Sara Hooker | DeepAI

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∙ 09/12/2023

The Grand Illusion: The Myth of Software Portability and Implications for ML Progress

Pushing the boundaries of machine learning often requires exploring diff...

0 Fraser Mince, et al. ∙

∙ 09/11/2023

Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

The Mixture of Experts (MoE) is a widely known neural architecture where...

0 Ted Zadouri, et al. ∙

∙ 09/08/2023

When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Large volumes of text data have contributed significantly to the develop...

0 Max Marion, et al. ∙

∙ 07/06/2023

Frontier AI Regulation: Managing Emerging Risks to Public Safety

Advanced AI models hold the promise of tremendous benefits for humanity,...

0 Markus Anderljung, et al. ∙

∙ 06/09/2023

Evaluating the Social Impact of Generative AI Systems in Systems and Society

Generative AI systems across modalities, ranging from text, image, audio...

2 Irene Solaiman, et al. ∙

∙ 05/30/2023

Intriguing Properties of Quantization at Scale

Emergent properties have been widely adopted as a term to describe behav...

10 Arash Ahmadian, et al. ∙

∙ 04/24/2023

On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research

Perception of toxicity evolves over time and often differs between geogr...

6 Luiza Pozzobon, et al. ∙

∙ 03/01/2023

FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling

Ensembling independent deep neural networks (DNNs) is a simple and effec...

4 Wei-Yin Ko, et al. ∙

∙ 11/04/2022

Intriguing Properties of Compression on Multilingual Models

Multilingual models are often particularly dependent on scaling to gener...

1 Kelechi Ogueji, et al. ∙

∙ 10/26/2022

Large language models are not zero-shot communicators

Despite widespread use of LLMs as conversational agents, evaluations of ...

3 Laura Ruis, et al. ∙

∙ 09/20/2022

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics

Modern machine learning research relies on relatively few carefully cura...

17 Shoaib Ahmed Siddiqui, et al. ∙

∙ 08/31/2022

Efficient Methods for Natural Language Processing: A Survey

Getting the most out of limited resources allows advances in natural lan...

9 Marcos Treviso, et al. ∙

∙ 07/01/2022

Studying the impact of magnitude pruning on contrastive learning methods

We study the impact of different pruning techniques on the representatio...

12 Francesco Corti, et al. ∙

∙ 06/13/2022

Robust Distillation for Worst-class Performance

Knowledge distillation has proven to be an effective technique in improv...

12 Serena Wang, et al. ∙

∙ 01/14/2022

When less is more: Simplifying inputs aids neural network understanding

How do neural network image classifiers respond to simpler and simpler i...

42 Robin Tibor Schirrmeister, et al. ∙

∙ 10/06/2021

The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation

A "bigger is better" explosion in the number of parameters in deep neura...

52 Orevaoghene Ahia, et al. ∙

∙ 07/27/2021

A Tale Of Two Long Tails

As machine learning models are increasingly employed to assist human dec...

64 Daniel D'souza, et al. ∙

∙ 07/16/2021

When does loss-based prioritization fail?

Not all examples are created equal, but standard deep neural network tra...

12 Niel Teng Hu, et al. ∙

∙ 06/22/2021

Randomness In Neural Network Training: Characterizing The Impact of Tooling

The quest for determinism in machine learning has disproportionately foc...

35 Donglin Zhuang, et al. ∙

∙ 02/02/2021

Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization

Training sparse networks to converge to the same performance as dense ne...

31 Kale-ab Tessera, et al. ∙

∙ 10/06/2020

Characterising Bias in Compressed Models

The popularity and widespread use of pruning and quantization is driven ...

0 Sara Hooker, et al. ∙

∙ 09/14/2020

The Hardware Lottery

Hardware, systems and algorithms research communities have historically ...

0 Sara Hooker, et al. ∙

∙ 08/26/2020

Estimating Example Difficulty using Variance of Gradients

In machine learning, a question of great interest is understanding what ...

8 Chirag Agarwal, et al. ∙

∙ 04/15/2020

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

With the recent wave of progress in artificial intelligence (AI) has com...

0 Miles Brundage, et al. ∙

∙ 11/13/2019

Selective Brain Damage: Measuring the Disparate Impact of Model Pruning

Neural network pruning techniques have demonstrated it is possible to re...

20 Sara Hooker, et al. ∙

∙ 02/25/2019

The State of Sparsity in Deep Neural Networks

We rigorously evaluate three state-of-the-art techniques for inducing sp...

0 Trevor Gale, et al. ∙

∙ 06/28/2018

Evaluating Feature Importance Estimates

Estimating the influence of a given feature to a model prediction is cha...

4 Sara Hooker, et al. ∙

∙ 11/02/2017

The (Un)reliability of saliency methods

Saliency methods aim to explain the predictions of deep neural networks....

0 Pieter-Jan Kindermans, et al. ∙

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