[HTML][HTML] Deep learning for low-data drug discovery: hurdles and opportunities
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to
protein structure prediction and synthesis planning. However, it is often challenged by the …
protein structure prediction and synthesis planning. However, it is often challenged by the …
Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging
Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over
the last few years. While the technical developments are manifold, less focus has been …
the last few years. While the technical developments are manifold, less focus has been …
Causality-driven one-shot learning for prostate cancer grading from mri
In this paper, we present a novel method for the automatic classification of medical images
that learns and leverages weak causal signals in the image. Our framework consists of a …
that learns and leverages weak causal signals in the image. Our framework consists of a …
Allsim: Simulating and benchmarking resource allocation policies in multi-user systems
Numerous real-world systems, ranging from healthcare to energy grids, involve users
competing for finite and potentially scarce resources. Designing policies for resource …
competing for finite and potentially scarce resources. Designing policies for resource …
Interpretable causal-based temporal graph convolutional network framework in complex spatio-temporal systems for CCUS-EOR
Global climate change has escalated in recent years. Carbon dioxide capture, enhanced oil
recovery (EOR)-utilization and storage (CCUS-EOR) has the potential to significantly …
recovery (EOR)-utilization and storage (CCUS-EOR) has the potential to significantly …
The role of causality in explainable artificial intelligence
Causality and eXplainable Artificial Intelligence (XAI) have developed as separate fields in
computer science, even though the underlying concepts of causation and explanation share …
computer science, even though the underlying concepts of causation and explanation share …
Improving causal reasoning in large language models: A survey
Causal reasoning (CR) is a crucial aspect of intelligence, essential for problem-solving,
decision-making, and understanding the world. While large language models (LLMs) can …
decision-making, and understanding the world. While large language models (LLMs) can …
Cohabitation of intelligence and systems: Towards self-reference in digital anatomies
The modern computing scenario of the Computing Continuum exhibits large and complex
applications with heterogeneous requirements running on distributed infrastructure. Still …
applications with heterogeneous requirements running on distributed infrastructure. Still …
Do not marginalize mechanisms, rather consolidate!
Structural causal models (SCMs) are a powerful tool for understanding the complex causal
relationships that underlie many real-world systems. As these systems grow in size, the …
relationships that underlie many real-world systems. As these systems grow in size, the …
Human Motion Prediction: Assessing Direct and Geometry-Aware Approaches in 3D Space
Predicting 3D human motion is a complex task, owing to the unpredictable nature of human
movements. The influx of deep learning innovations and the availability of extensive …
movements. The influx of deep learning innovations and the availability of extensive …