Poster Papers
24,624 papers found • Page 438 of 493
Conference
PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer
Tongkun Guan, Chengyu Lin, Wei Shen et al.
Position: $C^*$-Algebraic Machine Learning $-$ Moving in a New Direction
Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri
Position: A Call for Embodied AI
Giuseppe Paolo, Jonas Gonzalez-Billandon, Balázs Kégl
Position: A Call to Action for a Human-Centered AutoML Paradigm
Marius Lindauer, Florian Karl, Anne Klier et al.
Position: AI/ML Influencers Have a Place in the Academic Process
Iain Xie Weissburg, Mehir Arora, Xinyi Wang et al.
Position: AI-Powered Autonomous Weapons Risk Geopolitical Instability and Threaten AI Research
Riley Simmons-Edler, Ryan Badman, Shayne Longpre et al.
Positional Knowledge is All You Need: Position-induced Transformer (PiT) for Operator Learning
Junfeng CHEN, Kailiang Wu
Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
Martina G. Vilas, Federico Adolfi, David Poeppel et al.
Position: Application-Driven Innovation in Machine Learning
David Rolnick, Alan Aspuru-Guzik, Sara Beery et al.
Position: A Roadmap to Pluralistic Alignment
Taylor Sorensen, Jared Moore, Jillian Fisher et al.
Position: A Safe Harbor for AI Evaluation and Red Teaming
Shayne Longpre, Sayash Kapoor, Kevin Klyman et al.
Position: Automatic Environment Shaping is the Next Frontier in RL
Younghyo Park, Gabriel Margolis, Pulkit Agrawal
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla et al.
Position: Benchmarking is Limited in Reinforcement Learning Research
Scott Jordan, Adam White, Bruno da Silva et al.
Position: Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis
Jessica Dai
Position: Building Guardrails for Large Language Models Requires Systematic Design
Yi DONG, Ronghui Mu, Gaojie Jin et al.
Position: Categorical Deep Learning is an Algebraic Theory of All Architectures
Bruno Gavranović, Paul Lessard, Andrew Dudzik et al.
Position: Compositional Generative Modeling: A Single Model is Not All You Need
Yilun Du, Leslie Kaelbling
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
Florian Tramer, Gautam Kamath, Nicholas Carlini
Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities
Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh
Position: Data-driven Discovery with Large Generative Models
Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal et al.
Position: Do Not Explain Vision Models Without Context
Paulina Tomaszewska, Przemyslaw Biecek
Position: Do pretrained Transformers Learn In-Context by Gradient Descent?
Lingfeng Shen, Aayush Mishra, Daniel Khashabi
Position: Embracing Negative Results in Machine Learning
Florian Karl, Malte Kemeter, Gabriel Dax et al.
Position: Evolving AI Collectives Enhance Human Diversity and Enable Self-Regulation
Shiyang Lai, Yujin Potter, Junsol Kim et al.
Position: Explain to Question not to Justify
Przemyslaw Biecek, Wojciech Samek
Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training
Lin Lu, Chenxi Dai, Wangcheng Tao et al.
Position: Foundation Agents as the Paradigm Shift for Decision Making
Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao et al.
Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches
David Glukhov, Ilia Shumailov, Yarin Gal et al.
Position: Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym et al.
Position: Insights from Survey Methodology can Improve Training Data
Stephanie Eckman, Barbara Plank, Frauke Kreuter
Position: Is machine learning good or bad for the natural sciences?
David W. Hogg, Soledad Villar
Position: Key Claims in LLM Research Have a Long Tail of Footnotes
Anna Rogers, Sasha Luccioni
Position: Leverage Foundational Models for Black-Box Optimization
Xingyou Song, Yingtao Tian, Robert Lange et al.
Position: Machine Learning-powered Assessments of the EU Digital Services Act Aid Quantify Policy Impacts on Online Harms
Eleonora Bonel, Luca Nannini, Davide Bassi et al.
Position: Measure Dataset Diversity, Don't Just Claim It
Dora Zhao, Jerone Andrews, Orestis Papakyriakopoulos et al.
Position: Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen et al.
Position: On the Possibilities of AI-Generated Text Detection
Souradip Chakraborty, Amrit Singh Bedi, Sicheng Zhu et al.
Position: On the Societal Impact of Open Foundation Models
Sayash Kapoor, Rishi Bommasani, Kevin Klyman et al.
Position: Open-Endedness is Essential for Artificial Superhuman Intelligence
Edward Hughes, Michael Dennis, Jack Parker-Holder et al.
Position: Opportunities Exist for Machine Learning in Magnetic Fusion Energy
Lucas Spangher, Allen Wang, Andrew Maris et al.
Position: Optimization in SciML Should Employ the Function Space Geometry
Johannes Müller, Marius Zeinhofer
Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?
M. Saquib Sarfraz, Mei-Yen Chen, Lukas Layer et al.
Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems
Yifan Xia, Xianliang Yang, Zichuan Liu et al.
Position: Scaling Simulation is Neither Necessary Nor Sufficient for In-the-Wild Robot Manipulation
Homanga Bharadhwaj
Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized
Shomik Jain, Kathleen A. Creel, Ashia Wilson
Position: Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
Vincent Conitzer, Rachel Freedman, Jobstq Heitzig et al.
Position: Social Environment Design Should be Further Developed for AI-based Policy-Making
Edwin Zhang, Sadie Zhao, Tonghan Wang et al.
Position: Stop Making Unscientific AGI Performance Claims
Patrick Altmeyer, Andrew Demetriou, Antony Bartlett et al.
Position: Technical Research and Talent is Needed for Effective AI Governance
Anka Reuel, Lisa Soder, Benjamin Bucknall et al.