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Found 31 papers, 11 papers with code
Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding
Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy contexts and executing effective question answering.
Operating System And Artificial Intelligence: A Systematic Review
In the dynamic landscape of technology, the convergence of Artificial Intelligence (AI) and Operating Systems (OS) has emerged as a pivotal arena for innovation.
Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression
Humans can retain old knowledge while learning new information, but Large Language Models (LLMs) often suffer from catastrophic forgetting when post-pretrained or supervised fine-tuned (SFT) on domain-specific data.
DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models
In this paper, we introduce DP-MemArc, a novel training framework aimed at reducing the memory costs of large language models while emphasizing the protection of user data privacy.
NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
In addition to amino acid-level and peptide-level precision and recall, we evaluate the models' performance in terms of identifying post-tranlational modifications (PTMs), efficiency and robustness to peptide length, noise peaks and missing fragment ratio, which are important influencing factors while seldom be considered.
HORAE: A Domain-Agnostic Modeling Language for Automating Multimodal Service Regulation
Artificial intelligence is rapidly encroaching on the field of service regulation.
Tool-Planner: Task Planning with Clusters across Multiple Tools
Experiments show that our approach demonstrates a high pass and win rate across different datasets and optimizes the planning scheme for tool learning in models such as GPT-4 and Claude 3, showcasing the potential of our method.
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory
In this paper, we introduce a novel data-free federated class incremental learning framework with diffusion-based generative memory (DFedDGM) to mitigate catastrophic forgetting by generating stable, high-quality images through diffusion models.
SkinGEN: an Explainable Dermatology Diagnosis-to-Generation Framework with Interactive Vision-Language Models
With the continuous advancement of vision language models (VLMs) technology, remarkable research achievements have emerged in the dermatology field, the fourth most prevalent human disease category.
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity
In this paper, we improve the one-shot sequential federated learning for non-IID data by proposing a local model diversity-enhancing strategy.
TransLinkGuard: Safeguarding Transformer Models Against Model Stealing in Edge Deployment
Specifically, we identify four critical protection properties that existing methods fail to simultaneously satisfy: (1) maintaining protection after a model is physically copied; (2) authorizing model access at request level; (3) safeguarding runtime reverse engineering; (4) achieving high security with negligible runtime overhead.
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
Large language models (LLMs) demonstrate exceptional performance in numerous tasks but still heavily rely on knowledge stored in their parameters.
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis
Large language models (LLMs) have achieved commendable accomplishments in various natural language processing tasks.
Post-Training Attribute Unlearning in Recommender Systems
The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers.
GroundVLP: Harnessing Zero-shot Visual Grounding from Vision-Language Pre-training and Open-Vocabulary Object Detection
Visual grounding, a crucial vision-language task involving the understanding of the visual context based on the query expression, necessitates the model to capture the interactions between objects, as well as various spatial and attribute information.
Benchmarking Sequential Visual Input Reasoning and Prediction in Multimodal Large Language Models
Multimodal large language models (MLLMs) have shown great potential in perception and interpretation tasks, but their capabilities in predictive reasoning remain under-explored.
Federated Large Language Model: A Position Paper
Large scale language models (LLM) have received significant attention and found diverse applications across various domains, but their development encounters challenges in real-world scenarios.
Federated Learning on Non-iid Data via Local and Global Distillation
In this work, we propose FedND: federated learning with noise distillation.
RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing
Moreover, we present an image-text paired dataset in the field of remote sensing (RS), RS5M, which has 5 million RS images with English descriptions.
Ranked #1 on
Text Retrieval
on RSICD
(using extra training data)
EasySpider: A No-Code Visual System for Crawling the Web
As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research.
FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases
Thus, in this paper, we propose FreeEagle, the first data-free backdoor detection method that can effectively detect complex backdoor attacks on deep neural networks, without relying on the access to any clean samples or samples with the trigger.
Protecting Split Learning by Potential Energy Loss
In this paper, we focus on the privacy leakage from the forward embeddings of split learning.
"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution
Experiment results show that the MeTFA-smoothed explanation can significantly increase the robust faithfulness.
VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations
Inspired by the CheckList for testing natural language processing, we exploit VL-CheckList, a novel framework to understand the capabilities of VLP models.
Differentiable and Scalable Generative Adversarial Models for Data Imputation
DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model.
Backdoor Pre-trained Models Can Transfer to All
However, a pre-trained model with backdoor can be a severe threat to the applications.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
Joint Partial Optimal Transport for Open Set Domain Adaptation
However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes.
A Latent Feelings-aware RNN Model for User Churn Prediction with Behavioral Data
User churn needs to be predicted with only behavioral data.
Service Wrapper: a system for converting web data into web services
In this work, we construct a service wrapper system to convert available data in web pages into web services.
Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images
As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images.
