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Found 13 papers, 7 papers with code
A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos
To address this gap, we propose the first jailbreak attack targeting LRMs, exploiting their unique vulnerabilities stemming from the advanced reasoning capabilities.
JAQ: Joint Efficient Architecture Design and Low-Bit Quantization with Hardware-Software Co-Exploration
The co-design of neural network architectures, quantization precisions, and hardware accelerators offers a promising approach to achieving an optimal balance between performance and efficiency, particularly for model deployment on resource-constrained edge devices.
JoyVASA: Portrait and Animal Image Animation with Diffusion-Based Audio-Driven Facial Dynamics and Head Motion Generation
Specifically, in the first stage, we introduce a decoupled facial representation framework that separates dynamic facial expressions from static 3D facial representations.
MHAD: Multimodal Home Activity Dataset with Multi-Angle Videos and Synchronized Physiological Signals
This non-contact, real-time monitoring method holds great potential for home settings.
Qwen2 Technical Report
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models.
Ranked #2 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Exploring the Potential of Large Language Models in Graph Generation
In this paper, we propose LLM4GraphGen to explore the ability of LLMs for graph generation with systematical task designs and extensive experiments.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
Identification of cancer-keeping genes as therapeutic targets by finding network control hubs
One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell from an arbitrary state to a designated state.
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
A Two-stream End-to-End Deep Learning Network for Recognizing Atypical Visual Attention in Autism Spectrum Disorder
In this study, we developed a novel two-stream deep learning network for this recognition based on 700 images and corresponding eye movement patterns of ASD and TD, and obtained an accuracy of 0. 95, which was higher than the previous state-of-the-art.
Super Interaction Neural Network
Recent studies have demonstrated that the convolutional networks heavily rely on the quality and quantity of generated features.
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning
In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.
