Interested in behavior and computation. Research Scientist @MPI_animalbehav
Konstanz, Baden-Württemberg
Joined March 2009
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Jake Graving retweeted
When I talk about "causal salad", this is what I mean: no consideration of how the covariates relate to one another or the treatment.
Jake Graving retweeted
Awesome finale that @JTKerby's image made the @AnimalEcology cover in support of our method using drones to study animals in their natural social and physical landscapes! @adwait_d @jgraving @BlairRCostelloe @icouzin great team!
🔥JAE July Issue!
On the cover: Many animals behave in the context of dynamic social and physical landscapes. This is certainly the case in a band of gelada monkeys at the Guassa Community Conservation Area in Ethiopia.
📸by Jeff Kerby
Full issue 👉besjournals.onlinelibrary.wi…
Jake Graving retweeted
I'm really proud of this work published today in Proc B with @SmithBeeLab & @ben_koger on the importance of the 3D nest structure and building strategies in developing honeybee colonies doi.org/10.1098/rspb.2022.25…
We observe, manipulate, and model 3D nest construction - see below.
Jake Graving retweeted
The #ImagingHangar @UniKonstanz is abuzz with the sound of 60k #locust feet. More than 4k locusts have been tagged with reflective markers for tracking with the Motion Capture System. Researchers from @CBehav and @MPI_animalbehav aim to understand the behaviour of locust swarms.
Jake Graving retweeted
Out now in @AnimalEcology! A general approach for using drones to study animal behavior in the wild. Record the location and posture of many animals simultaneously at sub-second sub-meter resolution, plus reconstruct their 3D landscape: doi.org/10.1111/1365-2656.13…
Jake Graving retweeted
Our drone-based method for tracking the (geo-referenced) location and body-postures of free-roaming animals, including 3D landscape models and social context - out now! besjournals.onlinelibrary.wi… @ben_koger @BlairRCostelloe @CollectiveBehav @MPI_animalbehav @UniKonstanz @CBehav
Jake Graving retweeted
New blog post: Collective Intelligence for Deep Learning
Recently, @yujin_tang and I published a paper about how ideas like swarm behavior, self-organization, emergence are gaining traction in deep learning.
I wrote a blog post summarizing the key ideas:
blog.otoro.net/2022/10/01/co…
Jake Graving retweeted
The #CASCB is super excited: Currently we are running an experiment on locust swarms in the #ImagingHangar @UniKonstanz. Normally #locusts are studied in the lab in small groups of 200 animals in small arenas despite swarming in groups of millions of individuals in the wild.
Jake Graving retweeted
Come join our team as one of 12 PhD students in the new #WildDrone network! Visit wilddrone.eu/ for project descriptions and application information
12 PhD positions: We are looking for 12 Doctoral Candidates to join the WildDrone MSDN, which aims to revolutionize wildlife conservation practices across European and African countries using aerial robotics, computer vision, and wildlife ecology. See wilddrone.eu/recruitment
Jake Graving retweeted
Happy to announce the final release of seaborn 0.12.0, a major update with new features that I'm really excited about.
Check out the highlights: medium.com/@michaelwaskom/an…
Read the full release notes: seaborn.pydata.org/whatsnew/…
pip install seaborn==0.12.0
I hope you find it useful!
Jake Graving retweeted
If you have behavioral videos and want to try out keypoint discovery, we've open-sourced B-KinD: github.com/neuroethology/BKi…
You can train and run B-KinD on videos without human annotations!
Thanks to Serim Ryou for working with me on the code😊 Let us know if you have questions!
Annotating keypoints is expensive! We introduce B-KinD, a keypoint discovery method that works on a variety of behavioral videos without human annotations. Tested on:🐭🪰🌳🚶
Our work will be presented @CVPR in June 2022.
Paper: arxiv.org/abs/2112.05121
Code: Coming soon!
1/5
Jake Graving retweeted
Happy to announce that our Python package for active inference, pymdp, is now published in JOSS @JOSS_TheOJ:
"pymdp: A Python library for active inference in discrete state spaces"
Paper: joss.theoj.org/papers/10.211…
Code: github.com/infer-actively/py…
Docs: pymdp-rtd.readthedocs.io
Explainable models of behavior are a worthwhile goal for sure, but be careful when using these tools to make causal scientific claims! Most of these approaches can quickly break down into nonsense when viewed through the lens of causal inference. A thread...👇
Another @GoldenNeuron collaboration between myself and @nilssonsro hot off the press - in this current opinion we present that explainability and transparency metrics are the next critical direction for use of ML in behavioral classification. authors.elsevier.com/c/1ezqJ… 1/6
To summarize, off-the-shelf tools typically make bad assumptions about behavioral data, but methods exist to avoid falling into *some* but not *all* of these logical traps. Science is incredibly hard, but we think carefully about the models we're using to make inferences