6 citations · 11 across the 9 of their papers we have counts for
9 papers
Accelerating NeurASP with vectorization and caching
Alexander Philipp Rader, Alessandra Russo
Neurosymbolic AI combines neural networks with symbolic programs to create robust and explainable predictions. One such framework is NeurASP, which trains a neural network to predi…
Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policies
Kexin Gu Baugh, Luke Dickens, Alessandra Russo
Although deep reinforcement learning has been shown to be effective, the model's black-box nature presents barriers to direct policy interpretation. To address this problem, we pro…
NeuralFastLAS: Fast Logic-Based Learning from Raw Data
Theo Charalambous, Yaniv Aspis, Alessandra Russo
Symbolic rule learners generate interpretable solutions, however they require the input to be encoded symbolically. Neuro-symbolic approaches overcome this issue by mapping raw dat…
Reasoning over the Behaviour of Objects in Video-Clips for Adverb-Type Recognition
Amrit Diggavi Seshadri, Alessandra Russo
In this work, following the intuition that adverbs describing scene-sequences are best identified by reasoning over high-level concepts of object-behavior, we propose the design of…
Towards preserving word order importance through Forced Invalidation
Hadeel Al-Negheimish, Pranava Madhyastha, Alessandra Russo
Large pre-trained language models such as BERT have been widely used as a framework for natural language understanding (NLU) tasks. However, recent findings have revealed that pre-…
Neuro-symbolic Rule Learning in Real-world Classification Tasks
Kexin Gu Baugh, Nuri Cingillioglu, Alessandra Russo
Neuro-symbolic rule learning has attracted lots of attention as it offers better interpretability than pure neural models and scales better than symbolic rule learning. A recent ap…
Sparse Relational Reasoning with Object-Centric Representations
Alex F. Spies, Alessandra Russo, Murray Shanahan
We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsi…
Automatic Concept Extraction for Concept Bottleneck-based Video Classification
Jeya Vikranth Jeyakumar, Luke Dickens, Luis Garcia +7
Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reaso…
Neuro-Symbolic Learning of Answer Set Programs from Raw Data
Daniel Cunnington, Mark Law, Jorge Lobo +1
One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims…