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Found 11 papers, 4 papers with code
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
BCAmirs at SemEval-2024 Task 4: Beyond Words: A Multimodal and Multilingual Exploration of Persuasion in Memes
Memes, combining text and images, frequently use metaphors to convey persuasive messages, shaping public opinion.
An Information-Theoretic Framework for Out-of-Distribution Generalization
We study the Out-of-Distribution (OOD) generalization in machine learning and propose a general framework that provides information-theoretic generalization bounds.
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models
While previous works have explored image generation conditioned on scene graphs or layouts, our task is distinctive and important as it involves generating scene graphs themselves unconditionally from noise, enabling efficient and interpretable control for image generation.
Noisy Computing of the $\mathsf{OR}$ and $\mathsf{MAX}$ Functions
We consider the problem of computing a function of $n$ variables using noisy queries, where each query is incorrect with some fixed and known probability $p \in (0, 1/2)$.
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data.
On the Optimal Bounds for Noisy Computing
However, the upper and lower bounds do not match in terms of the dependence on $\delta$ and $p$.
Specformer: Spectral Graph Neural Networks Meet Transformers
To tackle these issues, we introduce Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain, leading to a learnable set-to-set spectral filter.
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation
Edge Computing (EC) offers a superior user experience by positioning cloud resources in close proximity to end users.
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Models
In this paper, we propose a novel adversarial en-semble training approach that jointly learns the label dependencies and member models.
Soft BIBD and Product Gradient Codes
In this paper, we aim to overcome such limitations and construct gradient codes which exist for a wide range of system parameters while retaining the superior performance of BIBD gradient codes.
