Search Results for author:
Found 19 papers, 7 papers with code
Uni-SMART: Universal Science Multimodal Analysis and Research Transformer
Scientific literature often includes a wide range of multimodal elements, such as molecular structure, tables, and charts, 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 natural language understanding and generation, igniting a surge of interest in leveraging these technologies in the field of 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.
Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
Recently deep learning based quantitative structure-activity relationship (QSAR) models has shown surpassing performance than traditional methods for property prediction tasks in drug discovery.
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+
Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive methods such as RDKit.
Do Deep Learning Methods Really Perform Better in Molecular Conformation Generation?
However, to our surprise, we design a simple and cheap algorithm (parameter-free) based on the traditional methods and find it is comparable to or even outperforms deep learning based MCG methods in the widely used GEOM-QM9 and GEOM-Drugs benchmarks.
Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?
What's more, they claim to perform better than traditional molecular docking, but the approach of comparison is not fair, since traditional methods are not designed for docking on the whole protein without a given pocket.
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).
Uni-Mol: A Universal 3D Molecular Representation Learning Framework
Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data.
Ranked #1 on
Molecular Property Prediction
on MUV
Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study
We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
This paper models these structures by presenting PredRNN, a new recurrent network, in which a pair of memory cells are explicitly decoupled, operate in nearly independent transition manners, and finally form unified representations of the complex environment.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks
In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.
Dynamic Graph Representation Learning with Fourier Temporal State Embedding
In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.
Why Do Neural Response Generation Models Prefer Universal Replies?
Recent advances in sequence-to-sequence learning reveal a purely data-driven approach to the response generation task.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
We present PredRNN++, an improved recurrent network for video predictive learning.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously.
Ranked #6 on
Video Prediction
on Human3.6M
Full-reference image quality assessment-based B-mode ultrasound image similarity measure
During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically.
