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Found 16 papers, 9 papers with code
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process.
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation.
Multi-Ship Tracking by Robust Similarity metric
The low frame rates and severe image shake caused by wave turbulence in ship datasets often result in minimal, or even zero, Intersection of Union (IoU) between the predicted and detected bounding boxes.
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data
Discovering genes with similar functions across diverse biomedical contexts poses a significant challenge in gene representation learning due to data heterogeneity.
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain.
Explicit Planning Helps Language Models in Logical Reasoning
In this paper, we propose LEAP, a novel system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure.
eQTL Studies: from Bulk Tissues to Single Cells
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant.
Robustness of Learning from Task Instructions
To our knowledge, this is the first work that systematically studies how robust a PLM is when it is supervised by instructions with different factors of variability.
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Our tiny-attention adapter learns to modify the hidden states at each position directly conditioned on the hidden states at all the other positions, which is missed by the previously proposed adapters.
A general kernel boosting framework integrating pathways for predictive modeling based on genomic data
Predictive modeling based on genomic data has gained popularity in biomedical research and clinical practice by allowing researchers and clinicians to identify biomarkers and tailor treatment decisions more efficiently.
A Manifold Proximal Linear Method for Sparse Spectral Clustering with Application to Single-Cell RNA Sequencing Data Analysis
This paper considers a widely adopted model for SSC, which can be formulated as an optimization problem over the Stiefel manifold with nonsmooth and nonconvex objective.
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates
BoXHED 1. 0 is a novel tree-based implementation of the generic estimator proposed in Lee, Chen, Ishwaran (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner.
A pathway-based kernel boosting method for sample classification using genomic data
Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes.
Characterizing Spatiotemporal Transcriptome of Human Brain via Low Rank Tensor Decomposition
An application of our method to a spatiotemporal brain expression data provides insights on gene regulation patterns in the brain.
Methodology
Low-Rank Modeling and Its Applications in Image Analysis
In this paper, we review the recent advance of low-rank modeling, the state-of-the-art algorithms, and related applications in image analysis.
Posterior Contraction Rates of the Phylogenetic Indian Buffet Processes
The Indian buffet process (IBP) is such an example that can be used to define a prior distribution on infinite binary features, where the exchangeability among subjects is assumed.
