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Found 6 papers, 1 papers with code
Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
Although tables can be used as input to LLMs with serialization, there is a lack of comprehensive studies that examine whether LLMs can truly comprehend such data.
Approximating Probability Distributions by using Wasserstein Generative Adversarial Networks
Studied here are Wasserstein generative adversarial networks (WGANs) with GroupSort neural networks as their discriminators.
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization
Wide applications of differentiable two-player sequential games (e. g., image generation by GANs) have raised much interest and attention of researchers to study efficient and fast algorithms.
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks
Tabular data analysis is performed every day across various domains.
Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic Processes
In this paper, we propose an intention neural process model (INP) for user cold-start recommendation (i. e., user with very few historical interactions), a novel extension of the neural stochastic process family using a general meta learning strategy with intrinsic and extrinsic intention learning for robust user preference learning.
Modular Blended Attention Network for Video Question Answering
In multimodal machine learning tasks, it is due to the complexity of the assignments that the network structure, in most cases, is assembled in a sophisticated way.
