Publications Type Select All Talks Thesis Papers Area Select All HEP Targeted systems HAC MMA Neuroscience Newest to Oldest Oldest to Newest Apply Filters Talks Uncertainty Quantification and Anomaly Detection with Evidential Deep Learning Read More > Papers Ultra Fast Transformers on FPGAs for Particle Physics Experiments Read More > Talks Ultra Fast Transformers on FPGAs for Particle Physics Experiments Read More > Talks The Stability of Positional Encodings for Graphs Read More > Talks Supernova Neutrino Burst Detection with Mineral Detectors Read More > Papers On the Stability of Expressive Positional Encodings for Graphs Read More > Talks Neuroscience ML challenges using CodaBench: Decoding multi-limb trajectories from two-photon calcium imaging Read More > Papers High Pileup Particle Tracking with Object Condensation Read More > Papers FPGA Deployment of LFADS for Real-time Neuroscience Experiments Read More > Talks Explainable AI for Interpretability of Neural Networks Read More > Thesis Evaluating the Quality of HLS4ML’s Basic Neural Network Implementations on FPGAs Read More > Thesis Deep Learning Applications for Particle Physics in Tracking and Calorimetry Read More > Papers The ZTF Source Classification Project: III. A Catalog of Variable Sources Read More > Papers Quantifying the Efficiency of High-Level Synthesis for Machine Learning Inference Read More > Papers Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs Read More > Papers HIDA: A Hierarchical Dataflow Compiler for High-Level Synthesis Read More > Talks Machine Learning Acceleration — Quantization Process and Tools Development Read More > Talks Machine Learning acceleration in the Global Event Processor of the ATLAS Trigger Update Read More > Papers Accelerating CNNs on FPGAs for Particle Energy Reconstruction Read More > Papers Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml Read More > Talks Portable Acceleration of CMS Mini-AOD Production with Coprocessors as a Service Read More > Talks Jets as sets or graphs: Fast jet classification on FPGAs for efficient triggering at the HL-LHC Read More > Talks MLPF: Machine Learning for Particle Flow Read More > Talks Scalable neural network models and terascale datasets for particle-flow reconstruction Read More > Papers Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors Read More > Talks ACTS as a Service Read More > Talks FKeras: A Sensitivity Analysis Tool for Edge Neural Networks Read More > Papers ScaleHLS, a Scalable High-level Synthesis Framework with Multi-level Transformations and Optimizations Read More > Papers AutoScaleDSE: A Scalable Design Space Exploration Engine for High-Level Synthesis Read More > Talks Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs Read More > Talks FPGA Deployment of LFADS for Real-time Neuroscience Experiments Read More > Talks Quantifying the Efficiency of High-Level Synthesis for Machine Learning Inference Read More > Talks Fast ML in the NSF HDR Institute: A3D3 Read More > Talks Community Vision, Needs, and Progress Read More > Talks ZTF SCoPe: A Catalog of Variable Sources Read More > Papers GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders Read More > Talks Parameter estimation using Likelihood-free Inference Read More > Talks BEVFusion-R: Efficient and Deployment-Ready Camera-Radar Fusion Read More > Talks SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer Read More > Talks FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer Read More > Talks BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation Read More > Papers SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning Read More > Papers Structural Re-weighting Improves Graph Domain Adaptation Read More > Papers Progress towards an improved particle flow algorithm at CMS with machine learning, 2023, ACAT Read More > Papers Semi-supervised Graph Neural Networks for Pileup Noise Removal, 2022, NeurIPS AI4Science, EPJC Read More > Papers Interpretable Geometric Deep Learning via Learnable Randomness Injection, 2023, ICLR Read More > Papers Neighborhood-aware Scalable Temporal Network Representation Learning, 2022, LoG Read More > Papers Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning, 2022, VLDB Read More > Papers TorchSparse++: Efficient Point Cloud Engine Read More > Papers NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers, 2023, submitted Read More > Talks Tutorials on ScaleHSL, FPGA, Feb. 27, 2022 Read More > Papers Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism, 2022, ICML Read More > Talks Javier Duarte, Artificial Intelligence at the Edge of Particle Physics, HEP Seminar, Columbia University, November 17, 2021 Read More > Papers Applications and Techniques for Fast Machine Learning in Science, 2021, Submitted to Front. Big Data Read More > Papers PointAcc: Efficient Point Cloud Accelerator, 2021, MICRO Read More > Talks Shih-Chieh Hsu, NSF HDR Institute: Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery (A3D3), Fast ML General Meeting, October 1, 2021 Read More > Papers ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation, 2022, HPCA Read More > Papers Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution, 2020, ECCV Read More > Papers Point-Voxel CNN for Efficient 3D Deep Learning, 2019, NeurIPS (Spotlight) Read More >
Talks Neuroscience ML challenges using CodaBench: Decoding multi-limb trajectories from two-photon calcium imaging Read More >
Talks Machine Learning acceleration in the Global Event Processor of the ATLAS Trigger Update Read More >
Papers Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml Read More >
Talks Jets as sets or graphs: Fast jet classification on FPGAs for efficient triggering at the HL-LHC Read More >
Talks Scalable neural network models and terascale datasets for particle-flow reconstruction Read More >
Papers Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors Read More >
Papers ScaleHLS, a Scalable High-level Synthesis Framework with Multi-level Transformations and Optimizations Read More >
Talks Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs Read More >
Talks SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution Vision Transformer Read More >
Talks BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation Read More >
Papers SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning Read More >
Papers Progress towards an improved particle flow algorithm at CMS with machine learning, 2023, ACAT Read More >
Papers Semi-supervised Graph Neural Networks for Pileup Noise Removal, 2022, NeurIPS AI4Science, EPJC Read More >
Papers Interpretable Geometric Deep Learning via Learnable Randomness Injection, 2023, ICLR Read More >
Papers Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning, 2022, VLDB Read More >
Papers NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers, 2023, submitted Read More >
Papers Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism, 2022, ICML Read More >
Talks Javier Duarte, Artificial Intelligence at the Edge of Particle Physics, HEP Seminar, Columbia University, November 17, 2021 Read More >
Papers Applications and Techniques for Fast Machine Learning in Science, 2021, Submitted to Front. Big Data Read More >
Talks Shih-Chieh Hsu, NSF HDR Institute: Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery (A3D3), Fast ML General Meeting, October 1, 2021 Read More >
Papers ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation, 2022, HPCA Read More >
Papers Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution, 2020, ECCV Read More >