Publication Title
309
Point-Voxel CNN for Efficient 3D Deep Learning
310
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
311
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
312
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
313
Calibrating Electrons and Photons in the CMS ECAL using Graph Neural Networks
314
PointAcc: Efficient Point Cloud Accelerator
315
Applications and Techniques for Fast Machine Learning in Science
316
Charged Particle Tracking via Edge-Classifying Interaction Networks
317
ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation
318
TorchSparse: Efficient point cloud inference engine
319
Hardware-accelerated inference for real-time gravitational-wave astronomy
320
A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics
321
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
322
ScaleHLS: a scalable high-level synthesis framework with multi-level transformations and optimizations
323
Algorithm and system co-design for efficient subgraph-based graph representation learning
324
Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism
325
Unveiling hidden physics at the LHC
326
Neighborhood-aware Scalable Temporal Network Representation Learning
327
Unsupervised Learning for Combinatorial Optimization Needs Meta-Learning
328
Semi-supervised Graph Neural Networks for Pileup Noise Removal
329
Interpretable Geometric Deep Learning via Learnable Randomness Injection
330
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning
331
Progress towards an improved particle flow algorithm at CMS with machine learning
332
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
333
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
334
TorchSparse++: Efficient Point Cloud Engine
335
Structural Re-weighting Improves Graph Domain Adaptation
336
Accelerating CNNs on FPGAs for Particle Energy Reconstruction
337
Low Latency Edge Classification GNN for Particle Trajectory Tracking on FPGAs
338
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
339
AutoScaleDSE: A Scalable Design Space Exploration Engine for High-Level Synthesis
56 rows
DOI/Link
Author(s)
Insitute
Publication Date
Code
Supplementary Materials (Data, Notebooks, Models)
Keywords
https://arxiv.org/abs/1907.03739
Zhijian Liu, Haotian Tang, Yujun Lin, Song Han
A3D3
https://github.com/mit-han-lab/pvcnn
Computer vision, Pattern recognition, Point-Voxel CNN, PVConv, Point-Voxel Convolution, Point-Voxel, Point-based Branch, Voxel-based Convolutions, Voxel-based Branch, Point-based Convolutions, Indoor Scene Segmentation, Voxel-based Methods
https://arxiv.org/abs/2007.16100
Haotian Tang, Zhijian Liu, Shujun Shen, Yujun Lin, Song Han
A3D3
Computer vision, Pattern recognition
https://doi.org/10.48550/arXiv.2106.05819
Susheel Suresh, Pan Li, Hao Cong, Jian Yao
A3D3
https://github.com/susheels/adgcl
Machine Learning, Artificial Intelligence
https://doi.org/10.48550/arXiv.2205.07690
Nicolò Ghielmetti, Vladimir Loncar, Maurizio Pierini, Marcel Roed, Sioni Summers, Thea Aarrestad, Christoffer Petersson, Hampus Linander, Jennifer Ngadiuba, Kelvin Lin, Philip Harris
A3D3
https://github.com/fastmachinelearning/hls4ml
Deep learning, PFGA, convolutional neural network
https://par.nsf.gov/servlets/purl/10421786
Simon Rothman
A3D3
https://github.com/ssrothman/DynamicReductionNetwork
Particle Physics, Calibration, CERN Large Hadron Collider, Dynamic Graph Neural Networks Regression, Graph Neural Networks, Electromagnetic Calorimeters, Calibrated, Machine Learning
https://arxiv.org/abs/2110.07600
Yujun Lin, Zhekai Zhang, Haotian Tang, Hanrui Wang, Song Han
A3D3
https://hanlab.mit.edu/projects/pointacc
Computer systems organization, neural networks, Hardware architecture, point cloud, neural network accelerator, sparse convolution
