The role of machine learning and design of experiments in the advancement of biomaterial and tissue engineering research
Optimisation of tissue engineering (TE) processes requires models that can identify
relationships between the parameters to be optimised and predict structural and …
relationships between the parameters to be optimised and predict structural and …
From machine learning to robotics: Challenges and opportunities for embodied intelligence
Machine learning has long since become a keystone technology, accelerating science and
applications in a broad range of domains. Consequently, the notion of applying learning …
applications in a broad range of domains. Consequently, the notion of applying learning …
Symbolic metaprogram search improves learning efficiency and explains rule learning in humans
Throughout their lives, humans seem to learn a variety of rules for things like applying
category labels, following procedures, and explaining causal relationships. These rules are …
category labels, following procedures, and explaining causal relationships. These rules are …
Interpretable and explainable logical policies via neurally guided symbolic abstraction
The limited priors required by neural networks make them the dominating choice to encode
and learn policies using reinforcement learning (RL). However, they are also black-boxes …
and learn policies using reinforcement learning (RL). However, they are also black-boxes …
A survey of reasoning with foundation models
Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …
world settings such as negotiation, medical diagnosis, and criminal investigation. It serves …
Human-like few-shot learning via bayesian reasoning over natural language
K Ellis - Advances in Neural Information Processing …, 2023 - proceedings.neurips.cc
A core tension in models of concept learning is that the model must carefully balance the
tractability of inference against the expressivity of the hypothesis class. Humans, however …
tractability of inference against the expressivity of the hypothesis class. Humans, however …
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
F Mumuni, A Mumuni - Cognitive Systems Research, 2024 - Elsevier
We review current and emerging knowledge-informed and brain-inspired cognitive systems
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
Explainable and interpretable machine learning and data mining
The growing number of applications of machine learning and data mining in many domains—
from agriculture to business, education, industrial manufacturing, and medicine—gave rise …
from agriculture to business, education, industrial manufacturing, and medicine—gave rise …
Scallop: A language for neurosymbolic programming
We present Scallop, a language which combines the benefits of deep learning and logical
reasoning. Scallop enables users to write a wide range of neurosymbolic applications and …
reasoning. Scallop enables users to write a wide range of neurosymbolic applications and …
Interpretable multimodal misinformation detection with logic reasoning
Multimodal misinformation on online social platforms is becoming a critical concern due to
increasing credibility and easier dissemination brought by multimedia content, compared to …
increasing credibility and easier dissemination brought by multimedia content, compared to …