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Found 17 papers, 6 papers with code
An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science
We propose a novel analyst-inspector framework to automatically evaluate and enforce the reproducibility of LLM-generated data science workflows - the first rigorous approach to the best of our knowledge.
Crabs: Consuming Resource via Auto-generation for LLM-DoS Attack under Black-box Settings
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks yet still are vulnerable to external threats, particularly LLM Denial-of-Service (LLM-DoS) attacks.
SAKA: An Intelligent Platform for Semi-automated Knowledge Graph Construction and Application
We prove the feasibility of the semi-automatic KG construction method on the SAKA platform.
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
General intelligence requires quick adaption across tasks.
SimpleFusion: A Simple Fusion Framework for Infrared and Visible Images
Integrating visible and infrared images into one high-quality image, also known as visible and infrared image fusion, is a challenging yet critical task for many downstream vision tasks.
X-MLP: A Patch Embedding-Free MLP Architecture for Vision
However, existing vision MLP architectures always depend on convolution for patch embedding.
Iterative-in-Iterative Super-Resolution Biomedical Imaging Using One Real Image
Deep learning-based super-resolution models have the potential to revolutionize biomedical imaging and diagnoses by effectively tackling various challenges associated with early detection, personalized medicine, and clinical automation.
Recognizable Information Bottleneck
The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound.
Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach
With the increasing proportion of renewable energy in the generation side, it becomes more difficult to accurately predict the power generation and adapt to the large deviations between the optimal dispatch scheme and the day-ahead scheduling in the process of real-time dispatch.
Deep Generative Mixture Model for Robust Imbalance Classification
In this paper, a deep generative classifier is proposed to mitigate this issue via both model perturbation and data perturbation.
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning
In this paper, we propose an evaluation approach to analyze the performance of RL agents in a look-ahead economic dispatch scheme.
Learning domain-specific causal discovery from time series
Causal discovery (CD) from time-varying data is important in neuroscience, medicine, and machine learning.
Neural Networks as Paths through the Space of Representations
Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to understand.
Interpretable Image Recognition by Constructing Transparent Embedding Space
This plug-in embedding space is spanned by transparent basis concepts which are constructed on the Grassmann manifold.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
Deep Generative Model for Robust Imbalance Classification
In this paper, a deep generative classifier is proposed to mitigate this issue via both data perturbation and model perturbation.
Cantonese Automatic Speech Recognition Using Transfer Learning from Mandarin
We propose a system to develop a basic automatic speech recognizer(ASR) for Cantonese, a low-resource language, through transfer learning of Mandarin, a high-resource language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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