On March, I attended the machine learning conferene ML Conf in NYC.
Here are links shared from the conferences:
- Slides
- [Book Discount (40%)](https://mlconf.com/blog/ tweet-for-a-chance-to-win-free-books-at-mlconf-nyc-this-friday/) ctwmlconfny19
- Speaker Resources
- Videos
Here are a list of my favorite talks.
Augmenting Mental Health Care in the Digital Age: Machine Learning as a Therapist Assistant (Talkspace)
- Slides , Video
- talked how to detect “crisis” in communications
- trained models from proxy data and mental health subreddits ( r/mentalhealth, r/physcology, r/bipolar)
- Multi task learning: one model trains/predicts crisis risk label, subreddit label, primary diagnosis
- how internal stakeholders evaluate models using Lime and ELI5
- stack: scikit-learn, pytorch, ELi5, Lime, dash, aws, jupyterhub, redshift, sagemaker, plotly, dash
- speakers third talk in an event; company paid for him and two other coworkers to attend
Putting the Tech in Biotech: Challenges and Opportunities in Application of AI in Healthcare (Amgen)
- How Amgen uses ML.
- Identify fractures use computer vision
- using clinical icd embeddings
Machine Learning to Detect Illegal Online Sales of Prescription Opioids (Intuit)
- Slides , Video
- Detect sales of opiods from online pharmacies by using twitter’s public streaming api for 5 months.
- Used lda to train a topic model to identify tweets from those pharmacies .
Using Network Analysis to Detect Kickback Schemes Among Medical Providers (Elder Research)
- Slides , Video
- Framed Kickback as a graph connectivity problem.
- 9 of the 12 doctors they identified is actively being investigated
Building Machine Learning Models with Strict Privacy Boundaries (Slack)
- Slides , Videos
- If u build a word2vec model on all slack communication, word similarity might reveal secret project keywords.
- Talked about how to train one model but at runtime use different params
Recommendations in a Marketplace: Personalizing Explainable Recommendations with Multi-objective Contextual Bandits: (Spotify)
- Slides , Video
- Talked about training models that have different objective functions (music that user likes, promoting new artists….)
- Very detailed slides (90+)
Representations from natural language data: successes and challenges: (Google)
- Slides , Video
- Decent overview of what BERT is.
- Before BERT , people were training different model architectures for different nlp tasks ( NER, Q/A, Intent)
Teaching a Machine to Code (Microsoft)
- Slides , Video
- Some of the models that power visual studio code.
- Method suggestion models (<5mb non dl model 50% accuracy; 78% accuracy for deep learning 40MB)
Increasing the Impact of AI Through Better Software (Facebook)
- Slides , Video
- Where facebook uses ML. (Social recommendation, machine translation, accessibility for images, crisis text line)
- Responses to facebook posts, include people offering to help self harm :(
- Decent overview of pytorch
Reducing Operational Barriers to Model Training (Sigopt)
Deep Learning Applications to Online Payment Fraud Detection (Paypal)