# NeurIPS NeurIPS is the annual Conference on Neural Information Processing Systems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## NeurIPS 2023: Our Favorite Papers on LLMs, Statistical Learning, and More DevFeed: [NeurIPS 2023: Our Favorite Papers on LLMs, Statistical Learning, and More]() Original publisher: [Read original article]() Author: Emily Majewski Published: 2024-03-21T19:52:06Z Content type: article Language: en Sources: [Two Sigma Engineering]() Topics: [NeurIPS](), [machine learning research](), [LLMs](), [LLM evaluation / benchmarking](), [Machine learning]() Tags: [evaluation](), [llms](), [machine-learning-research](), [neurips](), [papers]() ### AI overview Two Sigma reviews selected papers and presentations from NeurIPS 2023, with particular attention to large language models and statistical learning. It discusses research arguing that some apparent emergent abilities in LLMs may result from nonlinear metrics, limited evaluation resolution, and insufficient sampling. ### Source excerpt The post NeurIPS 2023: Our Favorite Papers on LLMs, Statistical Learning, and More appeared first on Two Sigma. ## Grading Complex Interactive Coding Programs with Reinforcement Learning DevFeed: [Grading Complex Interactive Coding Programs with Reinforcement Learning]() Original publisher: [Read original article]() Author: A Href; Allen Nie; Emma Brunskill; Chris Piech Published: 2022-03-28T07:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [rlvr](), [Code Challenge](), [Artificial Intelligence](), [Programming](), [NeurIPS](), [browser]() Tags: [agents](), [algorithms](), [browser](), [coding](), [courses](), [games](), [machine-learning](), [neurips](), [programming](), [reinforcement-learning](), [speed]() ### AI overview This article presents the Play to Grade Challenge, which applies reinforcement-learning methods for game-playing AI agents to the automated grading of complex interactive coding assignments. It explains why games and interactive applications are difficult to evaluate automatically and describes the challenge introduced in a NeurIPS 2021 paper. ### Source excerpt [Summary] tl;dr: A tremendous amount of effort has been poured into training AI algorithms to competitively play games that computers have traditionally had trouble with, such as the retro games published by Atari, Go, DotA, and StarCraft II. The practical machine learning knowledge accumulated in developing these algorithms has paved the way for people to now routinely train game-playing AI agents for many games. Following this line of work, we focus on a specific category of games - those developed by students as part of a programming assignment. Can the same algorithms that master Atari games help us grade these game assignments? In our recent NeurIPS 2021 paper, we illustrate the challenges in treating interactive coding assignment grading as game playing and introduce the Play to Grade Challenge. Introduction Massive Online Coding Education has reached striking success over the past decade. Fast internet speed, improved UI design, code editors that are embedded in a browser window allow educational platforms such as Code.org to build a diverse set of courses tailored towards students of different coding experiences and interest levels (for example, Code.org offers "Star War-themed coding challenge," and "Elsa/Frozen themed for-loop writing"). As a non-profit organization, Code.org claims to have reached over 60 million learners across the world 1. Such organizations typically provide a variety of carefully constructed teaching materials such as videos and programming challenges. A challenge faced by these platforms is that of grading assignments. It is well known that grading is critical to student learning 2, in part because it motivates students to complete their assignments. Sometimes manual grading can be feasible in small settings, or automated grading used in simple settings such as when assignments are multiple choice or adopt a fill-in-the-blink modular coding structure. Unfortunately, many of the most exciting assignments, such as developing games or i ## Stanford AI Lab Papers and Talks at NeurIPS 2021 DevFeed: [Stanford AI Lab Papers and Talks at NeurIPS 2021]() Original publisher: [Read original article]() Author: Compiled by Drew A. Hudson Published: 2021-12-06T08:00:00Z Content type: article Language: en Sources: [The Stanford AI Lab