research
∙
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 ...
share
research
∙
02/13/2023
Gradient-Based Automated Iterative Recovery for Parameter-Efficient Tuning
Pretrained large language models (LLMs) are able to solve a wide variety...
research
∙
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...
research
∙
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...
research
∙
05/30/2022
Chefs' Random Tables: Non-Trigonometric Random Features
We introduce chefs' random tables (CRTs), a new class of non-trigonometr...
research
∙
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...
research
∙
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...
research
∙
02/24/2022
First is Better Than Last for Training Data Influence
The ability to identify influential training examples enables us to debu...
research
∙
10/16/2021
EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks
Encoder-decoder transformer architectures have become popular recently w...
research
∙
06/08/2020
The Penalty Imposed by Ablated Data Augmentation
There is a set of data augmentation techniques that ablate parts of the ...
research
∙
02/19/2020
Estimating Training Data Influence by Tracking Gradient Descent
We introduce a method called TrackIn that computes the influence of a tr...
research
∙
06/19/2019
Incorporating Priors with Feature Attribution on Text Classification
Feature attribution methods, proposed recently, help users interpret the...
research
∙
07/11/2018
Differentially-Private "Draw and Discard" Machine Learning
In this work, we propose a novel framework for privacy-preserving client...
research
∙
08/22/2017
Handling Homographs in Neural Machine Translation
Homographs, words with different meanings but the same surface form, hav...
research
∙
04/17/2017
