55 citations · 186 across the 31 of their papers we have counts for
31 papers
An approach to melodic segmentation and classification based on filtering with the Haar-wavelet
Gissel Velarde, Tillman Weyde, David Meredith
We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wave…
Wavelet-Filtering of Symbolic Music Representations for Folk Tune Segmentation and Classification
Gissel Velarde, Tillman Weyde, David Meredith
The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet fil…
When to Accept Automated Predictions and When to Defer to Human Judgment?
Daniel Sikar, Artur Garcez, Tillman Weyde +2
Ensuring the reliability and safety of automated decision-making is crucial. It is well-known that data distribution shifts in machine learning can produce unreliable outcomes. Thi…
The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others
Daniel Sikar, Artur Garcez, Robin Bloomfield +6
This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM…
OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
Sevinj Teymurova, Ernesto Jiménez-Ruiz, Tillman Weyde +1
Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec…
Derivative-based regularization for regression
Enrico Lopedoto, Maksim Shekhunov, Vitaly Aksenov +2
In this work, we introduce a novel approach to regularization in multivariable regression problems. Our regularizer, called DLoss, penalises differences between the model's derivat…
Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe
Szymon Kubiak, Tillman Weyde, Oleksandr Galkin +2
We present a novel process for generating synthetic datasets tailored to assess asset allocation methods and construct portfolios within the fixed income universe. Our approach beg…
NeSy4VRD: A Multifaceted Resource for Neurosymbolic AI Research using Knowledge Graphs in Visual Relationship Detection
David Herron, Ernesto Jiménez-Ruiz, Giacomo Tarroni +1
NeSy4VRD is a multifaceted resource designed to support the development of neurosymbolic AI (NeSy) research. NeSy4VRD re-establishes public access to the images of the VRD dataset…
Theoretical Conditions and Empirical Failure of Bracket Counting on Long Sequences with Linear Recurrent Networks
Nadine El-Naggar, Pranava Madhyastha, Tillman Weyde
Previous work has established that RNNs with an unbounded activation function have the capacity to count exactly. However, it has also been shown that RNNs are challenging to train…
Towards a Unified Model for Generating Answers and Explanations in Visual Question Answering
Chenxi Whitehouse, Tillman Weyde, Pranava Madhyastha
The field of visual question answering (VQA) has recently seen a surge in research focused on providing explanations for predicted answers. However, current systems mostly rely on…
Exploring the Long-Term Generalization of Counting Behavior in RNNs
Nadine El-Naggar, Pranava Madhyastha, Tillman Weyde
In this study, we investigate the generalization of LSTM, ReLU and GRU models on counting tasks over long sequences. Previous theoretical work has established that RNNs with ReLU a…
Jazz Contrafact Detection
C. Bunks, T. Weyde
In jazz, a contrafact is a new melody composed over an existing, but often reharmonized chord progression. Because reharmonization can introduce a wide range of variations, detecti…
The Beyond the Fence Musical and Computer Says Show Documentary
Simon Colton, Maria Teresa Llano, Rose Hepworth +11
During 2015 and early 2016, the cultural application of Computational Creativity research and practice took a big leap forward, with a project where multiple computational systems…
Learning Speech Emotion Representations in the Quaternion Domain
Eric Guizzo, Tillman Weyde, Simone Scardapane +1
The modeling of human emotion expression in speech signals is an important, yet challenging task. The high resource demand of speech emotion recognition models, combined with the t…
Evaluation of Fake News Detection with Knowledge-Enhanced Language Models
Chenxi Whitehouse, Tillman Weyde, Pranava Madhyastha +1
Recent advances in fake news detection have exploited the success of large-scale pre-trained language models (PLMs). The predominant state-of-the-art approaches are based on fine-t…
Learned complex masks for multi-instrument source separation
Andreas Jansson, Rachel M. Bittner, Nicola Montecchio +1
Music source separation in the time-frequency domain is commonly achieved by applying a soft or binary mask to the magnitude component of (complex) spectrograms. The phase componen…
Relational Weight Priors in Neural Networks for Abstract Pattern Learning and Language Modelling
Radha Kopparti, Tillman Weyde
Deep neural networks have become the dominant approach in natural language processing (NLP). However, in recent years, it has become apparent that there are shortcomings in systema…
Anti-Transfer Learning for Task Invariance in Convolutional Neural Networks for Speech Processing
Eric Guizzo, Tillman Weyde, Giacomo Tarroni
We introduce the novel concept of anti-transfer learning for speech processing with convolutional neural networks. While transfer learning assumes that the learning process for a t…
Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signals
Eric Guizzo, Tillman Weyde, Jack Barnett Leveson
Robustness against temporal variations is important for emotion recognition from speech audio, since emotion is ex-pressed through complex spectral patterns that can exhibit signif…
Weight Priors for Learning Identity Relations
Radha Kopparti, Tillman Weyde
Learning abstract and systematic relations has been an open issue in neural network learning for over 30 years. It has been shown recently that neural networks do not learn relatio…
Making Good on LSTMs' Unfulfilled Promise
Daniel Philps, Artur d'Avila Garcez, Tillman Weyde
LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We exa…
Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy
Joaquin Perez-Lapillo, Oleksandr Galkin, Tillman Weyde
In recent years, deep learning has surpassed traditional approaches to the problem of singing voice separation. The Wave-U-Net is a recent deep network architecture that operates d…
Factors for the Generalisation of Identity Relations by Neural Networks
Radha Kopparti, Tillman Weyde
Many researchers implicitly assume that neural networks learn relations and generalise them to new unseen data. It has been shown recently, however, that the generalisation of feed…
Continual Learning Augmented Investment Decisions
Daniel Philps, Tillman Weyde, Artur d'Avila Garcez +1
Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making. We introduce Continual Learning Augmentation (CLA) which i…