Blog]() Topics: [NeurIPS](), [Artificial Intelligence](), [Reverse Engineering](), [data](), [Reinforcement learning]() Tags: [ai](), [generative](), [imitation](), [learning](), [models](), [neural-networks](), [neurips](), [reinforcement-learning](), [reverse-engineering](), [transformers]() ### AI overview Stanford AI Lab presents its research at NeurIPS 2021, including work on generative models, recurrent and transformer-based architectures, state-space models, emergent communication, reinforcement learning, imitation learning, and neural coding. ### Source excerpt The thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021 is being hosted virtually from Dec 6th - 14th. We're excited to share all the work from SAIL that's being presented at the main conference, at the Datasets and Benchmarks track and the various workshops, and you'll find links to papers, videos and blogs below. Some of the members in our SAIL community also serve as co-organizers of several exciting workshops that will take place on Dec 13-14, so we hope you will check them out! Feel free to reach out to the contact authors and the workshop organizers directly to learn more about the work that's happening at Stanford! Main Conference Improving Compositionality of Neural Networks by Decoding Representations to Inputs Authors: Mike Wu, Noah Goodman, Stefano Ermon Contact: wumike@stanford.edu Links: Paper Keywords: generative models, compositionality, decoder Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Authors: Jimmy T.H. Smith, Scott W. Linderman, David Sussillo Contact: jsmith14@stanford.edu Links: Paper | Website Keywords: recurrent neural networks, switching linear dynamical systems, interpretability, fixed points Compositional Transformers for Scene Generation Authors: Drew A. Hudson, C. Lawrence Zitnick Contact: dorarad@cs.stanford.edu Links: Paper | Github Keywords: GANs, transformers, compositionality, scene synthesis Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers Authors: Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Chris Ré Contact: albertgu@stanford.edu Links: Paper Keywords: recurrent neural networks, rnn, continuous models, state space, long range dependencies, sequence modeling Emergent Communication of Generalizations Authors: Jesse Mu, Noah Goodman Contact: muj@stanford.edu Links: Paper | Video Keywords: emergent communication, multi-agent communication, language grounding, compositionality Deep Lear ## NIPS 2017 Summary DevFeed: [NIPS 2017 Summary]() Original publisher: [Read original article]() Published: 2017-12-11T00:00:00Z Content type: article Language: en Sources: [Alex Korbonits]() Topics: [NeurIPS](), [AI Research](), [Deep learning](), [Deep neural networks](), [Reinforcement learning]() Tags: [ai-research](), [deep-learning](), [fairness](), [machine-learning](), [model](), [model-interpretability](), [neurips](), [open-source](), [papers](), [reinforcement-learning](), [research]() ### AI overview A summary of key themes from NIPS 2017, including rapid progress in Bayesian deep learning, growing attention to model interpretability, bias, and fairness, the need for more theory in deep learning, advances in deep reinforcement learning, and ongoing questions about GANs. ### Source excerpt Key takeaways from NeurIPS 2017: Bayesian deep learning, model interpretability, fairness, and the state of AI research. ## Notes from NIPS 2017 on geometric deep learning, GAN theory, reinforcement learning, fairness, and Bayesian deep learning DevFeed: [Notes from NIPS 2017 on geometric deep learning, GAN theory, reinforcement learning, fairness, and Bayesian deep learning]() Original publisher: [Read original article]() Published: 2017-12-04T12:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [NeurIPS](), [Deep learning](), [Machine learning](), [Graphs](), [Reinforcement learning]() Tags: [3d](), [bias](), [deep-learning](), [fairness](), [graphs](), [ml](), [neurips](), [reinforcement-learning]() ### AI overview Personal notes from attending NIPS 2017 cover geometric deep learning on manifolds and graphs, Bayesian deep learning, fairness and bias, theory, deep reinforcement learning, and GANs. The article also reflects on how those research themes developed over the following years. ### Source excerpt Notes from NIPS 2017 -- covering geometric deep learning, GAN theory, reinforcement learning, fairness in ML, Bayesian deep learning, and more.