| Date | Activity | Name | Title | Video
Available | Slides
Available |
|---|
| 2025-11-04 | colloq | | From Laplacians to Learning: Bridging Networks, Dynamics, and Chaos | Yes | No |
| 2025-05-06 | gensym25 | | QI learning group | No | No |
| 2024-09-30 | nsbp_iss | | Learning More about Physics Education Research | Yes | No |
| 2024-08-29 | firstbillion_c24 | | What do we learn from Clustering of Galaxies and SMBHs at Cosmic Dawn? (R) | Yes | Yes |
| 2024-05-08 | tde24 | | What can we learn from black hole x-ray binaries? | Yes | No |
| 2024-02-13 | hartle_c24 | | Some Things I Learned from Jim | Yes | Yes |
| 2024-02-09 | starswithlars_c24 | | How I Learned to Stop Worrying and Love White Dwarfs | Yes | No |
| 2023-12-21 | deeplearning23 | | Perspectives on deep learning and motor neuroscience | Yes | No |
| 2023-12-19 | deeplearning23 | | How two-layer neural networks learn, one (giant) step at a time | Yes | No |
| 2023-12-14 | deeplearning23 | | Inductive bias towards simplicity and feature learning in DNNs | Yes | No |
| 2023-12-14 | deeplearning23 | | From Bayes to Gibbs: a thermodynamic theory of learning | Yes | No |
| 2023-12-14 | deeplearning23 | | Understanding Deep-Learning as a physicist: what would Einstein do? | Yes | No |
| 2023-12-13 | pti23 | | Orthogonal weights, feature learning, and exactly marginal effective theories | Yes | No |
| 2023-12-12 | pti23 | Silverstein, Eva (Stanford) Related Words: ads/cft, cosmology, discussion, double, ds, gravity, inflation, large, new, physics, quantum, string, theory | Learning algorithms from physics: Microcanonical HMC sampling and Energy Conserving Descent optimization | Yes | No |
| 2023-12-07 | deeplearning23 | | Brain evolution as a machine learning algorithm | Yes | No |
| 2023-12-07 | deeplearning23 | | Some ideas and speculation about robustness, development. and learning in brains and artificial neural networks | Yes | No |
| 2023-12-05 | deeplearning23 | | Translating Theory to Practical Deep Learning: Depthwise Hyperparameter Transfer | Yes | No |
| 2023-12-05 | deeplearning23 | | The Neural Race Reduction: Feature learning dynamics in deep architectures | Yes | No |
| 2023-12-05 | deeplearning23 | | Learning the code of large neural populations with shallow networks and homeostatic random projections | Yes | No |
| 2023-11-28 | deeplearning23 | | Continual learning - the biological way | Yes | Yes |
| 2023-11-22 | deeplearning23 | | Beyond the Kernel Regime: Analytical Approaches to Single and Sequential Task Learning | Yes | No |
| 2023-11-22 | deeplearning23 | | Statistical physical insights into the dynamics of learning algorithms | Yes | Yes |
| 2023-11-21 | deeplearning23 | | Data dependence and abrupt transitions during in-context learning | Yes | No |
| 2023-11-20 | bblunch | | Statistical Mechanics of Deep Learning | Yes | No |
| 2023-11-16 | deeplearning23 | | Rich and Lazy neurons: network connectivity structure and the double implications of feature learning for generalization | Yes | No |
| 2023-11-16 | deeplearning23 | | Understanding the Universality Phenomenon in High-Dimensional Estimation and Learning: Some Recent Progress | Yes | No |
| 2023-10-25 | interfaces_c23 | | In-Context Operator Networks (ICON): Towards Large Scientific Learning Models | Yes | Yes |
| 2023-10-24 | manybody23 | | Machine learning the superfluid phase diagram of the BCS/BEC crossover | Yes | No |
| 2023-07-21 | brainlearn23 | | Keynote: Statistical learning in sensorimotor control | Yes | No |
