← Search

Danilo Bzdok

7 accepted papers

2026

At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization

ICML 2026poster

Pre-trained transformers have demonstrated remarkable generalization abilities, at times extending beyond the scope of their training data. Yet, real-world deployments often face unexpected or adversarial data that diverges from training data distributions. Without explicit mechanisms for handling s…

Cited by 0SourceScholar
2026

Quantifying LLM Attention-Head Stability: Implications for Circuit Universality

ICML 2026poster

In mechanistic interpretability, recent work scrutinizes transformer “circuits”—sparse, mono or multi layer sub computations, that may reflect human understandable functions. Yet, these network circuits are rarely acid-tested for their stability across different instances of the same deep learning a…

Cited by 0SourceScholar
2025

From Noise to Narrative: Tracing the Origins of Hallucinations in Transformers

NeurIPS 2025poster

As generative AI systems become competent and democratized in science, business, and government, deeper insight into their failure modes now poses an acute need. The occasional volatility in their behavior, such as the propensity of transformer models to hallucinate, impedes trust and adoption of em…

Cited by 0SourceScholar
2025

ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images

ICASSP 2025accepted

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same patients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, a…

Cited by 0SourceScholar
2017

Learning Neural Representations of Human Cognition across Many fMRI Studies

NeurIPS 2017poster

Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets. It opens the door to large-scale statistical models. Finding a unified perspective for all available data calls for scalable and automated solutions to an old challenge: how to aggregate heterogeneous infor…

2015

Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data

NeurIPS 2015poster

Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets. Existing neuroimaging methods typically perform either discovery of unknown neural structure or testing of neural structure associated with mental tasks. However, testing hypotheses on the neural corre…