Search Results for author:
Found 3 papers, 1 papers with code
TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data.
KwaiYiiMath: Technical Report
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning.
Ranked #88 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data
Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method.