| 2023-07-20 | brainlearn23 | | Love, death, and statistical learning | Yes | No |
| 2023-07-19 | brainlearn23 | | How flies got their sensilla? Statistical learning on evolutionary timescales | Yes | No |
| 2023-07-19 | brainlearn23 | | Mastering learning about relations: a honeybee perspective | Yes | No |
| 2023-07-19 | brainlearn23 | | A bottom-up sensory pathway for reward associative learning | Yes | No |
| 2023-07-19 | brainlearn23 | | Combining brain-wide connectivity maps with brain-wide activity and behaviour maps to understand learning in Drosophila | Yes | No |
| 2023-07-18 | brainlearn23 | | Mathematical framework of learning as inference and control | Yes | No |
| 2023-07-18 | brainlearn23 | | How can we use the architecture of learning circuits to provide clues about learning algorithms and constrain learning models? | Yes | No |
| 2023-07-17 | brainlearn23 | | Prediction as a learning objective | Yes | No |
| 2023-07-14 | brainlearn23 | | Parsing statiscal learning | Yes | No |
| 2023-07-12 | brainlearn_c23 | All participantsRelated Words: 1, 2, 20, 4, bloc, breakout, bring, closing, discussion, room, summary | General discussion: What data are needed to drive conceptual advances about learning? | Yes | No |
| 2023-07-12 | brainlearn_c23 | | Principles of operation of a cerebellar learning circuit | Yes | Yes |
| 2023-07-10 | brainlearn_c23 | | Input- and Target-Specific Synaptic Plasticity in Neocortical Networks During Sensory Learning | Yes | No |
| 2023-07-07 | brainlearn23 | | Discussion: internal beliefs, uncertainty and learning | Yes | No |
| 2023-07-07 | brainlearn23 | | Internal beliefs, uncertainty and learning | Yes | No |
| 2023-07-06 | brainlearn23 | | Introduction to the session and Representation of Primitives for Statistical Learning in the Auditory System | Yes | No |
| 2023-07-06 | brainlearn23 | | Statistical learning in single neurons: data and models | Yes | No |
| 2023-07-05 | brainlearn23 | All participantsRelated Words: 1, 2, 20, 4, bloc, breakout, bring, closing, discussion, room, summary | Dimensions of learning: Where does Statistical Learning map to? | Yes | No |
| 2023-06-30 | brainlearn23 | | Solvable models of deep learning dynamics, predictive coding and statistical learning | Yes | No |
| 2023-06-29 | brainlearn23 | | Modeling longer versus recent statistics - learning different contexts | Yes | No |
| 2023-06-27 | brainlearn23 | | Consequences of statistical learning on perception & working memory | Yes | Yes |
| 2023-06-27 | brainlearn23 | | Rapid emergence of latent knowledge in cortical networks drives learning | Yes | No |
| 2023-06-23 | brainlearn23 | | Beyond pairwise associations: what and how do artificial neural networks learn from them? | Yes | Yes |
| 2023-06-23 | brainlearn23 | | Bayesian chunk learning: beyond pairwise associations, beyond modalities | Yes | No |
| 2023-06-20 | brainlearn23 | | Roundtable discussion: From sensory modes to neural modes to behavioral modes: low-dimensional representations everywhere! The role of dimensionality and geometry of neural representations in statistical learning. | Yes | No |
| 2023-06-20 | brainlearn23 | | Neural manifolds and learning | Yes | No |
| 2023-06-16 | brainlearn23 | | Roundtable discussion: Why are learning and memory studied separately? Are there different types of statistical learning by memory system? Why do we learn so much but remember so little early in life? | Yes | No |
