Latest AI and machine learning research in prescriptions for healthcare professionals.
Biomedical knowledge graphs (KGs) encode rich, structured information critical for drug discovery tasks, but extracting meaningful insights from large-scale KGs remains challenging due to their complex structure. Existing biomedical subgraph retrieval methods are tailored for graph neural networks (GNNs), limiting compatibility with other paradigms, including large language models (LLMs). We int...
Drug discovery is a complex and time-intensive process that requires identifying and validating new therapeutic candidates. Computational approaches using large-scale biomedical knowledge graphs (KGs) offer a promising solution to accelerate this process. However, extracting meaningful insights from large-scale KGs remains challenging due to the complexity of graph traversal. Existing subgraph-b...
Humanoid robots have shown success in locomotion and manipulation. Despite these basic abilities, humanoids are still required to quickly understand...
Large language models (LLMs) are revolutionizing healthcare by improving diagnosis, patient care, and decision support through interactive communica...
The effective and targeted provision of health information to consumers, specifically tailored to their needs and preferences, is indispensable in h...
The MinRank problem is a simple linear algebra problem: given matrices with coefficients in a field, find a non trivial linear combination of the ma...
Learning the unknown interactions that govern a quantum system is crucial for quantum information processing, device benchmarking, and quantum sensi...
Many e-commerce search pipelines have four stages, namely: retrieval, filtering, ranking, and personalized-reranking. The retrieval stage must be ef...
This paper investigates the performance of multimodal pre-trained models in user profiling tasks based on visual-linguistic demographic data. These ...
The integration of vision-language models into robotic systems constitutes a significant advancement in enabling machines to interact with their sur...
Large language models (LLMs) have been shown to propagate and even amplify gender bias, in English and other languages, in specific or constrained c...
We propose a novel model for the prediction of robot trajectories on rough offroad terrain from the onboard camera images. This model enforces the l...
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphi...
This paper presents a complete explainable system that interprets a set of data, abstracts the underlying features and describes them in a natural l...
Recommender systems are crucial for personalizing user experiences but often depend on implicit feedback data, which can be noisy and misleading. Ex...
Due to their excellent drug-like and pharmacokinetic properties, small molecule drugs are widely used to treat various diseases, making them a criti...
When an individual reports a negative interaction with some system, how can their personal experience be contextualized within broader patterns of s...
Clinical trials remain critical in cardiac drug development but face high failure rates due to efficacy limitations and safety risks, incurring subs...
Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robu...
Advanced Persistent Threats (APTs) are challenging to detect due to their complexity and stealth. To mitigate such attacks, many approaches utilize ...