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Yordan Yordanov

4 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
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
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

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