106.14089">arXiv:2106.14089</a>. </li>
+				<li> Accelerating Recurrent Neural Networks for Gravitational Wave Experiments, <a href="https://doi.org/10.1109/ASAP52443.2021.00025">ASAP Conference 2021</a>. </li>
 				<li> Fast convolutional neural networks on FPGAs with hls4ml, <a href="https://doi.org/10.1088/2632-2153/ac0ea1">Mach. Learn.: Sci. Technol. 2, 045015 (2021)</a>. </li>
-				<li> MLPerf Tiny Benchmark, <a href="https://arxiv.org/abs/2106.07597">arXiv:2106.07597</a>. </li>
-				<li> hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices, <a href="https://arxiv.org/abs/2103.05579">arXiv:2103.05579</a>. </li>
-		    <li> GPU-accelerated machine learning inference as a service for computing in neutrino experiments, <a href="https://doi.org/10.1038/s42256-021-00356-5">Nat. Mach. Intell. 3, 675 (2021)</a>. </li>
+				<li> MLPerf Tiny Benchmark, <a href="https://arxiv.org/abs/2106.07597">NeurIPS 2021 Datasets and Benchmarks Track</a>. </li>
+				<li> hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices, <a href="https://arxiv.org/abs/2103.05579">tinyML Reserach Symposium 2021</a>. </li>
+		    <li> GPU-accelerated machine learning inference as a service for computing in neutrino experiments, <a href="https://doi.org/10.3389/fdata.2020.604083">Front. Big Data 3, 48 (2021)</a>. </li>
 		    <li> Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics, <a href="https://arxiv.org/abs/2008.03601">arXiv:2008.03601</a>. </li>
 		    <li> GPU coprocessors as a service for deep learning inference in high energy physics, <a href="https://arxiv.org/abs/2007.10359">arXiv:2007.10539</a>. </li>
 		    <li> Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors, <a href="https://arxiv.org/abs/2006.10159">arXiv:2006.10159</a>. </li>
 		    <li> Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml, <a href="https://doi.org/10.1088/2632-2153/aba042">Mach. Learn.: Sci. Technol. 2, 015001 (2021)</a>. </li>
 		    <li> Fast inference of Boosted Decision Trees in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/15/05/p05026">JINST 15, P05026 (2020)</a>. </li>
-		    <li> ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning, <a href="https://sld.cs.columbia.edu/pubs/giri_date20.pdf"> DATE Conference 2020 </a>. </li>
+		    <li> ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning, <a href="https://sld.cs.columbia.edu/pubs/giri_date20.pdf">DATE Conference 2020 </a>. </li>
 		    <li> Accelerated Machine Learning as a Service for Particle Physics Computing, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_64.pdf">NeurIPS ML4PS Workshop 2019</a>. </li>
 		    <li> Low-latency machine learning inference on FPGAs, <a href="https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_74.pdf">NeurIPS ML4PS Workshop 2019</a>. </li>
 		    <li> Fast inference of deep neural networks in FPGAs for particle physics, <a href="https://doi.org/10.1088/1748-0221/13/07/P07027">JINST 13, P07027 (2018)</a>. </li>