Noah D. Goodman
Identifiers
- name variant Noah D. Goodman 0.60 · backfill
Papers (26)
- CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning cs.AI · 2026 · author #5
- Measuring Progress Toward AGI: A Cognitive Framework cs.AI · 2026 · author #11
- Neural Garbage Collection: Learning to Forget while Learning to Reason cs.LG · 2026 · author #4
- GIANTS: Generative Insight Anticipation from Scientific Literature cs.CL · 2026 · author #8
- Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs cs.CL · 2025 · author #5
- Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking cs.CL · 2024 · author #6
- Learning to Explain: Answering Why-Questions via Rephrasing cs.CL · 2019 · author #3
- ShapeGlot: Learning Language for Shape Differentiation cs.CL · 2019 · author #4
- Joint Mapping and Calibration via Differentiable Sensor Fusion cs.CV · 2018 · author #5
- Pyro: Deep Universal Probabilistic Programming cs.LG · 2018 · author #10
- An Incremental Iterated Response Model of Pragmatics cs.CL · 2018 · author #2
- Planning, Inference and Pragmatics in Sequential Language Games cs.CL · 2018 · author #2
- Evaluating Compositionality in Sentence Embeddings cs.CL · 2018 · author #5
- DisSent: Sentence Representation Learning from Explicit Discourse Relations cs.CL · 2017 · author #3
- Learning Disentangled Representations with Semi-Supervised Deep Generative Models stat.ML · 2017 · author #5
- Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding cs.CL · 2017 · author #3
- Inducing Interpretable Representations with Variational Autoencoders stat.ML · 2016 · author #6
- Deep Amortized Inference for Probabilistic Programs cs.AI · 2016 · author #3
- The Language of Generalization cs.CL · 2016 · author #2
- Learning to Generate Compositional Color Descriptions cs.CL · 2016 · author #2
- Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks cs.GR · 2016 · author #4
- Learning the Preferences of Ignorant, Inconsistent Agents cs.AI · 2015 · author #3
- Coarse-to-Fine Sequential Monte Carlo for Probabilistic Programs cs.AI · 2015 · author #4
- C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching cs.AI · 2015 · author #3
- A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs cs.AI · 2012 · author #2
- Inducing Probabilistic Programs by Bayesian Program Merging cs.AI · 2011 · author #3
Mentions
- 2605.28742 #5 · arxiv_oai · confidence 0.70 Noah D. Goodman
- 2605.28405 #11 · arxiv_oai · confidence 0.70 Noah D. Goodman
- 1206.3555 #2 · backfill · confidence 0.70 Noah D. Goodman
- 1110.5667 #3 · backfill · confidence 0.70 Noah D. Goodman
- 2503.01307 #5 · arxiv_oai · confidence 0.70 Noah D. Goodman
- 2403.09629 #6 · arxiv_oai · confidence 0.70 Noah D. Goodman
Frequent Coauthors
- Andreas Stuhlm\"uller 5 shared papers
- Christopher Potts 3 shared papers
- Daniel Ritchie 3 shared papers
- N. Siddharth 3 shared papers
- Robert X.D. Hawkins 3 shared papers
- Alban Desmaison 2 shared papers
- Allen Nie 2 shared papers
- Anikait Singh 2 shared papers
- Brooks Paige 2 shared papers
- Erin D. Bennett 2 shared papers
- Frank Wood 2 shared papers
- Fritz Obermeyer 2 shared papers
- Jan-Willem van de Meent 2 shared papers
- Jonathan P. Chen 2 shared papers
- Michael Y. Li 2 shared papers
- Paul Horsfall 2 shared papers
- Philip H.S. Torr 2 shared papers
- Pushmeet Kohli 2 shared papers
- Will Monroe 2 shared papers
- Alison M. Snyder 1 shared papers