| 2023-06-16 | brainlearn23 | | Review: What is the relationship between statistical learning and episodic memory? | Yes | No |
| 2023-06-15 | brainlearn23 | | Math vs. Brains - Can/should all statistical learning problems be framed as reinforcement learning? | Yes | Yes |
| 2023-06-15 | brainlearn23 | | Introduction to the neurobiology and systems neuroscience view of statistical learning | Yes | No |
| 2023-06-14 | brainlearn23 | | Discussion: Challenges in building theories of statistical learning | Yes | No |
| 2023-06-14 | brainlearn23 | | Learning complex neural codes | Yes | No |
| 2023-06-14 | brainlearn23 | | Statistical learning: frequencies, pair-wise interactions, and more | Yes | No |
| 2023-06-13 | brainlearn23 | | What is statistical learning? | Yes | No |
| 2023-05-11 | nanoassembly23 | | Machine learning Nanoparticle Assemblies | Yes | No |
| 2023-03-24 | galevo_c23 | | Galaxy Zoo in the Deep Learning Era | Yes | No |
| 2023-03-23 | galevo_c23 | | Learning to Simulate the Universe with Deep Learning | Yes | No |
| 2023-03-23 | galevo_c23 | | Outliers: how I learned to love them, and why you should too | Yes | No |
| 2023-03-22 | galevo_c23 | | Unsupervised learning for stellar spectra with deep normalizing flows | Yes | No |
| 2023-03-22 | galevo_c23 | | Learning representations of galaxies from simulations and observations | Yes | No |
| 2023-03-22 | galevo_c23 | | Learning from simulations | Yes | No |
| 2023-03-22 | galevo_c23 | | Learning Galaxy Properties from Merger Trees | Yes | No |
| 2023-03-15 | galevo23 | | Review talk on combining the power of citizen science and machine learning | Yes | No |
| 2023-03-09 | bblunch | Wechsler, Risa (Stanford) Related Words: aud, cam, connection, dark, galaxies, galaxy, galaxy-halo, group, halo, kitp, podcast | Learning from billions of galaxies: Can our understanding of galaxy formation keep up with the upcoming data revolution? | Yes | No |
| 2023-03-09 | galevo23 | Huertas-Company, Marc (Instituto de Astrofisica de Canarias / Observatoire de Paris / Flatiron Institute) | Self-supervised learning | Yes | No |
| 2023-03-01 | galevo23 | | Machine Learning and Astronomy | Yes | No |
| 2023-02-21 | galevo23 | | Machine learning methods with resolved stars | Yes | No |
| 2023-01-24 | bootstrap_c23 | | What we can learn from being Free and Tensionless | Yes | Yes |
| 2023-01-24 | galevo23 | | Tutorial: The galaxy-halo connection and Machine Learning approaches | Yes | No |
| 2023-01-18 | galevo23 | | Tutorial: Large-scale galaxy formation simulations and Machine Learning approaches | Yes | No |
| 2022-11-23 | whitedwarfs22 | | What will we learn about white dwarfs in binaries from gravitational waves? | Yes | No |
| 2022-11-14 | whitedwarfs_c22 | | Machine Learning to Constrain the Initial-Final Mass Relation of White Dwarf Stars | Yes | Yes |
| 2022-11-01 | multiphase_c22 | | Particle-based methods for cloud microphysics: towards learning climate model parameterizations from libraries of particle simulations | Yes | Yes |
| 2022-09-13 | dynisq_c22 | | Learning global charges from local measurements | Yes | Yes |
| 2022-09-13 | dynisq_c22 | | Learning Entanglement in Quantum Simulation | Yes | No |
| 2022-09-12 | dynisq_c22 | | To learn and cancel quantum noise: Probabilistic error cancellation with sparse Pauli-Lindblad models on noisy quantum processors | Yes | Yes |
| 2022-08-08 | neuroloco22 | | Modeling animal learning | Yes | No |
