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Thomas Lukasiewicz

59 accepted papers

2026

Prototype Transformer: Towards Language Model Architectures Interpretable by Design

ICML 2026poster

While state-of-the-art language models (LMs) surpass the vast majority of humans in certain domains, their reasoning remains largely opaque, reducing trust and risking deception and hallucination. In this work, we introduce the Prototype Transformer (ProtoT)—an autoregressive LM architecture that re…

Cited by 0SourceScholar
2026

Towards the Training of Deeper Predictive Coding Neural Networks

ICML 2026poster

Predictive coding networks are neural models that perform inference through an iterative energy minimization process. While effective in shallow architectures, they suffer significant performance degradation beyond five to seven layers. In this work, we show that this degradation is caused by expone…

Cited by 5SourceScholar
2025

Benchmarking Predictive Coding Networks -- Made Simple

ICLR 2025spotlight

In this work, we tackle the problems of efficiency and scalability for predictive coding networks (PCNs) in machine learning. To do so, we propose a library that focuses on performance and simplicity, and use it to implement a large set of standard benchmarks for the community to use for their expe…

2025

Shh, don't say that! Domain Certification in LLMs

ICLR 2025poster

Large language models (LLMs) are often deployed to do constrained tasks, with narrow domains. For example, customer support bots can be built on top of LLMs, relying on their broad language understanding and capabilities to enhance performance. However, these LLMs are adversarially susceptible, pote…

Cited by 1SourcePDFScholar
2025

Towards Certification of Uncertainty Calibration under Adversarial Attacks

ICLR 2025poster

Since neural classifiers are known to be sensitive to adversarial perturbations that alter their accuracy, certification methods have been developed to provide provable guarantees on the insensitivity of their predictions to such perturbations. On the other hand, in safety-critical applications, the…

Cited by 0SourcePDFScholar
2024

A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks

ICLR 2024poster

Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply changing the temporal scheduling of the update rule for the synaptic weights lead…

Cited by 10SourcePDFScholar
2024

Fool Me Once? Contrasting Textual and Visual Explanations in a Clinical Decision-Support Setting

EMNLP 2024main

The growing capabilities of AI models are leading to their wider use, including in safety-critical domains. Explainable AI (XAI) aims to make these models safer to use by making their inference process more transparent. However, current explainability methods are seldom evaluated in the way they are…

Cited by 1SourcePDFScholar
2024

Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation

AAAI 2024technical

Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the…

2024

How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data

ICLR 2024poster

Deep Generative Models (DGMs) have been shown to be powerful tools for generating tabular data, as they have been increasingly able to capture the complex distributions that characterize them. However, to generate realistic synthetic data, it is often not enough to have a good approximation of their…

2024

PiShield: A PyTorch Package for Learning with Requirements

IJCAI 2024poster

Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks' topology…

2024

Predictive Coding beyond Correlations

ICML 2024poster

Biologically plausible learning algorithms offer a promising alternative to traditional deep learning techniques, especially in overcoming the limitations of backpropagation in fast and low-energy neuromorphic implementations. To this end, there has been extensive research in understanding what thei…

Cited by 3SourcePDFScholar
2024

The Defeat of the Winograd Schema Challenge (Abstract Reprint)

AAAI 2024technical

The Winograd Schema Challenge—a set of twin sentences involving pronoun reference disambiguation that seem to require the use of commonsense knowledge—was proposed by Hector Levesque in 2011. By 2019, a number of AI systems, based on large pre-trained transformer-based language models and fine-tuned…

Cited by 0SourcePDFScholar
2023

A Theoretical Framework for Inference and Learning in Predictive Coding Networks

ICLR 2023poster

Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process of prediction error minimization. PC networks (PCNs) are trained in two phases. First, neural activities are updated to o…

2023

An Empirical Analysis of Parameter-Efficient Methods for Debiasing Pre-Trained Language Models

ACL 2023long

The increasingly large size of modern pre-trained language models not only makes them inherit more human-like biases from the training corpora, but also makes it computationally expensive to mitigate such biases. In this paper, we investigate recent parameter-efficient methods in combination with co…

2023

Backpropagation at the Infinitesimal Inference Limit of Energy-Based Models: Unifying Predictive Coding, Equilibrium Propagation, and Contrastive Hebbian Learning

ICLR 2023poster

How the brain performs credit assignment is a fundamental unsolved problem in neuroscience. Many `biologically plausible' algorithms have been proposed, which compute gradients that approximate those computed by backpropagation (BP), and which operate in ways that more closely satisfy the constraint…

