2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
Proteus: Incremental Memory Activation for Long-Context Sequence Modeling
Reza Bayat, Ali Behrouz, Vahab Mirrokni +1
The quadratic cost of attention-based sequence models for long contexts has motivated a growing line of research on memory-based models that can compress context into a compact sta…
Tapered Language Models
Reza Bayat, Ali Behrouz, Aaron Courville
Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack of identical layers in which parameters are allocated uniformly…
Steering Large Language Model Activations in Sparse Spaces
Reza Bayat, Ali Rahimi-Kalahroudi, Mohammad Pezeshki +2
A key challenge in AI alignment is guiding large language models (LLMs) to follow desired behaviors at test time. Activation steering, which modifies internal model activations dur…
Performance Control in Early Exiting to Deploy Large Models at the Same Cost of Smaller Ones
Mehrnaz Mofakhami, Reza Bayat, Ioannis Mitliagkas +2
Early Exiting (EE) is a promising technique for speeding up inference by adaptively allocating compute resources to data points based on their difficulty. The approach enables pred…
The Pitfalls of Memorization: When Memorization Hurts Generalization
Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob +2
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explanations.This behavior leads to poor gener…
Adversarial Feature Desensitization
Pouya Bashivan, Reza Bayat, Adam Ibrahim +5
Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can drastically impair the network's perform…