1 citations · 1 across the 2 of their papers we have counts for
7 papers
Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs
Alireza A. Safaei, Laura M. Vowels, Matthew J. Vowels +2
The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environmental cost. In this paper, we e…
psifx -- Psychological and Social Interactions Feature Extraction Package
Guillaume Rochette, Mathieu Rochat, Nizar Michaud +1
psifx is a plug-and-play multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning techniques for human sciences res…
Sequences as Nodes for Contrastive Multimodal Graph Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
To tackle cold-start and data sparsity issues in recommender systems, numerous multimodal, sequential, and contrastive techniques have been proposed. While these augmentations can…
Multimodal Enhancement of Sequential Recommendation
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
We propose a novel recommender framework, MuSTRec (Multimodal and Sequential Transformer-based Recommendation), that unifies multimodal and sequential recommendation paradigms. MuS…
DSL: Understanding and Improving Softmax Recommender Systems with Competition-Aware Scaling
Bucher Sahyouni, Matthew Vowels, Liqun Chen +1
Softmax Loss (SL) is being increasingly adopted for recommender systems (RS) as it has demonstrated better performance, robustness and fairness. Yet in implicit-feedback, a single…
CaTs and DAGs: Integrating Directed Acyclic Graphs with Transformers for Causally Constrained Predictions
Matthew J. Vowels, Mathieu Rochat, Sina Akbari
Artificial Neural Networks (ANNs), including fully-connected networks and transformers, are highly flexible and powerful function approximators, widely applied in fields like compu…