-
ACMMM 20222022Actor identification and localization in movies and TV series seasons can enable deeper engagement with the content. Manual actor identification and tagging at every time-instance in a video is error prone as it is a highly repetitive, decision intensive and time-consuming task. The goal of this paper is to accurately label as many faces as possible in the video with actor names. We solve this problem using
-
IJCAI 2022 AI Safety Workshop2022Contextual bandits are widely used across the industry in many applications such as search engines, dialogue systems, recommendation systems, etc. In such applications, it is often necessary to update the policy regularly as the data distribution changes and new features are being on-boarded frequently. As any new policy deployment directly impacts the user experience, safety in model updates is an important
-
KDD 2022 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)2022Online advertising opportunities are sold through auctions, billions of times every day across the web. Advertisers who participate in those auctions need to decide on a bidding strategy: how much they are willing to bid for a given impression opportunity. Deciding on such a strategy is not a straightforward task, because of the interactive and reactive nature of the repeated auction mechanism. Indeed,
-
ICPR 20222022Detecting audio-video (A/V) synchronization error is important to measure end user experience. Today, researchers in this domain are mainly focused on contents such as movies or sports. The state of art algorithms usually first detect a specific type of events and then correlate the A/V data within during these events, e.g., find the human chatting events and then correlate the vocals with the lip shapes
-
ICML 2022 Workshop on Continuous Time Methods for Machine Learning2022Time series forecasting is a fundamental problem in machine learning with relevance to many applications including supply chain management, finance, healthcare, etc. As an example, consider a large e-commerce retailer with a system to produce forecasts of the demand distribution for a set of products at a target time T. Using these forecasts as an input, the retailer can then optimize buying and placement
Related content
-
May 13, 2020Watch the ICLR 2020 keynote presentation by Michael I. Jordan, Amazon scholar and UC Berkeley professor.
-
May 08, 2020Watch a video tutorial presented by AWS deep learning scientists and engineers at The Web Conference 2020.
-
May 07, 2020Dive into Deep Learning combines detailed instruction and math with hands-on examples and code.
-
May 04, 2020View recording of tutorial presented at The Web Conference 2020.
-
April 28, 2020Industry collaborators create nine-part series that provides theoretical underpinnings for explainable AI and industry case studies.
-
April 24, 2020Amazon’s Stefano Soatto on how learning representations came to dominate machine learning.