What drives innovation in machine learning?
In a new paper, we argue that application-driven work is systemically under-valued in the machine learning community, but that it's essential for both innovation and impact.
https://t.co/EJiKbILW6O
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@IAPR_TC7 Is it possible to understand the drivers of these changes in the ice shelves for example by including meteorological variables apart from SAR data?
Known for shedding light on the AI model, XAI has the potential to unveil new knowledge for applications in climate science – but which of the many XAI methods can we trust and should we choose?
See our preprint
https://t.co/X4L47Nj362
@TUBerlin_UMI with @Marlene_Climate
It's been almost ~5yrs since I returned to India after 15yrs in the US. I've written about it before but here is my story 5yrs later. It's been a ride & frankly one of my life's best decisions! If you are considering a move back to India, hope this helps:
https://t.co/BXv8RACH8x
Continuing the conversation on forest carbon credits quality, @PachamaInc just published a Research Brief, outlining our tech-driven approach to computing robust algorithmic baselines with validated uncertainty for avoided deforestation crediting.
https://t.co/PJHAjGVPKH
Super excited to present (albeit virtually) our @manpa_97 work on Mapping Biogeophysical Parameters Using Satellite Data and Geospatial Technologies at #AGU22. Drop by to hear me talk about it on Wed 14 at 20:55!
@theAGU@ClimateResearch@HariniNagendra
What an inspiring and exciting day it was at Slush 2022! Hearing about startups solving a wide range of problems from hyperspectral remote sensing to climate-friendly neo banks, kudos to the @SlushHQ team! #slush2022
In 2018, three colleagues and I published a front-cover paper in @PNASNews
It has > 170 citations as of 2022, and it was a success that might not have happened:
• We had $0 budget
• We had no PI (Principal Investigator)
• We had no credentials
[Here's how we did it] 🧵