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Crowdsourcing in computer vision. A Kovashka, O Russakovsky, L Fei-Fei, K Grauman. Foundations and Trends® in computer graphics and Vision 10 (3), 177-243. , 2016. 158. 2016. REVISE: A tool for measuring and mitigating bias in visual datasets. A Wang, A Liu, R Zhang, A Kleiman, L Kim, D Zhao, I Shirai, A Narayanan, ...
Matthew Coleman, Olga Russakovsky, +1 author. Ye Zhu. Published 2023. Computer Science. TLDR. Three diffusion-guidance techniques with a reduced representation of the state provided by quantile discretization are proposed: a gradient-based approach, a stochastic beam search approach, and a Q-learning approach. Expand. openreview.net.
Dr. Olga Russakovsky is an Associate Professor in the Computer Science Department at Princeton University. Her research is in computer vision, closely integrated with the fields of machine learning, human-computer interaction and fairness, accountability and transparency. She has been awarded the PAMI Young Researcher Award, the NSF CAREER ...
13 de nov. de 2018 · by Richard S. Sutton and Andrew G. Barto. Hardcover. $100.00. Hardcover. ISBN: 9780262039246. Pub date: November 13, 2018. Publisher: The MIT Press. 552 pp., 7 x 9 in, 64 color illus., 51 b&w illus. MIT Press Bookstore Penguin Random House Amazon Barnes and Noble Bookshop.org Indiebound Indigo Books a Million.
- Seeing Our World
- Helping Computers Understand Us
- Making Ai Smarter
- Expanding The Community
Improving our ability to capture and analyze images is an essential part of bringing human, or even superhuman, visual abilities to machines such as cellphones, robots and health devices. Felix Heideis one of the researchers who is developing AI methods to improve the computer’s eye, the camera. His goal is to help cameras evolve to the point where...
Princeton’s Natural Language Processing group aims to make computers understand and use human language effectively. The group was started by two assistant professors of computer science, Danqi Chen and Karthik Narasimhan, and includes Sanjeev Arora, Princeton’s Charles C. Fitzmorris Professor in Computer Science. Chen is working to develop machines...
AI has caught up with humans in many ways, becoming as good as we are at recognizing familiar images, translating languages and converting text to speech. And AI can do these things faster than most humans can. But can AI really help people create, and innovate? “We have generative models that synthesize new pictures and text,” explained Adams, a p...
The rapid adoption of AI must be accompanied by addressing questions about racial and gender biases in AI algorithms. Russakovskyis one of the researchers in the field grappling with ethical questions from the engineering perspective. “We’re starting to ask — as engineers, as builders of these systems — what can we do to ensure that they are equall...
The survey goes on to cover the Convolutional Neural Network (CNN), the Recurrent Neural Network (RNN) including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), the Auto-Encoder (AE), the Deep Belief Network (DBN), the Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).
Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning. Chelsea Finn. Sergey Levine. Large, diverse data. Broad generalizaon. (+ large models) Russakovsky et al. ‘14. GPT-2. Radford et al. ‘19. Under the paradigm of. Vaswani et al. ‘18. supervised learning. What if you don’t have a large dataset? medical imaging. robocs.
