Search Results for author: John F. Canny

Found 6 papers, 5 papers with code

What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics

1 code implementation12 May 2022 David M. Chan, Austin Myers, Sudheendra Vijayanarasimhan, David A. Ross, Bryan Seybold, John F. Canny

While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major barrier to using these systems in the real world.

Video Description

Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models

no code implementations14 Oct 2021 Forrest Huang, Gang Li, Xin Zhou, John F. Canny, Yang Li

The design process of user interfaces (UIs) often begins with articulating high-level design goals.

Retrieval

MSA Transformer

1 code implementation13 Feb 2021 Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John F. Canny, Pieter Abbeel, Tom Sercu, Alexander Rives

Unsupervised protein language models trained across millions of diverse sequences learn structure and function of proteins.

Masked Language Modeling Multiple Sequence Alignment +1

Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications

1 code implementation8 Apr 2019 Forrest Huang, John F. Canny

Sketching and natural languages are effective communication media for interactive applications.

Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics

1 code implementation11 Dec 2018 Biye Jiang, David M. Chan, Tianhao Zhang, John F. Canny

Finally we show that diagnostic visualization using LDAM leads to a novel insight into the parameter averaging method for deep net training.

t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data

1 code implementation31 Jul 2018 David M. Chan, Roshan Rao, Forrest Huang, John F. Canny

Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples.

Dimensionality Reduction

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