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Found 16 papers, 6 papers with code
MEDPNet: Achieving High-Precision Adaptive Registration for Complex Die Castings
Due to their complex spatial structure and diverse geometric features, achieving high-precision and robust point cloud registration for complex Die Castings has been a significant challenge in the die-casting industry.
A unified Bayesian framework for interval hypothesis testing in clinical trials
The American Statistical Association (ASA) statement on statistical significance and P-values \cite{wasserstein2016asa} cautioned statisticians against making scientific decisions solely on the basis of traditional P-values.
AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
We also revisit the evaluation protocol introduced by previous works and identify a limitation in this protocol that leads to an artificially high pass rate.
General Automatic Solution Generation of Social Problems
Given the escalating intricacy and multifaceted nature of contemporary social systems, manually generating solutions to address pertinent social issues has become a formidable task.
Spiking Structured State Space Model for Monaural Speech Enhancement
Speech enhancement seeks to extract clean speech from noisy signals.
Snow Removal for LiDAR Point Clouds with Spatio-temporal Conditional Random Fields
The proposed approach first constructs the CRF based on k-nearest neighbors with the snow confidence derived from the physical priors of snow, such as intensity and distribution.
Teeth And Root Canals Segmentation Using ZXYFormer With Uncertainty Guidance And Weight Transfer
In addition, there are weak edges at the tooth, between tooth and root canal, which makes it very difficult to segment such weak edges.
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models.
Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations
In this paper, we propose a new framework to boost ZSL by explicitly learning attribute prototypes beyond images and contrastively optimizing them with attribute-level features within images.
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
The key characteristic of these ViT models is to adopt different aggregation strategies of spatial patch information within the artificial neural networks (ANNs).
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model
In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model.
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way.
LaMDA: Language Models for Dialog Applications
We demonstrate that fine-tuning with annotated data and enabling the model to consult external knowledge sources can lead to significant improvements towards the two key challenges of safety and factual grounding.
Ranked #114 on
Code Generation
on HumanEval
Online Ordering Platform City Distribution Based on Genetic Algorithm
The results show that our model and algorithm can design the vehicle path superior to the original model in terms of distribution cost and delivery time, thus providing decision support for the M platform to save distribution cost in urban distribution in the future.
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks
To develop a better deep model for blood glucose forecasting, we analyze the data and detect important patterns.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.