2023

Faithfulness Tests for Natural Language Explanations

ACL 2023short

Explanations of neural models aim to reveal a model’s decision-making process for its predictions. However, recent work shows that current methods giving explanations such as saliency maps or counterfactuals can be misleading, as they are prone to present reasons that are unfaithful to the model’s i…

2023

Improving Language Models’ Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary

EMNLP 2023long main

The non-humanlike behaviour of contemporary pre-trained language models (PLMs) is a leading cause undermining their trustworthiness. A striking phenomenon of such faulty behaviours is the generation of inconsistent predictions, which produces logically contradictory results, such as generating diff…

Cited by 0SourceScholar
2023

KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations

ACL 2023short

While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and alleviating inconsistencies among generated NLEs. In this work, we leverage external knowledge…

2023

MPS-AMS: Masked Patches Selection and Adaptive Masking Strategy Based Self-Supervised Medical Image Segmentation

ICASSP 2023accepted

Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling methods are mainly utilized in natural images, and their applications in medical images are relatively lacking. Besides,…

Cited by 0SourceScholar
2023

Mathematical Capabilities of ChatGPT

NeurIPS 2023poster

We investigate the mathematical capabilities of two versions of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel evaluation scheme. In contrast to formal mathematics, where large databases o…

Cited by 660SourcePDFScholar
2023

Multi-Head Feature Pyramid Networks for Breast Mass Detection

ICASSP 2023accepted

Analysis of X-ray images is one of the main tools to diagnose breast cancer. The ability to quickly and accurately detect the location of masses from the huge amount of image data is the key to reducing the morbidity and mortality of breast cancer. Currently, the main factor limiting the accuracy of…

Cited by 0SourceScholar
2023

MvCo-DoT: Multi-View Contrastive Domain Transfer Network for Medical Report Generation

ICASSP 2023accepted

In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency. However, the existing medical report generation methods cannot exploit the rich multi-view mutual information of medical images. Therefore, in this wor…

Cited by 0SourceScholar
2023

NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation

ICML 2023poster

Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. This is useful in a wide range of real-world applications where collecting pixel-wise labels is not feasible in time or cost. Current approaches to semi-supervised semantic segmentation w…

2022

BECEL: Benchmark for Consistency Evaluation of Language Models

COLING 2022main

Behavioural consistency is a critical condition for a language model (LM) to become trustworthy like humans. Despite its importance, however, there is little consensus on the definition of LM consistency, resulting in different definitions across many studies. In this paper, we first propose the ide…

2022

Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence

NAACL 2022findings

The logical negation property (LNP), which implies generating different predictions for semantically opposite inputs (p is true iff ¬p is false), is an important property that a trustworthy language model must satisfy. However, much recent evidence shows that large-size pre-trained language models (…

2022

Explanations for Negative Query Answers under Inconsistency-Tolerant Semantics

IJCAI 2022poster

Inconsistency-tolerant semantics have been proposed to provide meaningful query answers even in the presence of inconsistent knowledge. Recently, explainability has also become a prominent problem in different areas of AI. While the complexity of inconsistency-tolerant semantics is rather well-under…

Cited by 17SourcePDFScholar
2022

Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations in a Label-Abundant Setup

EMNLP 2022finding

Training a model to provide natural language explanations (NLEs) for its predictions usually requires the acquisition of task-specific NLEs, which is time- and resource-consuming. A potential solution is the few-shot out-of-domain transfer of NLEs from a parent task with many NLEs to a child task.In…

2022

Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

ICML 2022spotlight

Models that generate extractive rationales (i.e., subsets of features) or natural language explanations (NLEs) for their predictions are important for explainable AI. While an extractive rationale provides a quick view of the features most responsible for a prediction, an NLE allows for a comprehens…

2022

Learning on Arbitrary Graph Topologies via Predictive Coding

NeurIPS 2022accept

Training with backpropagation (BP) in standard deep learning consists of two main steps: a forward pass that maps a data point to its prediction, and a backward pass that propagates the error of this prediction back through the network. This process is highly effective when the goal is to minimize a…

Cited by 39SourcePDFScholar
2022

NP-Match: When Neural Processes meet Semi-Supervised Learning

ICML 2022spotlight

Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP…

2022

Predictive Coding beyond Gaussian Distributions

NeurIPS 2022accept

A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation~(BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired method that performs inference on hierarchical Gaus…

Cited by 13SourcePDFScholar
2022

Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?

