When the stakes are high, machine-learning models are sometimes used to aid human decision-makers. For instance, a model could predict which law school applicants are most likely to pass the bar exam to help an admissions officer determine which students should be accepted. These models often have millions of parameters, so how they make predictions …
Inside the womb, fetuses can begin to hear some sounds around 20 weeks of gestation. However, the input they are exposed to is limited to low-frequency sounds because of the muffling effect of the amniotic fluid and surrounding tissues. A new MIT-led study suggests that this degraded sensory input is beneficial, and perhaps necessary, for …
Read more “Early sound exposure in the womb shapes the auditory system”
Guðni Thorlacius Jóhannesson, the president of Iceland, visited MIT on Friday, engaging in talks with several campus leaders and professors, and touring the Media Lab. Jóhannesson visited the Institute along with a substantial delegation of officials and scholars from Iceland. They met with MIT scholars, who delivered a variety of presentations on research, design, and …
Read more “President Guðni Thorlacius Jóhannesson of Iceland visits MIT”
Posted by Akib Uddin, Product Manager and Andrew Sellergren, Software Engineer, Google Health Every year, nearly a billion chest X-ray (CXR) images are taken globally to aid in the detection and management of health conditions ranging from collapsed lungs to infectious diseases. Generally, CXRs are cheaper and more accessible than other forms of medical imaging. …
Read more “Simplified Transfer Learning for Chest Radiography Model Development”
Posted by Cat Armato, Program Manager, University Relations Google is a leader in machine learning (ML) research with groups innovating across virtually all aspects of the field, from theory to application. We build machine learning systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more. Core …
Posted by Dustin Tran and Balaji Lakshminarayanan, Research Scientists, Google Research Deep learning models have made impressive progress in vision, language, and other modalities, particularly with the rise of large-scale pre-training. Such models are most accurate when applied to test data drawn from the same distribution as their training set. However, in practice, the data …
I sometimes see people refer to neural networks as just “another tool in your machine learning toolbox”. They have some pros and cons, they work here or there, and sometimes you can use them to win Kaggle competitions. Unfortunately, this interpretation completely misses the forest for the trees. Neural networks are not just another classifier, …
Update Oct 18, 2017: AlphaGo Zero was announced. This post refers to the previous version. 95% of it still applies. I had a chance to talk to several people about the recent AlphaGo matches with Ke Jie and others. In particular, most of the coverage was a mix of popular science + PR so the most …
The accepted papers at ICML have been published. ICML is a top Machine Learning conference, and one of the most relevant to Deep Learning, although NIPS has a longer DL tradition and ICLR, being more focused, has a much higher DL density. Most mentioned institutions I thought it would be fun to compute some stats on …
Have you looked at Google Trends? It’s pretty cool — you enter some keywords and see how Google Searches of that term vary through time. I thought — hey, I happen to have this arxiv-sanity database of 28,303 (arxiv) Machine Learning papers over the last 5 years, so why not do something similar and take a look at how …