From the 1 of 11 linked papers with an AI index.
11 papers
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…
Beyond Co-Movement: Locality by Exposures Enables a Joint Factor-Graph Framework for Portfolio Diversification
Sara Chehab, Giorgos Iacovides, Parisa Yazdanparast +1
Current portfolio construction methods are either agnostic to the effects of idiosyncratic shocks (standard factor models) or to the latent data structure driving systematic return…
FinSMART: Financial Sentiment Analysis for Algorithmic Trading through Market-Aligned Reinforcement Learning
Giorgos Iacovides, Wuyang Zhou, Danilo Mandic
FinSMART is a reinforcement‑learning framework that trains financial sentiment analysis models directly on realized market outcomes, enabling adaptive sentiment signals for algorit…
Tensorizing Engram: Sharing Latents Across N-Gram Embeddings is Beneficial in LLMs
Wuyang Zhou, Yuxuan Gu, Giorgos Iacovides +3
Modern language models represent text using discrete token-level embeddings, which forces recurring multi-token patterns to be learned implicitly across Transformer layers. Both Ov…
KromHC: Manifold-Constrained Hyper-Connections with Kronecker-Product Residual Matrices
Wuyang Zhou, Yuxuan Gu, Giorgos Iacovides +1
The success of Hyper-Connections (HC) in neural networks (NN) has also highlighted issues related to training instability and restricted scalability. The Manifold-Constrained Hyper…
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…