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Found 17 papers, 4 papers with code
HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights
While monomer protein structure prediction tools boast impressive accuracy, the prediction of protein complex structures remains a daunting challenge in the field.
Efficient Image Super-Resolution via Symmetric Visual Attention Network
An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms.
Ranked #53 on
Image Super-Resolution
on Set14 - 4x upscaling
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies.
Controlling Neural Style Transfer with Deep Reinforcement Learning
Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters.
Image-to-Image Translation with Deep Reinforcement Learning
The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.
In-context Autoencoder for Context Compression in a Large Language Model
We propose the In-context Autoencoder (ICAE), leveraging the power of a large language models (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes.
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design
In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations.
Extensible Prompts for Language Models on Zero-shot Language Style Customization
We propose eXtensible Prompt (X-Prompt) for prompting a large language model (LLM) beyond natural language (NL).
TCR: A Transformer Based Deep Network for Predicting Cancer Drugs Response
Our study highlights the prediction power of TCR and its potential value for cancer drug repurpose and precision oncology treatment.
Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering
To attain this goal, each time series is first assigned the forecast for its cluster representative, which can be considered as a "shrinkage prior" for the set of time series it represents.
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series
We conduct a comprehensive evaluation of both point and quantile forecasts for hierarchical time series (HTS), including public data and user records from a large financial software company.
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces.
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
Feature-based Recognition Framework for Super-resolution Images
In practical application, the performance of recognition network usually decreases when being applied on super-resolution images.
MECATS: Mixture-of-Experts for Probabilistic Forecasts of Aggregated Time Series
We introduce a mixture of heterogeneous experts framework called MECATS, which simultaneously forecasts the values of a set of time series that are related through an aggregation hierarchy.
Koopman analysis in oscillator synchronization
Synchronization is an important dynamical phenomenon in coupled nonlinear systems, which has been studied extensively in recent years.
Chaotic Dynamics
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work.