| 2022-06-24 | adapt22 | | Can deep learning help us detect polygenic adaptation from empirical data? | Yes | No |
| 2022-04-19 | gwaves_c22 | | Things I don't know about gravitational-wave astrophysics (but would like to learn) | Yes | No |
| 2022-02-08 | qcomp22 | | Experimental advantages in learning and what quantum computer science has to teach us about chemistry | Yes | No |
| 2022-01-27 | qcomp22 | | Generative Quantum Learning of Multivariate Distributions | Yes | No |
| 2022-01-13 | qcomp22 | | Learning molecular structure from NMR spectra with a quantum computer | Yes | No |
| 2021-12-13 | bblunch | | Machine Learning and the Physics of Climate | Yes | No |
| 2021-12-08 | climate21 | | Statistical learning of climate for large ensemble ice sheet simulations | Yes | Yes |
| 2021-12-06 | climate21 | | Bridging observations and numerical modeling using machine learning | Yes | No |
| 2021-12-01 | climate21 | | Symbolic regression and mathematical postprocessing for machine learning of (climate) dynamics | Yes | No |
| 2021-11-17 | climate21 | | Learning cause-and-effect relationships from time series data | Yes | No |
| 2021-11-04 | climate_c21 | | Manifold learning as a tool to link AI/ML and climate dynamics | Yes | Yes |
| 2021-11-04 | climate_c21 | | Machine Learning and Earth System Modeling: from parameter calibration to feature detection | Yes | Yes |
| 2021-11-04 | climate_c21 | | Deep learning and energy models for fine dead wood segmentation | Yes | No |
| 2021-11-03 | climate_c21 | | Atmospheric radiation: using machine learning for the unknowable and uncomputable | Yes | Yes |
| 2021-11-03 | climate_c21 | | A deep learning parameterization of gravity wave drag coupled to an atmospheric global climate model | Yes | No |
| 2021-11-02 | climate_c21 | | Representation learning and custom loss functions for atmospheric data | Yes | Yes |
| 2021-11-02 | climate_c21 | | Deep Learning for Subseasonal Global Precipitation Prediction | Yes | Yes |
| 2021-11-02 | climate_c21 | | Deep Unsupervised Learning for Climate Informatics | Yes | Yes |
| 2021-11-02 | climate_c21 | | Revealing the Impact of Global Heating on the Meridional Overturning Circulation with transparent machine learning | Yes | No |
| 2021-11-01 | climate_c21 | | Towards using machine learning in real climate models | Yes | Yes |
| 2021-11-01 | climate_c21 | | Interpretable and Generalizable Machine Learning for Fluid Dynamics | Yes | Yes |
| 2021-11-01 | climate_c21 | | Exploring physical and Machine Learning approaches for stochastic modeling and ensemble prediction of weather and climate | Yes | No |
| 2021-11-01 | climate_c21 | | Physics-Guided Deep Learning for Fluid Dynamics | Yes | Yes |
| 2021-11-01 | climate_c21 | | Machine Learning for Ocean Closures: Advances and Lessons | Yes | No |
| 2021-09-29 | universality_c21 | | Extracting complexity of quantum dynamics using machine learning | Yes | Yes |
| 2021-08-05 | topology21 | | Quantum Variational Learning of the Entanglement Hamiltonian | Yes | No |
| 2021-03-30 | precision21 | | QCD and Jets through the Lens of Machine Learning | Yes | Yes |
| 2021-03-18 | precision21 | | Unsupervised Learning for Fun and Precision | Yes | Yes |
| 2019-07-30 | morpho19 | | How tissues can actively avoid rupture (things we've learned from Placozoa) | Yes | Yes |
| 2019-06-25 | gravast_c19 | | Lessons Learned from GW170817 | Yes | No |
