Search Results for author: Ari Seff

Found 14 papers, 4 papers with code

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

no code implementations ICCV 2023 Ari Seff, Brian Cera, Dian Chen, Mason Ng, Aurick Zhou, Nigamaa Nayakanti, Khaled S. Refaat, Rami Al-Rfou, Benjamin Sapp

Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain.

Autonomous Vehicles Language Modelling +2

Vitruvion: A Generative Model of Parametric CAD Sketches

no code implementations ICLR 2022 Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams

Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards.

SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design

1 code implementation16 Jul 2020 Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams

Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design.

Program Synthesis

Discrete Object Generation with Reversible Inductive Construction

1 code implementation NeurIPS 2019 Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams

The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs.

Denoising Object +1

Continual Learning in Generative Adversarial Nets

no code implementations23 May 2017 Ari Seff, Alex Beatson, Daniel Suo, Han Liu

Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions.

Continual Learning

Learning from Maps: Visual Common Sense for Autonomous Driving

no code implementations25 Nov 2016 Ari Seff, Jianxiong Xiao

Today's autonomous vehicles rely extensively on high-definition 3D maps to navigate the environment.

Autonomous Driving Common Sense Reasoning +2

LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

4 code implementations10 Jun 2015 Fisher Yu, Ari Seff, yinda zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao

While there has been remarkable progress in the performance of visual recognition algorithms, the state-of-the-art models tend to be exceptionally data-hungry.

Interleaved Text/Image Deep Mining on a Very Large-Scale Radiology Database

no code implementations CVPR 2015 Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers

We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's picture archiving and communication system.

Retrieval Sentence

Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation

no code implementations12 May 2015 Holger R. Roth, Le Lu, Jiamin Liu, Jianhua Yao, Ari Seff, Kevin Cherry, Lauren Kim, Ronald M. Summers

By leveraging existing CAD systems, coordinates of regions or volumes of interest (ROI or VOI) for lesion candidates are generated in this step and function as input for a second tier, which is our focus in this study.

Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation

no code implementations4 May 2015 Hoo-chang Shin, Le Lu, Lauren Kim, Ari Seff, Jianhua Yao, Ronald M. Summers

We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System.

Sentence

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

no code implementations ICCV 2015 Chenyi Chen, Ari Seff, Alain Kornhauser, Jianxiong Xiao

To demonstrate this, we train a deep Convolutional Neural Network using recording from 12 hours of human driving in a video game and show that our model can work well to drive a car in a very diverse set of virtual environments.

Autonomous Driving

2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

no code implementations14 Aug 2014 Ari Seff, Le Lu, Kevin M. Cherry, Holger Roth, Jiamin Liu, Shijun Wang, Joanne Hoffman, Evrim B. Turkbey, Ronald M. Summers

In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue.

object-detection Object Detection

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