https://doi.org/10.48550/arXiv.2110.13041
Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini
A3D3
https://fastmachinelearning.org/
Machine learning, hardware architecture, data analysis, statistics and probability, instrumentation and detectors, fast Machine Learning
https://doi.org/10.48550/arXiv.2103.16701
Gage DeZoort, Savannah Thais, Javier Duarte, Vesal Razavimaleki, Markus Atkinson, Isobel Ojalvo, Mark Neubauer, Peter Elmer
A3D3
https://github.com/GageDeZoort/interaction_network_paper
Graph neural networks, tracking, particle physics
https://doi.org/10.1145/3489517.3530631
Hanchen Ye, Cong Hao, Jianyi Cheng, Hyunmin Jeong, Jack Huang, Stephen Neuendorffer, Deming Chen
A3D3
https://github.com/UIUC-ChenLab/scalehls
Programming Languages, Hardware Architecture, High-Level Synthesis, MLIR, Compiler, FPGA, Optimization, Design Space Exploration
https://doi.org/10.48550/arXiv.2204.10319
Haotian Tang, Zhijian Liu, Xiuyu Li, Yujun Lin, Song Han
A3D3
https://hanlab.mit.edu/projects/torchsparse
Machine learning, computer vision and pattern recognition, performance
https://doi.org/10.1038/s41550-022-01651-w
Alec Gunny, Dylan Rankin, Jeffrey Krupa, Muhammed Saleem, Tri Nguyen, Michael Coughlin, Philip Harris
A3D3
https://github.com/fastmachinelearning/gw-iaas
Astronomical instrumentation, design, synthesis and processing, software, astronomy, astrophysics and cosmology, data analysis, deep learning, perspective, physics, physics and astronomy, wave analysis
https://doi.org/10.1145/3526058.3535454
Alec Gunny, Dylan Rankin, Philip Harris, Erik Katsavounidis, Ethan Marx, Muhammed Saleem, Michael Coughlin, William Benoit
A3D3
https://github.com/ML4GW/hermes
Applied computing, physics, computing methodologies, neural networks, Gravitational waves, neural networks, mlops, applied computing, physical sciences and engineering, physics, computing methodologies, Machine Learning, Machine Learning approaches
https://doi.org/10.48550/arXiv.2206.07527
Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott, Jovan Mitrevski, Ben Hawks, Nhan Tran, Vladimir Loncar, Sioni Summers, Hendrik Borras, Jules Muhizi, Matthew Trahms, Shih-Chieh Hsu, Scott Hauck, Javier Duarte
A3D3
https://github.com/fastmachinelearning/qonnx
https://github.com/Xilinx/finn-base/tree/main/docs/QONNX
Quantized neural network, intermediate representation, FPGA
https://doi.org/10.1145/3489517.3530631
Hanchen Ye, Hyegang Jun, Hyunmin Jeong, Stephen Neuendorffer, Deming Chen
A3D3
https://github.com/UIUC-ChenLab/scalehls
Programming Languages, Hardware Architecture
https://doi.org/10.48550/arXiv.2202.13538
Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang, Pan Li
A3D3
https://github.com/Graph-COM/SUREL
Machine learning, databases, distributed, parallel, cluster computing
https://doi.org/10.48550/arXiv.2201.12987
Siqi Miao, Miaoyuan Liu, Pan Li
A3D3
https://github.com/Graph-COM/GSAT
Interpretation, Graph Neural Networks, Out-of-Distribution Generalization, Multilinear Extension, Causality, Geometric Deep Learning
https://doi.org/10.1140/epjc/s10052-022-10541-4
Oliver Fischer, Bruce Mellado, Stefan Antusch, Emanuele Bagnaschi, Shankha Banerjee, Geoff Beck, Benedetta Belfatto, Matthew Bellis, Zurab Berezhiani, Monika Blanke, Bernat Capdevila, Kingman Cheung, Andreas Crivellin, Nishita Desai, Bhupal Dev, Rohini Godbole, Tao Han, Philip Harris, Martin Hoferichter, Matthew Kirk, Suchita Kulkarni, Clemens Lange, Kati Lassila-Perini, Zhen Liu, Farvah Mahmoudi, Claudio Andrea Manzari, David Marzocca, Biswarup Mukhopadhyaya, Antonio Pich, Xifeng Ruan, Luc Schnell, Jesse Thaler, Susanne Westhoff
A3D3
Experimental particle physics, experimental nuclear physics, particle physics, physics and astronomy, societal outreach ofphysics, theoretical particle physics
