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Found 14 papers, 7 papers with code
From Similarity to Superiority: Channel Clustering for Time Series Forecasting
Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM).
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Solving image inverse problems (e. g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image).
An Item is Worth a Prompt: Versatile Image Editing with Disentangled Control
Building on the success of text-to-image diffusion models (DPMs), image editing is an important application to enable human interaction with AI-generated content.
Efficient High-Resolution Time Series Classification via Attention Kronecker Decomposition
The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains.
Leveraging Large Language Models for Concept Graph Recovery and Question Answering in NLP Education
We assess LLMs' zero-shot performance in creating domain-specific concept graphs and introduce TutorQA, a new expert-verified NLP-focused benchmark for scientific graph reasoning and QA.
Integrating View Conditions for Image Synthesis
In the field of image processing, applying intricate semantic modifications within existing images remains an enduring challenge.
XDLM: Cross-lingual Diffusion Language Model for Machine Translation
Recently, diffusion models have excelled in image generation tasks and have also been applied to neural language processing (NLP) for controllable text generation.
HiPool: Modeling Long Documents Using Graph Neural Networks
Encoding long sequences in Natural Language Processing (NLP) is a challenging problem.
Diffuser: Efficient Transformers with Multi-hop Attention Diffusion for Long Sequences
Efficient Transformers have been developed for long sequence modeling, due to their subquadratic memory and time complexity.
Adaptive Graph Spatial-Temporal Transformer Network for Traffic Flow Forecasting
Traffic flow forecasting on graphs has real-world applications in many fields, such as transportation system and computer networks.
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
We also show that the trained graph filters in KerGNNs can reveal the local graph structures of the dataset, which significantly improves the model interpretability compared with conventional GNN models.
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed.
Energy-efficient and Robust Cumulative Training with Net2Net Transformation
We achieve this by first training a small network (with lesser parameters) on a small subset of the original dataset, and then gradually expanding the network using Net2Net transformation to train incrementally on larger subsets of the dataset.
