Hawkins, Jonathan Cohen, Nathaniel Daw, Karthik R Narasimhan, Thomas L. Using natural language and program abstractions to instill human inductive biases in machinesīy Sreejan Kumar, Carlos G Correa, Ishita Dasgupta, Raja Marjieh, Michael Hu, Robert D.The framework and code used in this work will be open-sourced, providing a valuable asset for the research community. The authors demonstrate that this framework can be used to train SoTA models for several embodied AI tasks. This engine, in combination with provided digital assets and environmental controls, allows for generating a combinatorially large number of diverse environments. The core of the framework is an engine for building procedurally-generated, physics-enabled environments with which agents can interact. This work provides a framework for training embodied AI agents on large quantities of data, creating the potential for such agents to benefit from scaling, as language and image generation models have. ProcTHOR: Large-Scale Embodied AI Using Procedural Generationīy Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Kiana Ehsani, Jordi Salvador, Winson Han, Eric Kolve, Aniruddha Kembhavi, Roozbeh Mottaghi.This paper is likely to be an important contribution in the evolution of both the understanding and implementation of Diffusion Process based models. In this case the focus on this paper are generative models of images that incoporate some form of Diffusion Process, which have become extremely popular recently despite the difficulties of training such models. This paper is an excellent demonstration of how a well thought through survey, that seeks not just to list but to organise prior research into a coherent common framework, can provide insights that then lead to new modelling improvements. Elucidating the Design Space of Diffusion-Based Generative Modelsīy Tero Karras, Miika Aittala, Timo Aila, Samuli Laine. ![]() The results are impressive and of interest to a broad audience. This inherently practical decoupling is likely to be a dominant paradigm for large scale text to image models. This work represents one of the state of the art of such models, but also innovates in demonstrating the effective combination of an independently trained large language model with an image decoder at scale. High quality generative models of images based on Diffusion Process are having a huge impact both within and beyond machine learning. Sara Mahdavi, Raphael Gontijo-Lopes, Tim Salimans, Jonathan Ho, David J Fleet, Mohammad Norouzi Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingīy Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S.As such, it has the potential for broad theoretical and practical impact in this important research area. This work also raises new theoretical questions, for example, about the learnability of near-OOD detection. It provides 3 concrete impossibility theorems, which can be easily applied to determine the feasibility of OOD detection in practical settings, and which was used in this work to provide a theoretical grounding for existing OOD detection approaches. The work uses probably approximately correct (PAC) learning theory to show that OOD detection models are PAC learnable only for some conditions of the space of data distributions and the space of prediction models. This work provides a theoretical study of out-of-distribution (OOD) detection, focusing on the conditions under which such models are learnable. Is Out-of-distribution Detection Learnable?īy Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu.For the Datasets and Benchmarks track, we thank Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Neil Lawrence, Olga Russakovsky, and Serena Yeung.Ĭongratulations to all authors! Outstanding Papers We thank the awards committee for the main track, Anima Anandkumar, Phil Blunsom, Naila Murray, Devi Parikh, Rajesh Ranganath, and Tong Zhang. We are excited to announce the award-winning papers for NeurIPS 2022! The three categories of awards are Outstanding Main Track Papers, Outstanding Datasets and Benchmark Track papers, and the Test of Time paper. By Alekh Agarwal, Alice Oh, Danielle Belgrave, Kyunghyun Cho, Deepti Ghadiyaram, Joaquin Vanschoren
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