2 citations · 3 across the 7 of their papers we have counts for
9 papers
Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction
Burc Gokden
This monograph develops a unified account of training and inference in Power Law Decoder Representation language models (PLDR-LLMs). Exact finite work identities decompose changes…
Power law graph attention: exact generalization of scaled dot-product attention, empirical collapse at inference
Burc Gokden
The Large Language Model from Power Law Decoder Representations (PLDR-LLM) and its attention, Power Law Graph Attention (PLGA), replace the fixed bilinear form of scaled dot-produc…
PLDR-LLMs Reason At Self-Organized Criticality
Burc Gokden
We show that PLDR-LLMs pretrained at self-organized criticality exhibit reasoning at inference time. The characteristics of PLDR-LLM deductive outputs at criticality is similar to…
PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own Deep Neural Net At Inference
Burc Gokden
We show that Large Language Model from Power Law Decoder Representations (PLDR-LLM) is a foundational model whose deductive outputs are invariant tensors up to a small perturbation…
PLDR-LLM: Large Language Model from Power Law Decoder Representations
Burc Gokden
We present the Large Language Model from Power Law Decoder Representations (PLDR-LLM), a language model that leverages non-linear and linear transformations through Power Law Graph…
Power Law Graph Transformer for Machine Translation and Representation Learning
Burc Gokden
We present the Power Law Graph Transformer, a transformer model with well defined deductive and inductive tasks for prediction and representation learning. The deductive task learn…