b'Mateja Jamnik'

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...

0 Navindu Leelarathna, et al. ∙

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 ...

0 Xiangjian Jiang, et al. ∙

research

∙ 06/02/2023

Evaluating Language Models for Mathematics through Interactions

The standard methodology of evaluating large language models (LLMs) base...

0 Katherine M. Collins, et al. ∙

research

∙ 04/27/2023

Interpretable Neural-Symbolic Concept Reasoning

Deep learning methods are highly accurate, yet their opaque decision pro...

0 Pietro Barbiero, et al. ∙

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...

0 Konstantin Hemker, et al. ∙

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 ...

7 Katherine M. Collins, et al. ∙

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...

7 Dmitry Kazhdan, et al. ∙

research

∙ 01/25/2023

Towards Robust Metrics for Concept Representation Evaluation

Recent work on interpretability has focused on concept-based explanation...

0 Mateo Espinosa Zarlenga, et al. ∙

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 ...

0 Andrei Margeloiu, et al. ∙

research

∙ 11/20/2022

Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery

By one of the most fundamental principles in physics, a dynamical system...

0 Yana Lishkova, et al. ∙

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...

0 Shea Cardozo, et al. ∙

research

∙ 11/11/2022

Graph-Conditioned MLP for High-Dimensional Tabular Biomedical Data

Genome-wide studies leveraging recent high-throughput sequencing technol...

0 Andrei Margeloiu, et al. ∙

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...

0 Albert Q. Jiang, et al. ∙

research

∙ 09/19/2022

Distributed representations of graphs for drug pair scoring

In this paper we study the practicality and usefulness of incorporating ...

0 Paul Scherer, et al. ∙

research

∙ 09/19/2022

Concept Embedding Models

Deploying AI-powered systems requires trustworthy models supporting effe...

0 Mateo Espinosa Zarlenga, et al. ∙

research

∙ 07/27/2022

Encoding Concepts in Graph Neural Networks

The opaque reasoning of Graph Neural Networks induces a lack of human tr...

0 Lucie Charlotte Magister, et al. ∙

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...

0 Daniel Raggi, et al. ∙

research

∙ 05/25/2022

Autoformalization with Large Language Models

Autoformalization is the process of automatically translating from natur...

0 Yuhuai Wu, et al. ∙

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...

0 Albert Q. Jiang, et al. ∙

research

∙ 11/24/2021

Efficient Decompositional Rule Extraction for Deep Neural Networks

In recent years, there has been significant work on increasing both inte...

0 Mateo Espinosa Zarlenga, et al. ∙

research

∙ 05/10/2021

Do Concept Bottleneck Models Learn as Intended?

Concept bottleneck models map from raw inputs to concepts, and then from...

0 Andrei Margeloiu, et al. ∙

research

∙ 04/18/2021

Failing Conceptually: Concept-Based Explanations of Dataset Shift

Despite their remarkable performance on a wide range of visual tasks, ma...

8 Maleakhi A. Wijaya, et al. ∙

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...

11 Dmitry Kazhdan, et al. ∙

research

∙ 12/13/2020

MEME: Generating RNN Model Explanations via Model Extraction

Recurrent Neural Networks (RNNs) have achieved remarkable performance on...

17 Dmitry Kazhdan, et al. ∙

research

∙ 12/02/2020

Improving Interpretability in Medical Imaging Diagnosis using Adversarial Training

We investigate the influence of adversarial training on the interpretabi...

20 Andrei Margeloiu, et al. ∙

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...

14 Maja Trȩbacz, et al. ∙

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...

0 Nicholas Quek Wei Kiat, et al. ∙

research

∙ 10/25/2020

Now You See Me (CME): Concept-based Model Extraction

Deep Neural Networks (DNNs) have achieved remarkable performance on a ra...

3 Dmitry Kazhdan, et al. ∙

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...

5 Paul Scherer, et al. ∙

research

∙ 09/19/2020

Learned Low Precision Graph Neural Networks

Deep Graph Neural Networks (GNNs) show promising performance on a range ...

0 Yiren Zhao, et al. ∙

research

∙ 06/19/2020

Abstract Diagrammatic Reasoning with Multiplex Graph Networks

Abstract reasoning, particularly in the visual domain, is a complex huma...

0 Duo Wang, et al. ∙

research

∙ 06/15/2020

Generalisable Relational Reasoning With Comparators in Low-Dimensional Manifolds

While modern deep neural architectures generalise well when test data is...

0 Duo Wang, et al. ∙

research

∙ 03/21/2020

Probabilistic Dual Network Architecture Search on Graphs

We present the first differentiable Network Architecture Search (NAS) fo...

0 Yiren Zhao, et al. ∙

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...

0 Paul Scherer, et al. ∙

research

∙ 09/18/2019

Bayesian Optimisation with Gaussian Processes for Premise Selection

Heuristics in theorem provers are often parameterised. Modern theorem pr...

14 Agnieszka Słowik, et al. ∙

research

∙ 03/14/2019

Unsupervised and interpretable scene discovery with Discrete-Attend-Infer-Repeat

In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a R...

0 Duo Wang, et al. ∙

Mateja Jamnik

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