Mizzou IEEE CIS

Hi MU CISers, Today, we will have paper discussion start with Chapter 4 of "Learning Deep Architectures for AI" at 3 pm in Ketcham Auditorium W1005.

Hi MU CISers, Today, we will have paper discussion start with Chapter 2 of "Learning Deep Architectures for AI" at 3 pm in Ketcham Auditorium W1005. It is a great opportunity for everyone to address what you understand about this topic or to ask questions about what you would like to learn. So I want to encourage everyone of us to attend. If you have friends who are interested in the area and want to join, please feel free to invite them. You can also contact me or Dr. DeSouza to add him/her on the list.

Hi MU CISers, Tomorrow, we will have our first paper discussion on "Learning Deep Architectures for AI" at 3 pm in Ketcham Auditorium W1005. Likely we will start with the first couple of chapters. Depends on people's experience with Deep Learning area, we will decide to change the pace of the reading either faster or slower. Here is the link to the dropbox folder:... https://www.dropbox.com//lbvunt/AACUXlfF85HXaJQp_I7jCBgaa See more

To promote more group collaboration during the seminar series, in stead of doing a weekly presentation, we will start a biweekly group discussion on papers assigned by faculty members. For example, this week is still going to be the presentation but next week will be the group discussion. We will assign the paper for discussion a week before (probably on Tuesday) to give everyone enough time to read and study. Let me know if you have any questions regarding to this new approach of the CIS seminar!

Tomorrow, Jiao Changzhe will present a seminar on "Weakly Supervised Large Scale Object Localization with Multiple Instance Learning and Bag Splitting" at 3:00PM in Naka 120. (This is the last time using Naka 120. For the rest of this semester, all the seminar series will be held at Ketcham Auditorium W1005) Abstract of the paper: Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition task. Previous efforts have... required carefully designed features and have difficulty in handling images with cluttered backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object localization model from large-scale data without using bounding box annotations. We integrate rich general prior knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones. We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013 detection dataset, and our method outperforms supervised deformable part-based model without using box annotations. See more