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Peter McMahan<p>I'm explaining Hamiltonian Monte Carlo in my grad-level stats class tomorrow, so I put together this animation illustrating HMC in one dimension. I find it very soothing.</p><p><a href="https://mas.to/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a> <a href="https://mas.to/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://mas.to/tags/posterior" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>posterior</span></a> <a href="https://mas.to/tags/stats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stats</span></a> <a href="https://mas.to/tags/r" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>r</span></a> <a href="https://mas.to/tags/rlang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rlang</span></a> <a href="https://mas.to/tags/statistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>statistics</span></a> <a href="https://mas.to/tags/MCMC" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MCMC</span></a></p>
Dr. Anna Latour<p>I'm teaching my first lecture at the new job today, about probabilistic logic programming, probabilistic inference, and (weighted) model counting.</p><p>Some of the required reading is a paper (<a href="https://eccc.weizmann.ac.il/eccc-reports/2003/TR03-003/index.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">eccc.weizmann.ac.il/eccc-repor</span><span class="invisible">ts/2003/TR03-003/index.html</span></a>) that was written by a great mentor of mine, prof. dr. Fahiem Bacchus. He passed away just over 2 years ago, and I am honoured to keep his memory alive by teaching his ideas to a new generation of students. Hope to do him proud. 🌱 </p><p>Please send good vibes? 🥺 </p><p><a href="https://mathstodon.xyz/tags/AcademicChatter" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicChatter</span></a> <a href="https://mathstodon.xyz/tags/AcademicLife" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicLife</span></a> <a href="https://mathstodon.xyz/tags/AcademicMastodon" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AcademicMastodon</span></a> <a href="https://mathstodon.xyz/tags/Teaching" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Teaching</span></a> <a href="https://mathstodon.xyz/tags/Probability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Probability</span></a> <a href="https://mathstodon.xyz/tags/ProbabilisticInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProbabilisticInference</span></a> <a href="https://mathstodon.xyz/tags/Probabilities" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Probabilities</span></a> <a href="https://mathstodon.xyz/tags/Logic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Logic</span></a> <a href="https://mathstodon.xyz/tags/LogicProgramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>LogicProgramming</span></a> <a href="https://mathstodon.xyz/tags/PropositionalModelCounting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PropositionalModelCounting</span></a> <a href="https://mathstodon.xyz/tags/ProbabilisticLogicProgramming" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ProbabilisticLogicProgramming</span></a> <a href="https://mathstodon.xyz/tags/ModelCounting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ModelCounting</span></a> <a href="https://mathstodon.xyz/tags/PropositionalLogic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PropositionalLogic</span></a> <a href="https://mathstodon.xyz/tags/WeightedModelCounting" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>WeightedModelCounting</span></a> <a href="https://mathstodon.xyz/tags/DPLL" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DPLL</span></a> <a href="https://mathstodon.xyz/tags/BayesianProbability" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianProbability</span></a> <a href="https://mathstodon.xyz/tags/BayesNets" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesNets</span></a> <a href="https://mathstodon.xyz/tags/BasianStatistics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BasianStatistics</span></a> <a href="https://mathstodon.xyz/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://mathstodon.xyz/tags/BayesianNetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianNetworks</span></a> <a href="https://mathstodon.xyz/tags/KnowledgeCompilation" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>KnowledgeCompilation</span></a> <a href="https://mathstodon.xyz/tags/DecisionDiagrams" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DecisionDiagrams</span></a> <a href="https://mathstodon.xyz/tags/BinaryDecisionDiagrams" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BinaryDecisionDiagrams</span></a></p>
Ranjith Jaganathan<p>"Dear all,</p><p>We are thrilled to announce the inaugural <a href="https://neuromatch.social/tags/ComputationalPsychiatry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalPsychiatry</span></a> Conference to take place at Trinity College Dublin on July 6-8th, 2023 (<a href="https://neuromatch.social/tags/cpconf2023" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>cpconf2023</span></a>) </p><p><a href="https://www.cpconf.org/" rel="nofollow noopener" target="_blank"><span class="invisible">https://www.</span><span class="">cpconf.org/</span><span class="invisible"></span></a></p><p>One of the key aims of <a href="https://neuromatch.social/tags/ComputationalNeuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalNeuroscience</span></a> is to construct theoretical accounts of normal mental function that link characterizations of <a href="https://neuromatch.social/tags/neurobiology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neurobiology</span></a>, <a href="https://neuromatch.social/tags/psychology" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psychology</span></a> and aspects of the environment. In Computational Psychiatry (CP), these theories, realized in models at various scales, are used to elucidate dysfunction. </p><p>The 2023 Computational Psychiatry Conference (7th and 8th July) will contain six sessions, each with a keynote talk from senior faculty and also contributed talks and panel discussions. </p><p>The session themes will include Diagnostics, Reinforcement Learning models, Individual-level prediction, Development, Animal models and Treatments. There will also be poster sessions on both days. </p><p>The tutorial session (afternoon of 6th July) will contain three introductory talks on <a href="https://neuromatch.social/tags/psychiatry" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>psychiatry</span></a> for non-clinicians, <a href="https://neuromatch.social/tags/BehaviouralModelling" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BehaviouralModelling</span></a> using <a href="https://neuromatch.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> and <a href="https://neuromatch.social/tags/ReinforcementLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ReinforcementLearning</span></a>, and <a href="https://neuromatch.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>MachineLearning</span></a>.</p><p>Abstract submissions will be closed on March 15th, 2023. We will be able to support 10 participants with a travel award based on a competitive review of their abstract submissions. Top submissions will also be invited as talks.</p><p>We look forward to seeing everyone in Dublin this summer!"</p>
Cedric Archambeau<p>Today, we open sourced Fortuna (<a href="https://github.com/awslabs/fortuna" rel="nofollow noopener" target="_blank"><span class="invisible">https://</span><span class="">github.com/awslabs/fortuna</span><span class="invisible"></span></a>) a library for uncertainty quantification.<br>Deep neural networks are often overconfident and do not know what they don’t know. Quantifying the uncertainty in the predictions they make will help deploy deep learning more responsibly and more safely.<br><a href="https://sigmoid.social/tags/responsibleAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>responsibleAI</span></a> <a href="https://sigmoid.social/tags/ConformalPrediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ConformalPrediction</span></a> <a href="https://sigmoid.social/tags/BayesianInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BayesianInference</span></a> <a href="https://sigmoid.social/tags/UncertaintyQuantification" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>UncertaintyQuantification</span></a> <a href="https://sigmoid.social/tags/deeplearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>deeplearning</span></a> <a href="https://sigmoid.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a></p>