Search Results for author:
Found 15 papers, 4 papers with code
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild
A significant portion of key information is embedded in molecular structure figures, complicating large-scale literature searches and limiting the application of large language models in fields such as biology, chemistry, and pharmaceuticals.
Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs
Autism spectrum disorder(ASD) is a pervasive developmental disorder that significantly impacts the daily functioning and social participation of individuals.
A high-accuracy multi-model mixing retrosynthetic method
The field of computer-aided synthesis planning (CASP) has seen rapid advancements in recent years, achieving significant progress across various algorithmic benchmarks.
Uni-SMART: Universal Science Multimodal Analysis and Research Transformer
Scientific literature often includes a wide range of multimodal elements, such as tables, charts, and molecule, which are hard for text-focused LLMs to understand and analyze.
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis.
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction
XtalNet represents a significant advance in CSP, enabling the prediction of complex structures from PXRD data without the need for external databases or manual intervention.
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis
Single-step retrosynthesis (SSR) in organic chemistry is increasingly benefiting from deep learning (DL) techniques in computer-aided synthesis design.
Ranked #1 on
Single-step retrosynthesis
on USPTO-50k
A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training
Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel ``copy/new'' feedback paradigm to help shape the signal generation of the subject toward the optimal distribution; 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process.
Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement
In these methods, only a subset of the input dataset is needed to train neural networks for the estimation of poses and conformations.
Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars
The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky.
Boosted ab initio Cryo-EM 3D Reconstruction with ACE-EM
The central problem in cryo-electron microscopy (cryo-EM) is to recover the 3D structure from noisy 2D projection images which requires estimating the missing projection angles (poses).
3D Molecular Generation via Virtual Dynamics
Extensive experiment results on pocket-based molecular generation demonstrate that VD-Gen can generate novel 3D molecules to fill the target pocket cavity with high binding affinities, significantly outperforming previous baselines.
CCMB: A Large-scale Chinese Cross-modal Benchmark
In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks.
Ranked #3 on
Image Retrieval
on Flickr30k-CN
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment
Basically, there are two main methods for SLU tasks: (1) Two-stage method, which uses a speech model to transfer speech to text, then uses a language model to get the results of downstream tasks; (2) One-stage method, which just fine-tunes a pre-trained speech model to fit in the downstream tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques
In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning (ML) tools to improve the motor decoding accuracy at the level of individual fingers.
