activity
20162026
most citedA Closer Look at Memorization in Deep Networks

353 citations · 1.3k across the 22 of their papers we have counts for

collaborators
Showing cs.LGShow all

7 papers · 1 filter

cs.LG2024★ 16 cited

Foundational Challenges in Assuring Alignment and Safety of Large Language Models

Usman Anwar, Abulhair Saparov, Javier Rando +39

This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are organized into three different categories…

cs.LG2023

Implicit meta-learning may lead language models to trust more reliable sources

Dmitrii Krasheninnikov, Egor Krasheninnikov, Bruno Mlodozeniec +2

We demonstrate that LLMs may learn indicators of document usefulness and modulate their updates accordingly. We introduce random strings ("tags") as indicators of usefulness in a s…

cs.LG2022

Generalizing in the Real World with Representation Learning

Tegan Maharaj

Machine learning (ML) formalizes the problem of getting computers to learn from experience as optimization of performance according to some metric(s) on a set of data examples. Thi…

cs.LG2022★ 6 cited

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics

Shoaib Ahmed Siddiqui, Nitarshan Rajkumar, Tegan Maharaj +2

Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or raw data, practitioners are faced with si…

cs.LG2020★ 8 cited

Predicting Infectiousness for Proactive Contact Tracing

Yoshua Bengio, Prateek Gupta, Tegan Maharaj +20

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-sca…

cs.LG2020★ 6 cited

Hidden Incentives for Auto-Induced Distributional Shift

David Krueger, Tegan Maharaj, Jan Leike

Decisions made by machine learning systems have increasing influence on the world, yet it is common for machine learning algorithms to assume that no such influence exists. An exam…

cs.LG2016★ 7 cited

Surprisal-Driven Zoneout

Kamil Rocki, Tomasz Kornuta, Tegan Maharaj

We propose a novel method of regularization for recurrent neural networks called suprisal-driven zoneout. In this method, states zoneout (maintain their previous value rather than…