| 2019-06-18 | gravast19 | | On multiband GW astronomy: What are we learning from gravitational wave observations of merging binaries, and what do we need to learn more? | Yes | Yes |
| 2019-05-21 | exostar_c19 | | What can we learn from the Sun? | Yes | Yes |
| 2019-05-15 | gravast19 | | What have we really learned about the nuclear equation of state from GW170817? | Yes | Yes |
| 2019-05-14 | gravast19 | | What have we really learned about the nuclear equation of state from GW170817? | Yes | Yes |
| 2019-05-06 | gaia19 | | Using cosmological simulations to learn about the Milky Way in the context of Gaia | Yes | Yes |
| 2019-04-25 | colloq | | Sweating the small stuff: Or how I learned to START worrying and love the smallest galaxies | Yes | No |
| 2019-03-21 | machine19 | | Discussion: Introduction to kernel methods for machine learning | Yes | No |
| 2019-03-13 | machine19 | | Tutorial: Overview of classical and quantum learning theory | Yes | No |
| 2019-03-12 | machine19 | | Learning matched action parameters for multi-scale algorithms in lattice QCD | Yes | Yes |
| 2019-02-28 | machine19 | | Quantum error correction and machine learning | Yes | Yes |
| 2019-02-28 | scape19 | | Generalisation dynamics of online learning in over-parameterised neural networks | Yes | No |
| 2019-02-27 | scape19 | | Learning of discrete graphical models | Yes | No |
| 2019-02-26 | machine19 | | Reinforcement Learning to Control Quantum Systems away from Equilibrium | Yes | Yes |
| 2019-02-25 | bblunch | | Dreaming, computing, inspiring: Flavors of machine learning in many-body quantum physics | Yes | No |
| 2019-02-21 | machine19 | | Tutorial: Variational Monte Carlo and Machine Learning | Yes | No |
| 2019-02-16 | machinet_c19 | | Introduction to Deep Learning | Yes | Yes |
| 2019-02-16 | machinet_c19 | | How machine learning is revolutionizing drug discovery and material design: Improving electronic structure calculations | Yes | Yes |
| 2019-02-15 | machine_c19 | | Machine Learning Geometry and String Theory | Yes | Yes |
| 2019-02-15 | machine_c19 | | Physics-inspired Machine Learning: Non-equilibrium, Perturbation Theory, and Goldstone Modes | Yes | No |
| 2019-02-15 | machine_c19 | | Machine Learning for Lattice Quantum Field Theory Calculations | Yes | Yes |
| 2019-02-14 | machine_c19 | | Learning Quantum States with Generative Models | Yes | Yes |
| 2019-02-14 | machine_c19 | | Machine Learning Quantum Emergence | Yes | Yes |
| 2019-02-14 | machine_c19 | | Uncovering the Behavior of Quantum Annealers with Statistical Learning | Yes | Yes |
| 2019-02-13 | machine_c19 | | Mapping Distant Galaxies with Machine Learning | Yes | No |
| 2019-02-13 | machine_c19 | | Successes and Perspectives of Deep Learning Applications to Neutrino Physics | Yes | No |
| 2019-02-12 | machine_c19 | | Machine Learning in Electronic Structure: Finding Better Density Functionals than Humans do | Yes | Yes |
| 2019-02-12 | machine_c19 | | Machine Learning and Enhanced Sampling | Yes | No |
| 2019-02-11 | machine_c19 | | Deep Learning on Graphs: from Astrophysics to Fake News Detection | Yes | No |
| 2019-02-11 | machine_c19 | | Rough-glassy Landscapes from Inference to Machine Learning | Yes | Yes |
| 2019-02-11 | machine_c19 | | Statistical Physics and Machine Learning | Yes | Yes |
| 2019-02-11 | machine_c19 | | Mean Field Concepts in Machine Learning | Yes | No |
| 2019-02-07 | machine19 | | Generation of topologically constrained states through deep reinforcement learning | Yes | Yes |
