Frederick Liu | DeepAI

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∙ 06/09/2023

Using Foundation Models to Detect Policy Violations with Minimal Supervision

Foundation models, i.e. large neural networks pre-trained on large text ...

0 Sid Mittal, et al. ∙

∙ 02/13/2023

Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning

Pretrained large language models (LLMs) are able to solve a wide variety...

0 Maximilian Mozes, et al. ∙

∙ 02/01/2023

FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features

The problem of efficient approximation of a linear operator induced by t...

0 Valerii Likhosherstov, et al. ∙

∙ 10/21/2022

Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation

Knowledge distillation is one of the primary methods of transferring kno...

0 Ziqi Wang, et al. ∙

∙ 05/30/2022

Chefs' Random Tables: Non-Trigonometric Random Features

We introduce chefs' random tables (CRTs), a new class of non-trigonometr...

0 Valerii Likhosherstov, et al. ∙

∙ 05/23/2022

Tracing Knowledge in Language Models Back to the Training Data

Neural language models (LMs) have been shown to memorize a great deal of...

0 Ekin Akyürek, et al. ∙

∙ 02/24/2022

Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations

A popular explainable AI (XAI) approach to quantify feature importance o...

0 Chih-Kuan Yeh, et al. ∙

∙ 02/24/2022

First is Better Than Last for Training Data Influence

The ability to identify influential training examples enables us to debu...

0 Chih-Kuan Yeh, et al. ∙

∙ 10/16/2021

EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks

Encoder-decoder transformer architectures have become popular recently w...

0 Frederick Liu, et al. ∙

∙ 06/08/2020

The Penalty Imposed by Ablated Data Augmentation

There is a set of data augmentation techniques that ablate parts of the ...

0 Frederick Liu, et al. ∙

∙ 02/19/2020

Estimating Training Data Influence by Tracking Gradient Descent

We introduce a method called TrackIn that computes the influence of a tr...

13 Garima Pruthi, et al. ∙

∙ 06/19/2019

Incorporating Priors with Feature Attribution on Text Classification

Feature attribution methods, proposed recently, help users interpret the...

0 Frederick Liu, et al. ∙

∙ 07/11/2018

Differentially-Private "Draw and Discard" Machine Learning

In this work, we propose a novel framework for privacy-preserving client...

0 Vasyl Pihur, et al. ∙

∙ 08/22/2017

Handling Homographs in Neural Machine Translation

Homographs, words with different meanings but the same surface form, hav...

0 Frederick Liu, et al. ∙

∙ 04/17/2017

Learning Character-level Compositionality with Visual Features

Previous work has modeled the compositionality of words by creating char...

0 Frederick Liu, et al. ∙

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