Tatsunori B. Hashimoto | DeepAI

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∙ 07/07/2023

One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention

Recent works have empirically analyzed in-context learning and shown tha...

0 Arvind Mahankali, et al. ∙

∙ 05/30/2023

Likelihood-Based Diffusion Language Models

Despite a growing interest in diffusion-based language models, existing ...

0 Ishaan Gulrajani, et al. ∙

∙ 01/31/2023

Benchmarking Large Language Models for News Summarization

Large language models (LLMs) have shown promise for automatic summarizat...

0 Tianyi Zhang, et al. ∙

∙ 11/29/2022

Coder Reviewer Reranking for Code Generation

Sampling diverse programs from a code language model and reranking with ...

0 Tianyi Zhang, et al. ∙

∙ 09/08/2022

Data Feedback Loops: Model-driven Amplification of Dataset Biases

Datasets scraped from the internet have been critical to the successes o...

5 Rohan Taori, et al. ∙

∙ 05/27/2022

Diffusion-LM Improves Controllable Text Generation

Controlling the behavior of language models (LMs) without re-training is...

0 Xiang Lisa Li, et al. ∙

∙ 05/23/2022

TempLM: Distilling Language Models into Template-Based Generators

While pretrained language models (PLMs) have greatly improved text gener...

0 Tianyi Zhang, et al. ∙

∙ 11/20/2019

Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Overparameterized neural networks can be highly accurate on average on a...

0 Shiori Sagawa, et al. ∙

∙ 11/16/2019

Learning Autocomplete Systems as a Communication Game

We study textual autocomplete—the task of predicting a full sentence fro...

0 Mina Lee, et al. ∙

∙ 09/04/2019

Distributionally Robust Language Modeling

Language models are generally trained on data spanning a wide range of t...

0 Yonatan Oren, et al. ∙

∙ 04/04/2019

Unifying Human and Statistical Evaluation for Natural Language Generation

How can we measure whether a natural language generation system produces...

0 Tatsunori B. Hashimoto, et al. ∙

∙ 12/04/2018

A Retrieve-and-Edit Framework for Predicting Structured Outputs

For the task of generating complex outputs such as source code, editing ...

0 Tatsunori B. Hashimoto, et al. ∙

∙ 06/20/2018

Fairness Without Demographics in Repeated Loss Minimization

Machine learning models (e.g., speech recognizers) are usually trained t...

0 Tatsunori B. Hashimoto, et al. ∙

∙ 04/11/2018

Derivative free optimization via repeated classification

We develop an algorithm for minimizing a function using n batched functi...

0 Tatsunori B. Hashimoto, et al. ∙

∙ 11/06/2017

Unsupervised Transformation Learning via Convex Relaxations

Our goal is to extract meaningful transformations from raw images, such ...

0 Tatsunori B. Hashimoto, et al. ∙

∙ 09/26/2017

Generating Sentences by Editing Prototypes

We propose a new generative model of sentences that first samples a prot...

0 Kelvin Guu, et al. ∙

∙ 11/02/2015

From random walks to distances on unweighted graphs

Large unweighted directed graphs are commonly used to capture relations ...

0 Tatsunori B. Hashimoto, et al. ∙

∙ 09/18/2015

Word, graph and manifold embedding from Markov processes

Continuous vector representations of words and objects appear to carry s...

0 Tatsunori B. Hashimoto, et al. ∙

∙ 11/20/2014

Metric recovery from directed unweighted graphs

We analyze directed, unweighted graphs obtained from x_i∈R^d by connecti...

0 Tatsunori B. Hashimoto, et al. ∙

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