| 2019-02-06 | machine19 | | Tutorial: Reinforcement Learning for Physicists | Yes | No |
| 2019-02-05 | machine19 | | Reinforcement learning for fault-tolerant quantum computation | Yes | Yes |
| 2019-01-31 | machine19 | | Quantum Loop Topography for Machine Learning Transport | Yes | Yes |
| 2019-01-29 | machine19 | | Learning and representing quantum states with probability | Yes | Yes |
| 2019-01-15 | scape19 | | Thermodynamics of trajectories, optimal dynamics and reinforcement learning | Yes | No |
| 2019-01-10 | scape_c19 | | Dynamics of Neural Networks with Learning Rules Inferred from Data | Yes | Yes |
| 2019-01-10 | scape_c19 | | Iterative Projective Approach for Linear Systems with Link to Deep Learning | Yes | Yes |
| 2019-01-10 | scape_c19 | | Energy Landscapes: from Molecules and Nanodevices to Machine Learning | Yes | Yes |
| 2019-01-09 | scape_c19 | | TUTORIAL: Optimization Methods from a Machine Learning Perspective | Yes | No |
| 2018-10-23 | dynq18 | | Reinforcement Learning: Introduction and Applications to Nonequilibrium Dynamics | Yes | No |
| 2018-09-13 | brain18 | | Learning metrics of neural population codes and stimuli | Yes | No |
| 2018-09-11 | brain18 | | Neural constraints on learning | Yes | No |
| 2018-09-07 | brain18 | | Cerebellar learning using perturbation | Yes | Yes |
| 2018-08-08 | snav18 | | Depth Learning: how zebrafish come to navigate and balance in the water column | Yes | Yes |
| 2018-02-15 | memform_c18 | | Phase Transitions in Machine Learning and Distributed Control | Yes | No |
| 2018-02-15 | memform_c18 | | Statistical physics of learning a rule: Decades old story continued | Yes | Yes |
| 2018-02-12 | bio99 | | Uncovering how the brain learns | Yes | No |
| 2018-01-10 | memform18 | | Generalization, learning and memories | Yes | Yes |
| 2017-11-28 | qinfo17 | | Mathematical Overview of Machine Learning | Yes | No |
| 2017-10-20 | qinfo17 | | Machine learning wavefunctions with restricted Boltzmann machines | Yes | No |
| 2017-08-31 | intertwined17 | | Quantum Loop Topography for Machine learning on topological phase, phase transitions, and beyond | Yes | Yes |
| 2017-08-24 | ecoevo17 | | What can be learned about adaptation from E&R experiments in Drosophila? | Yes | No |
| 2017-08-14 | ecoevo17 | | Re-learning how to do phenomenological theories and why this is so important in biology[Podcast][Aud][Cam] KITP Blackboard Lunch | Yes | No |
| 2017-08-09 | ecoevo17 | | What can we learn from experimental evolution in sexual populations? | Yes | Yes |
| 2017-08-08 | intertwined17 | | Itinerant quantum critical points and self-learning quantum Monte Carlo method | Yes | Yes |
| 2017-07-27 | intertwined17 | All ParticipantsRelated Words: 1, 2, 20, 4, bloc, breakout, bring, closing, discussion, room, summary | Informal Discussion on Machine Learning Application to Condensed Matter Physics | Yes | No |
| 2017-06-08 | hearing17 | | Life-long learning in machine recognition of speech | Yes | No |
| 2017-05-19 | galhalo_c17 | | What Can We Learn from Small-scale Clustering? | Yes | Yes |
| 2017-04-12 | stars17 | | What can we learn from massive stars population synthesis? | Yes | No |
| 2016-11-21 | topoquant16 | | Machine learning topological phases | Yes | Yes |
| 2016-11-08 | synquant16 | | Machine learning for many body systems | Yes | Yes |
| 2015-10-14 | mbl15 | | High order correlations and what we can learn about the solution for many body problems from experiment | Yes | No |
