We are organizing an exciting workshop on the intersection of deep generative modelling and neurosymbolic learning at #ICLR2023 💎!

The call for papers is out now: https://nesygems.github.io/callforpapers/
Submission deadline: 3rd of February 2023

#neurosymbolic #generativemodels

Great news! Our hybrid workshop on Neurosymbolic Generative Models was accepted at ICLR 2023 🎉🎉🎉

More details on https://nesygems.github.io/

Co-organisers: @EmilevanKrieken, Halley Young, Disha Shrivastava, @jmtomczak and Kevin Ellis.

#neurosymbolic #generativemodelling #ML #AI

Time for an #introduction:

🎓 I'm a final year PhD student in machine learning at the University of Amsterdam.

👨‍🔬 My research primarily focuses on active learning and sensing. I'm also interested in AI alignment, AI for science, effective altruism, and everything Bayesian.

I also sing a lot. 🎶

Looking forward to seeing where this goes!

#introduction

I'm a senior researcher at Microsoft Research AI4Science in Amsterdam.

My research interests include AI4Science, single- and multi-agent reinforcement learning, and structure, symmetry, and equivariance in deep learning.

#reinforcementlearning #ai4science #machinelearning #deeplearning #equivariantagents

#Introduction

I'm a senior researcher at Microsoft Research Health futures in Amsterdam/Cambridge.

My research interests include invariance/equivariance, causality and representation learning, especially the interplay of these 3 things. All applied to medical imaging.

#machinelearning #deeplearning #causality #invariance

Hi all, my #introduction:
I'm a prof at #UCLA CS, living in #LosAngeles, and researching #ArtificialIntelligence.

I enjoy bridging #machinelearning with probabilistic and logical #reasoning.
That makes me work on probabilistic programming (#probprog), tractable probabilistic models (e.g., #probcircuit), and #neurosymbolic #AI.

Looking forward to some more authentic discourse about AI on this platform.

#introduction

I'm a CS prof at UT Austin. My original research background is in Programming Languages and Formal Methods, and these days, I'm mostly interested in the interface of these areas and ML. Technical themes in my work include neurosymbolic methods, program synthesis, coupling reasoning and learning, and interpretable/controllable ML. I am interested in applications of these ideas in ML for code/math, high-assurance autonomy, and scientific knowledge discovery.

Hi all, I am a research engineer at Huawei Research Vancouver. Homepage https://luxxxlucy.github.io

I am into probabilistic programming and program synthesis with a focus on mixed discrete and continuous variables. I worked on interpretability&explanation in ML, neural-symbolic AI.

I believe procedural program-like models are truly interpretable and maintainable and wish to discover such model from data.

#introduction #introductions 
#ai #machinelearning #deeplearning #logic #neurosymbolic

#introduction

Hi everyone! I'm a PhD student at the University of Toronto / Vector Institute, and a researcher at Google Brain. I'm interested in building probabilistic models of discrete objects, and in sequential decision making in the presence of uncertainty. Recently I've been thinking a lot about programming and program synthesis/induction, but I've also done some work with (discrete) diffusion models, contrastive learning, and music generation!