Search Results for author:
Found 10 papers, 5 papers with code
Just Say What You Want: Only-prompting Self-rewarding Online Preference Optimization
We address the challenge of online Reinforcement Learning from Human Feedback (RLHF) with a focus on self-rewarding alignment methods.
Data Contamination Report from the 2024 CONDA Shared Task
The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models.
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation
We tackle the novel class discovery in point cloud segmentation, which discovers novel classes based on the semantic knowledge of seen classes.
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions.
Benchmarking Benchmark Leakage in Large Language Models
By analyzing 31 LLMs under the context of mathematical reasoning, we reveal substantial instances of training even test set misuse, resulting in potentially unfair comparisons.
Novel Class Discovery for Long-tailed Recognition
In this paper, we consider a more realistic setting for novel class discovery where the distributions of novel and known classes are long-tailed.
Class-relation Knowledge Distillation for Novel Class Discovery
In addition, to enable a flexible knowledge distillation scheme for each data point in novel classes, we develop a learnable weighting function for the regularization, which adaptively promotes knowledge transfer based on the semantic similarity between the novel and known classes.
Mutual Information-guided Knowledge Transfer for Novel Class Discovery
Our insight is to utilize mutual information to measure the relation between seen classes and unseen classes in a restricted label space and maximizing mutual information promotes transferring semantic knowledge.
CdtGRN: Construction of qualitative time-delayed gene regulatory networks with a deep learning method
Therefore, it is of great significance to elucidate the regulation mechanism over time points.
Visual Anomaly Detection for Images: A Survey
Visual anomaly detection is an important and challenging problem in the field of machine learning and computer vision.
