Explaining Blended Matching Pursuit: A Multi-Purpose AI Algorithm

By Cyrille Combettes This is an informal summary of our recent paper Blended Matching Pursuit with my advisor Sebastian Pokutta. It will be presented at the Conference on Neural Information Processing Systems (NeurIPS) in Vancouver, British Columbia, Dec. 8-14, 2019. In this post, we motivate and explain the main ideas behind the design of our … Continue reading Explaining Blended Matching Pursuit: A Multi-Purpose AI Algorithm

Making Artificial Intelligence Work in a Changing Environment

By Adrian Rivera Cardoso and He Wang Machine learning (ML) is changing our lives. We can instantly translate from one language to another, search entire libraries in a matter of seconds, and even prevent credit card fraud. ML’s success is mostly due to the power of artificial neural networks — a machine learning model inspired … Continue reading Making Artificial Intelligence Work in a Changing Environment

Explaining Nonparametric Regression on Low Dimensional Manifolds using Deep Neural Networks

By Minshuo Chen Background and Motivation Deep learning has made significant breakthroughs in various real-world applications, such as computer vision, natural language processing, healthcare, robotics, etc. In image classification, the winner of the $latex 2017$ ImageNet challenge retained a top-$latex 5$ error rate of $latex 2.25\% $ [1], while the data set consists of about … Continue reading Explaining Nonparametric Regression on Low Dimensional Manifolds using Deep Neural Networks

Artificial Intelligence System Gives Fashion Advice

People turn to many different sources for clothing style advice, from magazines to best friends to Instagram. Soon, though, you may be able to ask your smartphone. A University of Texas at Austin computer science team, in partnership with researchers from Cornell Tech, Georgia Tech and Facebook AI Research, has developed an artificial intelligence system … Continue reading Artificial Intelligence System Gives Fashion Advice

Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded

By Ramprasaath R. Selvaraju Many popular and well-performing models for multi-modal, vision and language tasks exhibit poor visual grounding -- failing to appropriately associate words or phrases with the image regions they denote and relying instead on superficial linguistic correlations. For example, answering the question “What color are the bananas?” with yellow regardless of their … Continue reading Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded

Embodied Amodal Recognition: Learning to Move to Perceive Objects

By Jianwei Yang and Zhile Ren With the rapid development of computer vision, several technologies such as object detection and image classification are becoming mature and effective. Those vision algorithms play important roles in many real-world systems, enabling applications ranging from augmented reality to self-driving cars.  The pipeline for designing a typical computer vision system … Continue reading Embodied Amodal Recognition: Learning to Move to Perceive Objects

Overcoming Large-scale Annotation Requirements for Understanding Videos in the Wild

By Min-Hung Chen, Zsolt Kira and Ghassan AlRegib Videos have become an increasingly important type of media from which we obtain valuable information and knowledge. This motivates the need for the development of video analysis techniques. The development of these techniques could, for example, provide recommendations or support discovery for different objectives. Given the recent … Continue reading Overcoming Large-scale Annotation Requirements for Understanding Videos in the Wild