A comprehensive survey and a taxonomy of state-of-the-art open LLMs and AIFMs for clinical tasks and a subjective evaluation of LLM-driven personalized prescriptions are provided, highlighting the importance of responsible and transparent deployment in healthcare contexts.
Maximum a posteriori inference in a finite Markov random field is generally NP-hard. We study an instance-wise optimizer that minimizes the exact multilinear energy under product marginals. The canonical parameterization adds a GNN output to free per-node logits. This residual bypass is a global change of coordinates,...
Yaomin Wang, Chaolong Ying, Xiaodong Luo et al.· 0 citations
Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA might not be as complex as we think, as the way they are built distorts our perception of progress in this field. For example, we find that in these ben...
Cosimo Gregucci, Bo Xiong, Daniel Hernandez et al.· 0 citations
Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation...
Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu et al.· 0 citations
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Rating-based human evaluation has become an essential tool to accurately evaluate the impressive performance of large language models (LLMs). However, current rating systems suffer from several important limitations: first, they fail to account for biases that significantly influence evaluation results, second, they re...
Jasper Dekoninck, Maximilian Baader, Martin Vechev· 0 citations
Due to the ongoing electrification of transportation systems, energy-based routing problems such as the Electric Vehicle Routing Problem (EVRP) are becoming increasingly important. Unlike Euclidean routing problems, the EVRP involves asymmetric energy costs caused by road topology. Existing learning-based routing appro...
Attila Lischka, Filip Rydin, Jiaming Wu et al.· 0 citations
MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing parameters. The learning sparse gating network selects a set of experts for each token to be processed; however, this may lead to differences in the number of tokens processed by...
Peizhuang Cong, Aomufei Yuan, Shimao Chen et al.· 0 citations
This work is the first work to include hidden layer distillation for student MLP on graphs and to combine graph Positional Encoding with MLP, and is the first work to include hidden layer distillation for student MLP on graphs.
Junxian Li, Bin Shi, Er-Fei Cui et al.· arXiv.org· 1 citation
Network traffic analysis in cloud and internet service provider (ISP) networks increasingly relies on machine learning (ML) models, as encryption and service consolidation make rule-based classification unreliable. At high flow-arrival rates, however, new flows can arrive faster than end-to-end analysis pipelines can p...
Shinan Liu, Ted Shaowang, Gerry Wan et al.· 0 citations
This paper considers the setting in which local Mixture-of-experts (MoE) models are fitted independently on distributed datasets and must be combined into a single global MoE model. Weighted averaging the local mixtures, whose experts are not aligned across machines, often yields a large mixture with many experts inste...
Diffusion models have shown promising results for a wide range of generative tasks with continuous data, such as image and audio synthesis. However, little progress has been made on using diffusion models to generate symbolic music since this class of generative models struggles with discrete data while its iterative s...
Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However, the application of LLMs in the recommendation domain has not been thoroughly investigated. To bridge this gap, we propose LLMRec, a LLM-bas...
Junling Liu, Chao Liu, Peilin Zhou et al.· 0 citations
