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Found 10 papers, 3 papers with code
Clause Final Verb Prediction in Hindi: Evidence for Noisy Channel Model of Communication
The models differ in their use of prior context during the prediction process – the context is either noisy or noise-free.
What Determines the Order of Verbal Dependents in Hindi? Effects of Efficiency in Comprehension and Production
In this work, we investigate whether the order and relative distance of preverbal dependents in Hindi, an SOV language, is affected by factors motivated by efficiency considerations during comprehension/production.
Mysterious Projections: Multimodal LLMs Gain Domain-Specific Visual Capabilities Without Richer Cross-Modal Projections
It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models.
A Survey on Explainability of Graph Neural Networks
Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems.
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs
We find that one reason for degradation is the collapse of latents for each class in the $\mathcal{W}$ latent space.
Ranked #1 on
Conditional Image Generation
on ImageNet-LT
A Survey of Graph Neural Networks for Social Recommender Systems
In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis).
Representation Learning in Continuous-Time Dynamic Signed Networks
Modeling such dynamics of signed networks is crucial to understanding the evolution of polarization in the network and enabling effective prediction of the signed structure (i. e., link signs and signed weights) in the future.
Task and Model Agnostic Adversarial Attack on Graph Neural Networks
In this work, we study this problem and show that GNNs remain vulnerable even when the downstream task and model are unknown.
Hiding Data in Images Using Cryptography and Deep Neural Network
Previously, steganography has been combined with cryptography and neural networks separately.