IJCAI 2022poster

The backpropagation of error algorithm (BP) used to train deep neural networks has been fundamental to the successes of deep learning. However, it requires sequential backwards updates and non-local computations which make it challenging to parallelize at scale and is unlike how learning works in th…

Cited by 58SourcePDFScholar
2022

Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation

CVPR 2022poster

Recently, several Bayesian deep learning methods have been proposed for semi-supervised medical image segmentation. Although they have achieved promising results on medical benchmarks, some problems are still existing. Firstly, their overall architectures belong to the discriminative models, and hen…

Cited by 39PDFcodeScholar
2022

Reverse Differentiation via Predictive Coding

AAAI 2022technical

Deep learning has redefined AI thanks to the rise of artificial neural networks, which are inspired by neurological networks in the brain. Through the years, this dualism between AI and neuroscience has brought immense benefits to both fields, allowing neural networks to be used in a plethora of app…

Cited by 43SourcePDFScholar
2022

Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction

EMNLP 2022main

Open information extraction (OIE) is the task of extracting facts "(Subject, Relation, Object)” from natural language text. We propose several new methods for training neural OIE models in this paper. First, we propose a novel method for computing syntactically rich text embeddings using the structu…

Cited by 2SourcePDFScholar
2022

Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models

ICML 2022spotlight

A large number of neural network models of associative memory have been proposed in the literature. These include the classical Hopfield networks (HNs), sparse distributed memories (SDMs), and more recently the modern continuous Hopfield networks (MCHNs), which possess close links with self-attentio…

2021

Associative Memories via Predictive Coding

NeurIPS 2021poster

Associative memories in the brain receive and store patterns of activity registered by the sensory neurons, and are able to retrieve them when necessary. Due to their importance in human intelligence, computational models of associative memories have been developed for several decades now. In this p…

Cited by 84SourcePDFScholar
2021

E-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks

ICCV 2021poster

Recently, there has been an increasing number of efforts to introduce models capable of generating natural language explanations (NLEs) for their predictions on vision-language (VL) tasks. Such models are appealing, because they can provide human-friendly and comprehensive explanations. However, the…

Cited by 111PDFcodeScholar
2021

Learning from the Best: Rationalizing Predictions by Adversarial Information Calibration

AAAI 2021technical

Explaining the predictions of AI models is paramount in safety-critical applications, such as in legal or medical domains. One form of explanation for a prediction is an extractive rationale, i.e., a subset of features of an instance that lead the model to give its prediction on the instance. Previo…

2021

Preferred Explanations for Ontology-Mediated Queries under Existential Rules

AAAI 2021technical

Recently, explanations for query answers under existential rules have been investigated, where an explanation is an inclusion-minimal subset of a given database that, together with the ontology, entails the query. In this paper, we take a step further and study explanations under different minimalit…

Cited by 17SourcePDFScholar
2021

RSG: A Simple but Effective Module for Learning Imbalanced Datasets

CVPR 2021poster

Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes duri…

Cited by 128PDFcodeScholar
2021

The Gap on Gap: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets

AAAI 2021technical

Diagnostic datasets that can detect biased models are an important prerequisite for bias reduction within natural language processing. However, undesired patterns in the collected data can make such tests incorrect. For example, if the feminine subset of a gender-bias-measuring coreference resolutio…

2021

The Surprising Power of Graph Neural Networks with Random Node Initialization

IJCAI 2021poster

Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressi…

Cited by 289SourcePDFScholar
2020

BoxE: A Box Embedding Model for Knowledge Base Completion

NeurIPS 2020spotlight

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are su…

2020

Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks

NeurIPS 2020poster

Backpropagation (BP) has been the most successful algorithm used to train artificial neural networks. However, there are several gaps between BP and learning in biologically plausible neuronal networks of the brain (learning in the brain, or simply BL, for short), in particular, (1) it has been uncl…

Cited by 125SourcePDFScholar
2020

Hybrid Deep-Semantic Matrix Factorization for Tag-Aware Personalized Recommendation

ICASSP 2020accepted

Matrix factorization has now become a dominant solution for personalized recommendation on the Social Web. To alleviate the cold start problem, previous approaches have incorporated various additional sources of information into traditional matrix factorization models. These upgraded models, however…

Cited by 0SourceScholar
2020

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

NeurIPS 2020poster

We propose a novel lightweight generative adversarial network for efficient image manipulation using natural language descriptions. To achieve this, a new word-level discriminator is proposed, which provides the generator with fine-grained training feedback at word-level, to facilitate training a li…

2019

Long Text Analysis Using Sliced Recurrent Neural Networks with Breaking Point Information Enrichment

ICASSP 2019accepted

Sliced recurrent neural networks (SRNNs) are the state-of-the-art efficient solution for long text analysis tasks; however, their slicing operations inevitably result in long-term dependency loss in lower-level networks and thus limit their accuracy. Therefore, we propose a breaking point informatio…

Cited by 0SourceScholar
2018

e-SNLI: Natural Language Inference with Natural Language Explanations

NeurIPS 2018poster

In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with…