NLP for Mapping Physics Research with Matteo Chinazzi - #353

NLP for Mapping Physics Research with Matteo Chinazzi - #353

Predicting the future of science, particularly physics, is the task that Matteo Chinazzi, an associate research scientist at Northeastern University focused on in his paper Mapping the Physics Research Space: a Machine Learning Approach. In addition to predicting the trajectory of physics research, Matteo is also active in the computational epidemiology field. His work in that area involves building simulators that can model the spread of diseases like Zika or the seasonal flu at a global scale.

Jaksot(778)

Sensory Prediction Error Signals in the Neocortex with Blake Richards - #331

Sensory Prediction Error Signals in the Neocortex with Blake Richards - #331

Today we continue our 2019 NeurIPS coverage, this time around joined by Blake Richards, Assistant Professor at McGill University and a Core Faculty Member at Mila. Blake was an invited speaker at the Neuro-AI Workshop, and presented his research on “Sensory Prediction Error Signals in the Neocortex.” In our conversation, we discuss a series of recent studies on two-photon calcium imaging. We talk predictive coding, hierarchical inference, and Blake’s recent work on memory systems for reinforcement lea

24 Joulu 201940min

How to Know with Celeste Kidd - #330

How to Know with Celeste Kidd - #330

Today we’re joined by Celeste Kidd, Assistant Professor at UC Berkeley, to discuss her invited talk “How to Know” which details her lab’s research about the core cognitive systems people use to guide their learning about the world. We explore why people are curious about some things but not others, and how past experiences and existing knowledge shape future interests, why people believe what they believe, and how these beliefs are influenced, and how machine learning figures into the equation.

23 Joulu 201953min

Using Deep Learning to Predict Wildfires with Feng Yan - #329

Using Deep Learning to Predict Wildfires with Feng Yan - #329

Today we’re joined by Feng Yan, Assistant Professor at the University of Nevada, Reno to discuss ALERTWildfire, a camera-based network infrastructure that captures satellite imagery of wildfires. In our conversation, Feng details the development of the machine learning models and surrounding infrastructure. We also talk through problem formulation, challenges with using camera and satellite data in this use case, and how he has combined the use of IaaS and FaaS tools for cost-effectiveness and scalability

20 Joulu 201951min

Advancing Machine Learning at Capital One with Dave Castillo - #328

Advancing Machine Learning at Capital One with Dave Castillo - #328

Today we’re joined by Dave Castillo, Managing VP for ML at Capital One and head of their Center for Machine Learning. In our conversation, we explore Capital One’s transition from “lab-based” ML to enterprise-wide adoption and support of ML, surprising ML use cases, their current platform ecosystem, their design vision in building this into a larger, all-encompassing platform, pain points in building this platform, and much more.

19 Joulu 201947min

Helping Fish Farmers Feed the World with Deep Learning w/ Bryton Shang - #327

Helping Fish Farmers Feed the World with Deep Learning w/ Bryton Shang - #327

Today we’re joined by Bryton Shang, Founder & CEO at Aquabyte, a company focused on the application of computer vision to various fish farming use cases. In our conversation, we discuss how Bryton identified the various problems associated with mass fish farming, challenges developing computer algorithms that can measure the height and weight of fish, assess issues like sea lice, and how they’re developing interesting new features such as facial recognition for fish!

17 Joulu 201937min

Metaflow, a Human-Centric Framework for Data Science with Ville Tuulos - #326

Metaflow, a Human-Centric Framework for Data Science with Ville Tuulos - #326

Today we kick off our re:Invent 2019 series with Ville Tuulos, Machine Learning Infrastructure Manager at Netflix. At re:Invent, Netflix announced the open-sourcing of Metaflow, their “human-centric framework for data science.” In our conversation, we discuss all things Metaflow, including features, user experience, tooling, supported libraries, and much more. If you’re interested in checking out a Metaflow democast with Villa, reach out at twimlai.com/contact!

13 Joulu 201956min

Single Headed Attention RNN: Stop Thinking With Your Head with Stephen Merity - #325

Single Headed Attention RNN: Stop Thinking With Your Head with Stephen Merity - #325

Today we’re joined by Stephen Merity, an independent researcher focused on NLP and Deep Learning. In our conversation, we discuss Stephens latest paper, Single Headed Attention RNN: Stop Thinking With Your Head, detailing his primary motivations behind the paper, the decision to use SHA-RNNs for this research, how he built and trained the model, his approach to benchmarking, and finally his goals for the research in the broader research community.

12 Joulu 201959min

Automated Model Tuning with SigOpt - #324

Automated Model Tuning with SigOpt - #324

In this TWIML Democast, we're joined by SigOpt Co-Founder and CEO Scott Clark. Scott details the SigOpt platform, and gives us a live demo! This episode is best consumed by watching the corresponding video demo, which you can find at twimlai.com/talk/324.

9 Joulu 201946min

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