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Łukasz Struski

8 accepted papers

2026

EPIC: Explanation of Pretrained Image Classification Networks via Prototypes

AAAI 2026technical

Explainable AI (XAI) methods generally fall into two categories. Post-hoc approaches generate explanations for pre-trained models and are compatible with various neural network architectures. These methods often use feature importance visualizations, such as saliency maps, to indicate which input re

Cited by 0SourcePDFScholar
2026

SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs

ICML 2026poster

Sparse Mixture-of-Experts (MoE) architectures enable scaling LLM parameters under a fixed inference budget by activating only a small subset of experts via top-k routing. While this preserves causality and suits autoregressive language models, the discrete top-k operator is not differentiable, forci…

Cited by 0SourceScholar
2025

LapSum - One Method to Differentiate Them All: Ranking, Sorting and Top-k Selection

ICML 2025poster

We present a novel technique for constructing differentiable order-type operations, including soft ranking, soft top-k selection, and soft permutations. Our approach leverages an efficient closed-form formula for the inverse of the function LapSum, defined as the sum of Laplace distributions. This f…

Cited by 0SourcePDFScholar
2025

SEMU: Singular Value Decomposition for Efficient Machine Unlearning

ICML 2025poster

While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pressing challenges in AI safety, machine unlearning (MU) has become increasingly critical to meet upcoming safety regulati…

Cited by 0SourcePDFScholar
2024

Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations

AAAI 2024technical

Prototypical parts-based networks are becoming increasingly popular due to their faithful self-explanations. However, their similarity maps are calculated in the penultimate network layer. Therefore, the receptive field of the prototype activation region often depends on parts of the image outside t…

2023

Bounding Evidence and Estimating Log-Likelihood in VAE

AISTATS 2023poster

Many crucial problems in deep learning and statistical inference are caused by a variational gap, i.e., a difference between model evidence (log-likelihood) and evidence lower bound (ELBO). In particular, in a classical VAE setting that involves training via an ELBO cost function, it is difficult to…

Cited by 4SourcePDFScholar
2022

Interpretable Image Classification with Differentiable Prototypes Assignment

ECCV 2022poster

"Existing prototypical-based models address the black-box nature of deep learning. However, they are sub-optimal as they often assume separate prototypes for each class, require multi-step optimization, make decisions based on prototype absence (so-called negative reasoning process), and derive vagu…

2018

Processing of missing data by neural networks

NeurIPS 2018poster

We propose a general, theoretically justified mechanism for processing missing data by neural networks. Our idea is to replace typical neuron's response in the first hidden layer by its expected value. This approach can be applied for various types of networks at minimal cost in their modification.…