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Found 25 papers, 8 papers with code
Memorize or Generalize? Evaluating LLM Code Generation with Evolved Questions
Large Language Models (LLMs) are known to exhibit a memorization phenomenon in code generation: instead of truly understanding the underlying principles of a programming problem, they tend to memorize the original prompt and its solution together in the training.
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data
The ability of the proposed ADDGAN to learn realistic SOMs from noisy image data is demonstrated.
Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in Medical Image Denoising
However, such hand-crafted penalties may not be able to preserve task-relevant information in measured image data and can lead to oversmoothed image appearances and patchy artifacts that degrade signal detectability.
The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model
Based on our observations, we propose the Babel Tower Hypothesis, which describes the entire process of LLMs acquiring new language capabilities.
Understanding Particles From Video: Property Estimation of Granular Materials via Visuo-Haptic Learning
The generalization capability has also been evaluated and a real-world application on the beach is also demonstrated.
FullStack Bench: Evaluating LLMs as Full Stack Coders
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing.
MLAE: Masked LoRA Experts for Visual Parameter-Efficient Fine-Tuning
To address these issues, a natural idea is to enhance the independence and diversity of the learning process for the low-rank matrices.
User-Friendly Customized Generation with Multi-Modal Prompts
Text-to-image generation models have seen considerable advancement, catering to the increasing interest in personalized image creation.
Improve Cross-Architecture Generalization on Dataset Distillation
Dataset distillation, a pragmatic approach in machine learning, aims to create a smaller synthetic dataset from a larger existing dataset.
Unsupervised Generation of Pseudo Normal PET from MRI with Diffusion Model for Epileptic Focus Localization
In this study, we investigated unsupervised learning methods for unpaired MRI to PET translation for generating pseudo normal FDG PET for epileptic focus localization.
AmbientCycleGAN for Establishing Interpretable Stochastic Object Models Based on Mathematical Phantoms and Medical Imaging Measurements
Ideally, computation of task-based measures of IQ needs to account for all sources of randomness in the measurement data, including the variability in the ensemble of objects to be imaged.
Ambient-Pix2PixGAN for Translating Medical Images from Noisy Data
Recently, an augmented GAN architecture named AmbientGAN has been proposed that can be trained on noisy measurement data to synthesize high-quality clean medical images.
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
In this survey, we comprehensively review the existing evaluation protocols of multimodal reasoning, categorize and illustrate the frontiers of MLLMs, introduce recent trends in applications of MLLMs on reasoning-intensive tasks, and finally discuss current practices and future directions.
Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization
To enhance the efficiency of such a process, we propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model.
InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal Large Language Models
To mitigate this issue, we manually curate a benchmark dataset specifically designed for MLLMs, with a focus on complex reasoning tasks.
Semantic Prompt for Few-Shot Image Recognition
Instead of the naive exploitation of semantic information for remedying classifiers, we explore leveraging semantic information as prompts to tune the visual feature extraction network adaptively.
Coarse-to-Fine Covid-19 Segmentation via Vision-Language Alignment
Segmentation of COVID-19 lesions can assist physicians in better diagnosis and treatment of COVID-19.
Ranked #1 on
Medical Image Segmentation
on MosMedData
A deep learning based multimodal fusion model for skin lesion diagnosis using smartphone collected clinical images and metadata
The proposed model showed promising performance and could be a potential tool for skin cancer screening.
Ranked #3 on
Skin Lesion Classification
on PAD-UFES-20
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class.
Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency
In addition, to cope with new attacks in real-world deployment, we propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of unseen sample data and adapt the parameters of encoder.
Safe, efficient and socially-compatible decision of automated vehicles: a case study of unsignalized intersection driving
By investigating the causes of 4, 300 video clips of traffic accidents, we find that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents, especially in those involving trucks.
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images
Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class.
Quantization of Deep Neural Networks for Accurate Edge Computing
Deep neural networks (DNNs) have demonstrated their great potential in recent years, exceeding the per-formance of human experts in a wide range of applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Progressive Cluster Purification for Transductive Few-shot Learning
Furthermore, the inter-class classification and the intra-class transduction are extremely flexible to be repeated several times to progressively purify the clusters.
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
Nevertheless, how to effectively extract discriminative spatial and temporal features is still a challenging problem.
Ranked #64 on
Skeleton Based Action Recognition
on NTU RGB+D
