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Found 17 papers, 5 papers with code
Enhancing Self-Attention with Knowledge-Assisted Attention Maps
Large-scale pre-trained language models have attracted extensive attentions in the research community and shown promising results on various tasks of natural language processing.
Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems.
Exploring Neuron Interactions and Emergence in LLMs: From the Multifractal Analysis Perspective
Prior studies on the emergence in large models have primarily focused on how the functional capabilities of large language models (LLMs) scale with model size.
Prompting with Divide-and-Conquer Program Makes Large Language Models Discerning to Hallucination and Deception
Foundation models, such as Large language Models (LLMs), have attracted significant amount of interest due to their large number of applications.
TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
The past decade has witnessed significant advances in time series modeling with deep learning.
Neuro-Inspired Hierarchical Multimodal Learning
Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world.
Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model
Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions.
Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations
Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes.
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc.
Estimating Treatment Effects in Continuous Time with Hidden Confounders
Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making.
SVGformer: Representation Learning for Continuous Vector Graphics Using Transformers
Advances in representation learning have led to great success in understanding and generating data in various domains.
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods.
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
To address these issues, in this paper, we build up a causal framework that model the causal effect of misinformation from the perspective of temporal point process.
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results.
BIG-bench Machine Learning
Physics-informed machine learning
Spectral Temporal Graph Neural Network for Trajectory Prediction
To this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which can capture inter-agent correlations and temporal dependency simultaneously in frequency domain in addition to time domain.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Multivariate Time-series Anomaly Detection via Graph Attention Network
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
