Latest Research
TFPred: Learning Discriminative Representations from Unlabeled Data for Few-Label Rotating Machinery Fault Diagnosis
Xiaohan-Chen/TFPred
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Control Engineering Practice 2024
Recent advances in intelligent rotating machinery fault diagnosis have been enabled by the availability of massive labeled training data.
Gradient-based Automatic Per-Weight Mixed Precision Quantization for Neural Networks On-Chip
Model size and inference speed at deployment time, are major challenges in many deep learning applications.
RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow Estimation
hmorimitsu/ptlflow
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IEEE International Conference on Robotics and Automation (ICRA) 2024
Extracting motion information from videos with optical flow estimation is vital in multiple practical robot applications.
Ranked #6 on
Optical Flow Estimation
on KITTI 2015
Computational Approaches for Integrating out Subjectivity in Cognate Synonym Selection
However, as we show here, binary character matrices, which are used as input for computational methods, do allow for representing the entire dataset including all synonyms.
MicroDreamer: Zero-shot 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction
In this paper, we introduce score-based iterative reconstruction (SIR), an efficient and general algorithm for 3D generation with a multi-view score-based diffusion model.
ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging
wdika/atommic
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We benchmark 25 DL models on eight publicly available datasets to present distinct applications of ATOMMIC on accelerated MRI reconstruction, image segmentation, quantitative parameter map estimation, and joint accelerated MRI reconstruction and image segmentation utilizing MTL.
CLIP-Mamba: CLIP Pretrained Mamba Models with OOD and Hessian Evaluation
State space models and Mamba-based models have been increasingly applied across various domains, achieving state-of-the-art performance.
RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
Large Language Models (LLMs) have catalyzed significant advancements in Natural Language Processing (NLP), yet they encounter challenges such as hallucination and the need for domain-specific knowledge.
Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
dunni3/flowmol
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In this work, we explore the use of flow matching, a recently proposed generative modeling framework that generalizes diffusion models, for the task of de novo molecule generation.
KAN: Kolmogorov-Arnold Networks
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs).
