From the 1 of 13 linked papers with an AI index.
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TEMPER: Tensorized Efficient Manifold-constrained Parameterization for Expressive Residual Routing
Yuxuan Gu, Wuyang Zhou, Huijun Xing +1
Residual connections rely on a static residual pathway, and are essential for training deep neural networks. Hyper-connections (HC) increase the expressivity of residual routing by…
TeRA: Vector-based Random Tensor Network for High-Rank Adaptation of Large Language Models
Yuxuan Gu, Wuyang Zhou, Giorgos Iacovides +1
Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), have significantly reduced the number of trainable parameters needed in fine-tuning large langua…
RefineBridge: Generative Bridge Models Improve Financial Forecasting by Foundation Models
Anthony Bolton, Wuyang Zhou, Zehua Chen +2
Financial time series forecasting is particularly challenging for transformer-based time series foundation models (TSFMs) due to non-stationarity, heavy-tailed distributions, and h…
Domain-Aware Tensor Network Structure Search
Giorgos Iacovides, Wuyang Zhou, Chao Li +2
Tensor networks (TNs) provide efficient representations of high-dimensional data, yet identification of the optimal TN structures, the so called tensor network structure search (TN…
Understanding the Rank of Tensor Networks via an Intuitive Example-Driven Approach
Wuyang Zhou, Giorgos Iacovides, Kriton Konstantinidis +2
Tensor Network (TN) decompositions have emerged as an indispensable tool in Big Data analytics owing to their ability to provide compact low-rank representations, thus alleviating…
Towards LLM-guided Efficient and Interpretable Multi-linear Tensor Network Rank Selection
Giorgos Iacovides, Wuyang Zhou, Danilo Mandic
We propose a novel framework that leverages large language models (LLMs) to guide the rank selection in tensor network models for higher-order data analysis. By utilising the intri…