Top Tweets for #statML
Sad after #AISTATS2025 and #ICLR2025 notifications? When a door closes, a bigger one opens 😜
If you have a fantastic paper on #uncertainty #AI #ML #causality #statML #probabilisticmodels #reasoning #impreciseprobabilities etc consider submitting to #UAI2025🇧🇷 deadline 10 Feb 💥
The 41st Conference on #Uncertainty in #AI will be held in Rio de Janeiro 🇧🇷, July 21-25!
The CfP is out 👉https://t.co/bFnMjEGWDe
🚨 Feb 10: Paper submission
🗣️ Apr 3-10: rebuttal period
🎉/💀 May 6: Author notification
#UAI2025 #ML #stats #learning #reasoning #uncertainty
🚨 Fully-funded PhD Positions in Statistical ML! 🚨
Passionate about bringing epistemic intelligence to machine learning? Join my lab @ CCDS NTU Singapore 🇸🇬!
👉 Learn more: https://t.co/k0lCzXNNq1
🙏 RTs appreciated!
#PhDRecruitment #StatML
New preprint out now on arXiv!
Dropout Regularization in Extended GLMs based on Double Exponential Families
by Benedikt Lütke Schwienhorst, Lucas Kock, David J. Nott and Nadja Klein from @RCTrustworthy @uniallianz (1,2,4) & @NUSingapore (3)
#statml #machinelearning
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families. (arXiv:2305.06625v1 [https://t.co/zjV5HgYw5a]) https://t.co/EoQqBshEs9
Check out the latest work from our #StatML student Michael Hutchinson (@MHutchinson141), to appear in #NeurIPS2021.
https://t.co/A9C4xRljym
Ever wondered how to create vector-valued Gaussian processes on arbitrary manifolds? Wonder no more!
Our latest paper Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels does exactly that!
Links to all: https://t.co/zYuzy5698J
🤖 Neural Ordinary Differential Equations
✍️ Tian Qi Chen et al.
🔗 https://t.co/uLwPdbtbAr
🔊 Tweeted by @DavidDuvenaud et al.
#Learning #csLG #csAI #statML
🤖 A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
✍️ Saurabh Arora, Prashant Doshi
🔗 https://t.co/p6N8VaX0hA
🔊 Tweeted by @icoxfog417 et al.
#Learning #csLG #statML
🤖 Polynomial Regression As an Alternative to Neural Nets
✍️ Xi Cheng et al.
🔗 https://t.co/sefzQnjrVY
🔊 Tweeted by @alex_yakubovich, @xamat et al.
#Learning #csLG #statML
🤖 Rotation Equivariant CNNs for Digital Pathology
✍️ Bastiaan S. Veeling et al.
🔗 https://t.co/eElvUrs8od
🔊 Tweeted by @BasVeeling, @jmtomczak et al.
#ComputerVisionandPatternRecognition #csCV #csLG #statML
🤖 Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
✍️ Kurtland Chua et al.
🔗 https://t.co/aE3Kii0MZQ
🔊 Tweeted by @RogerGrosse, @v_vashishta et al.
#Learning #csLG #csAI #csRO #statML
🤖 Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
✍️ Lechao Xiao et al.
🔗 https://t.co/03My6rcfSH
🔊 Tweeted by @johnplattml, @yasamanbb et al.
#MachineLearning #statML #csLG
🤖 Autoregressive Quantile Networks for Generative Modeling
✍️ Georg Ostrovski et al.
🔗 https://t.co/VKwoL2MRSI
🔊 Tweeted by @DeepMindAI, @mosko_mule, @Miles_Brundage et al.
#Learning#autoregressive #generativemodel #machinelearning #quantileregression #csLG #statML
Large-Scale Stochastic Sampling from the Probability Simplex (Baker et al.): stochastic gradient MCMC on the probability simplex via the stochastic CIR process https://t.co/MSh30PToGS #MCMC #MachineLearning #StatML #BayesianInference
🤖 Differentiable Compositional Kernel Learning for Gaussian Processes
✍️ Shengyang Sun et al.
🔗 https://t.co/eOK4keMWfm
🔊 Tweeted by @RogerGrosse et al.
#Learning#MachineLearning #ICML #csLG #statML
🤖 Yes, but Did It Work?: Evaluating Variational Inference
✍️ Yuling Yao et al.
🔗 https://t.co/SzjeGqXFyk
🔊 Tweeted by @_hylandSL et al.
#MachineLearning #statML #statCO
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