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Found 11 papers, 5 papers with code
SecGPT: An Execution Isolation Architecture for LLM-Based Systems
These LLM apps leverage the de facto natural language-based automated execution paradigm of LLMs: that is, apps and their interactions are defined in natural language, provided access to user data, and allowed to freely interact with each other and the system.
Mitigating Label Noise on Graph via Topological Sample Selection
Despite the success of the carefully-annotated benchmarks, the effectiveness of existing graph neural networks (GNNs) can be considerably impaired in practice when the real-world graph data is noisily labeled.
BMLP: Behavior-aware MLP for Heterogeneous Sequential Recommendation
In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying.
Online Continual Knowledge Learning for Language Models
In this paper, we introduce a novel problem in the realm of continual learning: Online Continual Knowledge Learning (OCKL).
SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Specifically, SiDA attains a remarkable speedup in MoE inference with up to 3. 93X throughput increasing, up to 75% latency reduction, and up to 80% GPU memory saving with down to 1% performance drop.
CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity
Roadside camera-driven 3D object detection is a crucial task in intelligent transportation systems, which extends the perception range beyond the limitations of vision-centric vehicles and enhances road safety.
Ranked #2 on
3D Object Detection
on Rope3D
Harnessing Scalable Transactional Stream Processing for Managing Large Language Models [Vision]
Large Language Models (LLMs) have demonstrated extraordinary performance across a broad array of applications, from traditional language processing tasks to interpreting structured sequences like time-series data.
Making Binary Classification from Multiple Unlabeled Datasets Almost Free of Supervision
Training a classifier exploiting a huge amount of supervised data is expensive or even prohibited in a situation, where the labeling cost is high.
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation.
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural Prompts
The field of text-to-image generation has made remarkable strides in creating high-fidelity and photorealistic images.
SlowLiDAR: Increasing the Latency of LiDAR-Based Detection Using Adversarial Examples
LiDAR-based perception is a central component of autonomous driving, playing a key role in tasks such as vehicle localization and obstacle detection.