| 2015-09-11 | undergrad15 | | Machine Learning for Designing DNA-stabilized Silver Clusters | Yes | Yes |
| 2015-06-16 | qgravity15 | | What I Learned from AMPS | Yes | No |
| 2015-06-16 | smell15 | | Early olfactory processing is shaped by previously learned information | Yes | No |
| 2014-09-19 | superbugs14 | | What have we learned? | Yes | No |
| 2014-08-25 | lasers14 | | What we learn about super-intense interactions from intermediate intensity experiments | Yes | Yes |
| 2014-03-05 | neuro14 | | Watching the hippocampal network learn during trace conditioning: sequential activity and correlations | Yes | Yes |
| 2014-03-05 | neuro14 | | Learning & Memory in the Head-Direction Cell Circuit | Yes | Yes |
| 2014-02-27 | neuro14 | | Circuit Events in the Hippocampus during Goal-Oriented Spatial Learning | Yes | Yes |
| 2014-02-24 | neuro14 | | Neurophysics of Space, Time and Learning[Podcast][Aud][Cam] KITP Blackboard Lunch | Yes | No |
| 2014-02-04 | neuro14 | | Spatial Learning and Neural Coding in Virtual Reality | Yes | Yes |
| 2013-09-30 | geoflows13 | | Vortices, streaks and Coherent Structures: Lessons learned from the turbulence transition in pipe flow | Yes | Yes |
| 2013-06-27 | primocosmo13 | | What can we learn about inflation from B-modes measurements beyond "r" | Yes | No |
| 2013-06-07 | kohnfest13 | | Much of the physics I know I learned from Walter-- but not all of it | Yes | No |
| 2013-03-13 | coldmoles_c13 | | State-to-State Dynamics in Ultracold Collisions: What Can We Learn From High Resolution Spectroscopy of Weakly Bound Molecular Complexes? | Yes | Yes |
| 2013-03-06 | resident | | Salacious Science: Subtitle: What I Learned from Writing an Infamous Sex Column | Yes | No |
| 2013-03-02 | qcontrolt_c13 | | Active Learning and Quantum Simulations in the Classroom | Yes | Yes |
| 2013-03-01 | qcontrol_c13 | | Robust Online Hamiltonian Learning | No | No |
| 2013-03-01 | qcontrol_c13 | | Learning Algorithms for Designing Efficient, Precise, Robust, Single-shot, Quantum- enhanced, adaptive parameter estimation policies | No | No |
| 2013-02-27 | qcontrol_c13 | | Quantum Control and Chemistry: Learning from Each Other | Yes | No |
| 2012-05-02 | bitbranes12 | | Discussion: What Have We Learned About Info Loss/Conservation From 1+1 BHs? | Yes | No |
| 2011-07-21 | brain_m11 | | Biologically Constrained Learning (cont'd) | Yes | No |
| 2011-07-20 | brain_m11 | | Biologically Constrained Learning | Yes | Yes |
| 2011-05-26 | turbulence11 | | Turbulence on Petascale Computers: What Have We Learned, and What We Hope to Learn | Yes | Yes |
| 2010-12-10 | resident | | How to Make Good Science into a Great Talk? Learn Secrets from Theatre -- an Interactive Workshop | Yes | No |
| 2010-12-07 | compqcm10 | | What Can We Learn About Many-body Entanglement from Holography | Yes | Yes |
| 2010-09-30 | neuro10 | | Functions and Mechanisms of Behavioral Variability During Motor Learning | Yes | No |
| 2010-09-23 | neuro10 | | Neural Theory and Practice of Song Learning | Yes | No |
| 2010-02-10 | materials_c10 | | Superconducting Materials: What We Learn From the Heavy Fermions | Yes | Yes |
| 2009-11-30 | qinfo09 | | Learning Much from Little: Compressed Sensing Approach to Quantum State Tomography and Other Ideas of Systems Identification | Yes | Yes |
| 2009-09-23 | qinfo09 | | Predictive Quantum Learning | Yes | Yes |
| 2009-08-19 | sdeath_c09 | | What We Learn from Observing SNe Remnants | Yes | Yes |
