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Found 9 papers, 6 papers with code
Deep Neural Network-Based Prediction of B-Cell Epitopes for SARS-CoV and SARS-CoV-2: Enhancing Vaccine Design through Machine Learning
The accurate prediction of B-cell epitopes is critical for guiding vaccine development against infectious diseases, including SARS and COVID-19.
Reinterpreting PID Controller From the Perspective of State Feedback and Lumped Disturbance Compensation
This paper analyzes the motion of solutions to non-homogeneous linear differential equations.
Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
The availability of high-quality data is one of the most important factors in improving the reasoning capability of LLMs.
FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information Disclosure
This paper builds a benchmark FinTruthQA, that can evaluate advanced natural language processing (NLP) techniques for the automatic quality assessment of information disclosure in financial Q&A data.
SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks
Based on DSSA, we propose a novel spiking Vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters.
LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model
Extensive experimental results have demonstrated that our model can outperform previous state-of-the-art works on various types of datasets, which promote SNNs to achieve a brand-new level of performance comparable to quantized ANNs.
Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation
A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support images.
Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration
In this study, we propose a novel Deformer module along with a multi-scale framework for the deformable image registration task.
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
Unlike image-space methods, our FeMaSR restores HR images by matching distorted LR image {\it features} to their distortion-free HR counterparts in our pretrained HR priors, and decoding the matched features to obtain realistic HR images.
