Machine learning (ML) solutions to challenging networking problems are a promising avenue but the lack of interpretability and the behavioral uncertainty affect trust and hinder adoption. A key advantage of ML algorithms and architectures, such as deep neural networks and reinforcement learning, is that they can discover solutions that are attuned to specific problem instances. As an example, consider a video bit rate adaptation logic that is tuned specifically for Netflix clients in the United States. Yet, there is a general fear that ML systems are black boxes.
AI technologies, such as machine learning, are seeing increasing adoption in adversarial settings. One important domain in which AI techniques are particularly promising is detection: for example, one can, in principle, use data to learn how to detect a host of malicious activities, including malware and intrusions. A key challenge in detection is how to trade off the consequences of a failure to detect malicious activity and the cost of false alarms, especially in the case when the malicious party makes deliberate attempts to avoid being detected.
Codecinella is partnering with W-ACM to hold a tech interview sprint.
Interviewing can be stressful. Come to practice your interview skills with professional women software developers. You will gain some practice with a mock technical interview. Bring at least three copies of your current resume. Volunteers will give you feedback on your resume as well as on your interview.
The CS department Fall Picnic will be on Friday, Sept 28th at 4pm in Edward Klief Park! This picnic is for all CS undergraduates, graduates, faculty, and staff. Families are also invited. Past picnics have featured volleyball, frisbee, touch football, soccer, and croquet in addition to the food and drink. The picnics are well attended by faculty and students alike.
Come join us for food, conversation, and sports. SACM will be providing burgers, brats, soda, and snacks.
Abstract: We present an algorithm for creating high resolution anatomically plausible images that are consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images.
Mathematical logic was developed in an effort to provide formal foundations
for mathematics. In this quest, which ultimately failed, logic begat
computer science, yielding both computers and theoretical computer science.
But then logic turned out to be a disappointment as foundations for
computer science, as almost all decision problems in logic are either
unsolvable or intractable. Starting from the mid 1970s, however, there
has been a quiet revolution in logic in computer science, and problems that