13 accepted papers
Large Language Models (LLMs) have demonstrated impressive capabilities in code generation. Like human programmers, LLMs tend to call high-level APIs and libraries to program efficiently. However, this shortcut may hinder LLMs from learning the essential algorithm reasoning, leading instead to rote m
Large language models (LLMs) show significant improvement in code generation. A common practice is sampling multiple candidate codes to increase the likelihood of producing an accurate solution. However, effectively identifying the best candidate from the pool is a significant challenge. Although ex
Learned image compression (LIC) methods surpass traditional algorithms in rate-distortion (RD) performance, but still struggle to optimally balance effectiveness and efficiency. Moreover, although recent studies have demonstrated the effectiveness of utilizing frequency-domain information, they typi
Training large language models for code generation requires selecting high-quality data from solution pools where each problem admits multiple correct implementations. Conventional studies on data selection hold that sophisticated strategies that employ various optimization objectives, such as diver…
Learning-based lossless compressors have been validated to have competitive advantages in genomics data (GD) compression. However, learning-based GD-dedicated compressors typically need to be pre-trained on multi-source data and then are directly used to compress another target data, we denote them…
Few-Shot Relation Extraction (FSRE) aims to achieve high classification performance by training relation classification models with a small amount of labeled data. Prototypical networks serve as a straightforward and efficient method for optimizing model performance by combining similarity evaluatio…
Learning-based compression shows competitive compression ratios for genomics data. It often includes three types of compressors: static, adaptive and semi-adaptive. However, these existing compressors suffer from inferior compression ratios or throughput, and adaptive compressors also faces model c…
Video large language models (Video-LLMs) have made significant progress in understanding videos. However, processing multiple frames leads to lengthy visual token sequences, presenting challenges such as the limited context length cannot accommodate the entire video, and the inclusion of irrelevant…
Explosive growth of multi-source data (MSD) poses challenges in data transmitting and storing. Neural Network (NN)-based lossless compressors are an important type of compression approaches to alleviate these problems. However, existing NN-based lossless compressors suffer from poor compression rati…
Large Language Models (LLMs) have demonstrated exceptional performance in code generation, becoming increasingly vital for software engineering and development. Recently, Chain-of-Thought (CoT) has proven effective for complex tasks by prompting LLMs to reason step-by-step and provide a final answer…
Abductive Learning (ABL) is a promising framework for integrating sub-symbolic perception and logical reasoning through abduction. In this case, the abduction process provides supervision for the perception model from the background knowledge. Nevertheless, this process naturally contains uncertaint…
Discriminability and transferability are two goals of feature learning for domain adaptation (DA), as we aim to find the transferable features from the source domain that are helpful for discriminating the class label in the target domain. Modern DA approaches optimize discriminability and transfera…
In this paper, we address a special scenario of semi-supervised learning, where the label missing is caused by a preceding filtering mechanism, i.e., an instance can enter a subsequent process in which its label is revealed if and only if it passes the filtering mechanism. The rejected instances are…