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 HIDA: A Hierarchical Dataflow Compiler for High-Level Synthesis Read More > Talks hls4ml: low latency neural network inference on FPGAs Read More > Talks Graph Neural Network-based Track finding as a Service with ACTS Read More > Talks NSF HDR ML Anomaly Detection Challenge Read More > Talks A search for binary mergers in archival LIGO data using aframe, a machine learning detection pipeline Read More > Talks A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences Read More > Talks Energy Frontier Exploration using Particle Physics and AI Read More > Papers A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences Read More > Papers Point Transformer V3: Simpler, Faster, Stronger Read More > Papers LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models Read More > Papers AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration Read More > 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 A search for binary mergers in archival LIGO data using aframe, a machine learning detection pipeline Read More >
Talks A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences Read More >
Papers A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences 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 >