research
∙
06/27/2023
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs
Variational Autoencoders and their many variants have displayed impressi...
share
research
∙
06/21/2023
ProtoGate: Prototype-based Neural Networks with Local Feature Selection for Tabular Biomedical Data
Tabular biomedical data poses challenges in machine learning because it ...
research
∙
06/02/2023
Evaluating Language Models for Mathematics through Interactions
The standard methodology of evaluating large language models (LLMs) base...
research
∙
04/27/2023
Interpretable Neural-Symbolic Concept Reasoning
Deep learning methods are highly accurate, yet their opaque decision pro...
research
∙
04/11/2023
CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs
Rule-based surrogate models are an effective and interpretable way to ap...
research
∙
03/22/2023
Human Uncertainty in Concept-Based AI Systems
Placing a human in the loop may abate the risks of deploying AI systems ...
research
∙
02/09/2023
GCI: A (G)raph (C)oncept (I)nterpretation Framework
Explainable AI (XAI) underwent a recent surge in research on concept ext...
research
∙
01/25/2023
Towards Robust Metrics for Concept Representation Evaluation
Recent work on interpretability has focused on concept-based explanation...
research
∙
11/28/2022
Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data
Tabular biomedical data is often high-dimensional but with a very small ...
research
∙
11/20/2022
Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery
By one of the most fundamental principles in physics, a dynamical system...
research
∙
11/14/2022
Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations
Recent work has suggested post-hoc explainers might be ineffective for d...
research
∙
11/11/2022
Graph-Conditioned MLP for High-Dimensional Tabular Biomedical Data
Genome-wide studies leveraging recent high-throughput sequencing technol...
research
∙
10/21/2022
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
The formalization of existing mathematical proofs is a notoriously diffi...
research
∙
09/19/2022
Distributed representations of graphs for drug pair scoring
In this paper we study the practicality and usefulness of incorporating ...
research
∙
09/19/2022
Concept Embedding Models
Deploying AI-powered systems requires trustworthy models supporting effe...
research
∙
07/27/2022
Encoding Concepts in Graph Neural Networks
The opaque reasoning of Graph Neural Networks induces a lack of human tr...
research
∙
06/07/2022
Representational Systems Theory: A Unified Approach to Encoding, Analysing and Transforming Representations
The study of representations is of fundamental importance to any form of...
research
∙
05/25/2022
Autoformalization with Large Language Models
Autoformalization is the process of automatically translating from natur...
research
∙
05/22/2022
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
In theorem proving, the task of selecting useful premises from a large l...
research
∙
11/24/2021
Efficient Decompositional Rule Extraction for Deep Neural Networks
In recent years, there has been significant work on increasing both inte...
research
∙
05/10/2021
Do Concept Bottleneck Models Learn as Intended?
Concept bottleneck models map from raw inputs to concepts, and then from...
research
∙
04/18/2021
Failing Conceptually: Concept-Based Explanations of Dataset Shift
Despite their remarkable performance on a wide range of visual tasks, ma...
research
∙
04/14/2021
Is Disentanglement all you need? Comparing Concept-based Disentanglement Approaches
Concept-based explanations have emerged as a popular way of extracting h...
research
∙
12/13/2020
MEME: Generating RNN Model Explanations via Model Extraction
Recurrent Neural Networks (RNNs) have achieved remarkable performance on...
research
∙
12/02/2020
Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training
We investigate the influence of adversarial training on the interpretabi...
research
∙
11/22/2020
Using ontology embeddings for structural inductive bias in gene expression data analysis
Stratifying cancer patients based on their gene expression levels allows...
research
∙
11/02/2020
Pairwise Relations Discriminator for Unsupervised Raven's Progressive Matrices
Abstract reasoning is a key indicator of intelligence. The ability to hy...
research
∙
10/25/2020
Now You See Me (CME): Concept-based Model Extraction
Deep Neural Networks (DNNs) have achieved remarkable performance on a ra...
research
∙
09/29/2020
Incorporating network based protein complex discovery into automated model construction
We propose a method for gene expression based analysis of cancer phenoty...
research
∙
09/19/2020
Learned Low Precision Graph Neural Networks
Deep Graph Neural Networks (GNNs) show promising performance on a range ...
research
∙
06/19/2020
Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Abstract reasoning, particularly in the visual domain, is a complex huma...
research
∙
06/15/2020
Generalisable Relational Reasoning With Comparators in Low-Dimensional Manifolds
While modern deep neural architectures generalise well when test data is...
research
∙
03/21/2020
Probabilistic Dual Network Architecture Search on Graphs
We present the first differentiable Network Architecture Search (NAS) fo...
research
∙
10/18/2019
Decoupling feature propagation from the design of graph auto-encoders
We present two instances, L-GAE and L-VGAE, of the variational graph aut...
research
∙
09/18/2019
Bayesian Optimisation with Gaussian Processes for Premise Selection
Heuristics in theorem provers are often parameterised. Modern theorem pr...
research
∙
03/14/2019