https://doi.org/10.48550/arXiv.2209.01084
Yuhong Luo, Pan Li
A3D3
https://github.com/Graph-COM/Neighborhood-Aware-Temporal-Network
Machine learning, social and information networks
https://doi.org/10.48550/arXiv.2301.03116
Pan Li, Raghav Kansal, Javier Duarte, Philip Harris
A3D3
https://github.com/Graph-COM/Meta_CO
Machine learning, artificial intelligence
https://doi.org/10.1140/epjc/s10052-022-11083-5
Tianchun Li, Shikun Liu, Yongbin Feng, Garyfallia Paspalaki, Nhan Tran, Miaoyuan Liu, Pan Li
A3D3
Accelerator physics, learning algorithms, Machine Learning, particle acceleration, stochastic learning and adaptive control, synaptic pruning
https://doi.org/10.48550/arXiv.2210.16966
Si Si, Raghav Kansal, Javier Duarte, Philip Harris, Pan Li
A3D3
https://github.com/Graph-COM/LRI
Machine learning
https://doi.org/10.48550/arXiv.2303.03379
Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang, Pan Li
A3D3
https://github.com/Graph-COM/SUREL_Plus
Machine learning, databases, distributed, parallel, cluster computing
https://doi.org/10.48550/arXiv.2303.17657
Farouk Mokhtar, Joosep Pata, Jean-Roch Vlimant
A3D3
Data analysis, statistics and probability, Machine Learning, high energy physics - experiment, instrumentation and detectors
https://doi.org/10.48550/arXiv.2207.00559
Elham Khoda, Dylan Rankin, Rafael Teixeira de Lima, Philip Harris, Scott Hauck, Shih-Chieh Hsu, Michael Kagan, Vladimir Loncar
A3D3
https://github.com/fastmachinelearning/hls4ml
Machine learning, high energy physics - experiment, instrumentation and detectors, Machine Learning
https://doi.org/10.48550/arXiv.2306.00978
Ji Lin, Haotian Tang, Xingyu Dang, Song Han
A3D3
https://github.com/mit-han-lab/llm-awq
Computation and language
https://doi.org/10.48550/arXiv.2204.10319
Haotian Tang, Zhijian Liu, Xiuyu Li, Yujun Lin, Song Han
A3D3
https://github.com/mit-han-lab/torchsparse
https://hanlab.mit.edu/projects/torchsparse
Point cloud compression, Deep learning, Three-dimensional displays, Convolution, Semantic segmentation, Graphics processing units, Libraries, Point Cloud, Deep Learning, 3D Reconstruction, Inference System, 3D Detection, Input Features, Object Detection, PyTorch, Matrix Multiplication, Vanilla, Hash Function, Residual Block, 3D Point Cloud, Masked Images, 3D Scene, 3D Object Detection, Output Point, Point Cloud Model, Sparse Tensor
https://doi.org/10.48550/arXiv.2306.03221
Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiu Qiang, Pan Li
A3D3
https://github.com/Graph-COM/StruRW
https://zenodo.org/records/8015774
Graph Domain Adaptation, High-energy Physics, Node Representations, Benchmark Dataset, Node Attributes, Distribution Shift, Graph-structured Data
https://people.ece.uw.edu/hauck/publications/DeepcaloJ.pdf
ChiJui Chen, YanLun Huang, LingChi Yang, Ziang Yin, Philip Harris, Scott Hauck, ShihChieh Hsu, BoCheng Lai, Kelvin Lin, Dylan Rankin, Alexander Schuy
A3D3
https://github.com/ChiRuiChen/ACFPER
hls4ml, DeepCalo models, FPGAs, Particle Physics, Deep Learning Models, CERN
https://doi.org/10.48550/arXiv.2306.11330
Shi-Yu Huang, Yun-Chen Yang, Yu-Ru Su, Bo-Cheng Lai
A3D3
Hardware architecture, Machine Learning, high energy physics - experiment
https://doi.org/10.48550/arXiv.2309.12307
Yukang Chen, Shengju Qian, Haotian Tang, Xin Lai, Zhijian Liu, Song Han, Jiaya Jia
A3D3
https://github.com/JIA-Lab-research/LongLoRA
Computation language, artificial intelligence, Machine Learning
https://doi.org/10.1145/3572959
Hyegang Jun, Hanchen Ye, Hyunmin Jeong, Deming Chen
A3D3
Hardware, resource binding and sharing, high-level and register-transfer level synthesis, High-level synthesis, design space exploration, static analysis