| 2009-07-09 | qcontrol09 | | NMR Pulse Design by OCT: Introduction, Applications and What We Can Learn From It | Yes | Yes |
| 2008-10-15 | genetics08 | | Learning from Re-Sequencing Data: What To Do When the $1000 Genome Arrives? | Yes | Yes |
| 2008-09-30 | milkyway_c08 | | Neutral Gas in the Milky Way: Some Things We've Learned (and Some We'd Still Like to Know) | Yes | Yes |
| 2008-04-08 | brain08 | | What can we learn from synaptic weight distributions? | Yes | Yes |
| 2007-11-09 | stars07 | | Wrap-up: What did we learn this week? | Yes | Yes |
| 2007-09-26 | colloq | | Crackling Noise: Learning from Magnets about Earthquakes? | Yes | No |
| 2007-03-23 | snovae_c07 | | Lessons Learned from SNe Ia at z>1 | Yes | No |
| 2007-03-22 | snovae_c07 | | What has been learned about Type Ia SN from their Remnants | Yes | Yes |
| 2006-09-13 | strings06 | | LHC and String Theory: What will we Learn | Yes | No |
| 2006-06-11 | bio_c06 | | How can future researchers learn to distinguish a critical biological problem from a mundane one? | Yes | No |
| 2006-05-23 | spintr06 | | What You Can Learn from Ferromagnetism in III-V Semiconductors and Its Manipulation | Yes | No |
| 2004-11-17 | qcd_c04 | | RHIC experimental overview: What we have (not) learned | No | Yes |
| 2004-10-28 | igm_c04 | | What More can we Learn from QSO Absorption Lines? | No | Yes |
| 2004-09-30 | brain04 | | Fluctuation-Dissipation Theorem and Models of Learning | Yes | No |
| 2004-09-21 | brain04 | | A Synaptic Theory of Gradient Learning with Empiric Inputs | Yes | Yes |
| 2004-09-20 | igm04 | | How I Stopped Worrying and Learned to Love Baryons[Aud][Cam] | Yes | No |
| 2004-09-10 | brain04 | | Active Learning by Extraction of Predictive Information | Yes | No |
| 2004-09-08 | brain04 | | Machine Learning for Sensorimotor Processing | Yes | Yes |
| 2004-08-12 | brain04 | | Minimum Population Information: More on the Link Between Information, Learning, and Neurophysiology | Yes | Yes |
| 2003-03-03 | neutrino_c03 | | What can we learn about Neutrinos from Galaxy Distributions? | No | No |
| 2003-01-30 | clusters_c03 | | What we have Learned from Extragalactic GCs about Globular Cluster and Galaxy Formation | No | Yes |
| 2002-05-02 | astro99 | | Accretion Disk Turbulence, Or: How I Learned to Stop Worrying and Love Rotation | No | Yes |
| 2001-12-05 | neuro01 | | Learning to See | No | No |
| 2001-11-26 | bblunch | | Learning to See: The Development of Maps in the Brain | No | No |
| 2001-10-12 | colloq | | Predictability, Complexity and Learning | No | Yes |
| 2001-10-12 | neuro01 | | Predictability, Complexity and Learning | No | Yes |
| 2001-09-25 | neuro01 | | Associative Learning and Delayed Neuronal Activity | No | Yes |
| 2001-03-09 | pcgm17 | | What can we learn about small objects in AdS? | No | Yes |
| 2000-12-06 | hightc00 | | What Can We Learn from Comparison between Cuprates and He Films? -- Phase Separation and Fluctuating Superfluidity | No | No |
| 2000-10-23 | hightc00 | | What can we Learn about High Tc from Higher Dimensional Systems | No | Yes |
| 1999-03-30 | bhole99 | | Learning about Central Engines from Relativistic Outflows, Part II | No | Yes |
| 1999-03-24 | bhole99 | | Learning about Central Engines from Relativistic Outflows | No | Yes |
| 1998-03-04 | colloq | | The Next Generation Cosmic Microwave Background Measurements and What We Hope to Learn About the Early Universe | No | Yes |
| 1997-12-02 | snu | | What have we learned? Where are we headed? | No | Yes |
