Search Results for author: David Rosenberg

Found 8 papers, 1 papers with code

DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation

no code implementations19 Jul 2023 Rodrigo Castellon, Achintya Gopal, Brian Bloniarz, David Rosenberg

The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance.

MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies

1 code implementation26 May 2023 Shiyue Zhang, Shijie Wu, Ozan Irsoy, Steven Lu, Mohit Bansal, Mark Dredze, David Rosenberg

Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE).

BloombergGPT: A Large Language Model for Finance

no code implementations30 Mar 2023 Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, Gideon Mann

The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering.

Causal Judgment Date Understanding +21

Dual Reinforcement-Based Specification Generation for Image De-Rendering

no code implementations2 Mar 2021 Ramakanth Pasunuru, David Rosenberg, Gideon Mann, Mohit Bansal

Since these are sequence models, we must choose an ordering of the objects in the graphics programs for likelihood training.

Inductive Bias

Improving Grey-Box Fuzzing by Modeling Program Behavior

no code implementations21 Nov 2018 Siddharth Karamcheti, Gideon Mann, David Rosenberg

While these fuzzers have been able to find vulnerabilities in many widely used programs, they are not efficient; of the millions of inputs executed by AFL in a typical fuzzing run, only a handful discover unseen behavior or trigger a crash.

Adaptive Grey-Box Fuzz-Testing with Thompson Sampling

no code implementations24 Aug 2018 Siddharth Karamcheti, Gideon Mann, David Rosenberg

Fuzz testing, or "fuzzing," refers to a widely deployed class of techniques for testing programs by generating a set of inputs for the express purpose of finding bugs and identifying security flaws.

Thompson Sampling

Scatteract: Automated extraction of data from scatter plots

no code implementations21 Apr 2017 Mathieu Cliche, David Rosenberg, Dhruv Madeka, Connie Yee

Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points.

Optical Character Recognition Optical Character Recognition (OCR)

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