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ICBINB@NeurIPS 2023 : ICBINB@NeurIPS2023 - Failure Modes in the Age of Foundation Models

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Link: https://sites.google.com/view/icbinb-2023/home
 
When Dec 16, 2023 - Dec 16, 2023
Where New Orleans
Submission Deadline Oct 1, 2023
Notification Due Oct 27, 2023
Final Version Due Nov 30, 2023
Categories    machine learning   NLP   philosophy of science
 

Call For Papers

Subject: Call For Papers ICBINB@NeurIPS2023 - Failure Modes in the Age of Foundation Models

We are happy to announce the I Can’t Believe It’s Not Better workshop at NeurIPS 2023. This year the workshop is titled Failure Modes in the Age of Foundation Models. We invite submissions that focus on surprising or negative results when using foundation models as well as submissions with more general negative results from machine learning. The full call for papers is below.

Key Information

Paper Submission Deadline - October 1, 2023 (Anywhere on Earth)

Workshop Website: https://sites.google.com/view/icbinb-2023/home

Call For Papers

The goal of the I Can’t Believe It’s Not Better workshop series is to promote “slow science” that pushes back against “leaderboard-ism”, and provides a forum to share surprising or negative results. In 2023 we propose to apply this same approach to the timely topic of foundation models.

The hype around ChatGPT, Stable Diffusion and SegmentAnything might suggest that all the interesting problems have been solved and artificial general intelligence is just around the corner. In this workshop we cooly reflect on this optimism, inviting submissions on failure modes of foundation models, i.e. unexpected negative results. In addition we invite contributions that will help us understand when we should expect foundation models to disrupt existing sub-fields of ML and when these powerful methods will remain complementary to another sub-field of machine learning.

We invite submissions on the following topics:
Failure modes of current foundation models (safety, explainability, methodological limitations, etc.)
Failure modes of applying foundation models, embeddings or other massive scale deep learning models.
Development of machine learning methodologies that benefit from foundation models, but necessitate other techniques.
Meta machine learning research and reflections on the impact of foundation models on the broader field of machine learning.
Negative scientific findings in a more general sense. In keeping with previous workshops we will accept findings on methodologies or tools that gave surprising negative results without foundation models. Such submissions are encouraged especially with discussion on the relevance of findings in the present climate where foundation models are changing the field.

Technical submissions may center on machine learning, deep learning or deep learning adjacent fields (causal DL, meta-learning, generative modelling, adversarial examples, probabilistic reasoning, etc) as well as domain specific applications.

Papers will be assessed on:
Clarity of writing
Rigor and transparency in the scientific methodologies employed
Novelty and significance of insights
Quality of discussion of limitations
Reproducibility of results

Selected papers will be optionally included in a special issue of PMLR. Alternatively, some authors may prefer their paper to be in the non-archival track which is to share preliminary findings that will later go to full review at another venue.

Related Resources

FL@FM-IJCAI 2024   International Workshop on Federated Learning in the Age of Foundation Models In Conjunction with IJCAI 2024
ECAI 2024   27th European Conference on Artificial Intelligence
FLLM 2024   The 2nd International Conference on Foundation and Large Language Models
AIM@EPIA 2024   Artificial Intelligence in Medicine
FL@FM-TheWebConf 2024   International Workshop on Federated Foundation Models for the Web 2024
ICMLA 2024   23rd International Conference on Machine Learning and Applications
FL@FM-ICME 2024   International Workshop on Federated Learning and Foundation Models for Multi-Media
ICDM 2024   IEEE International Conference on Data Mining
MODELS 2024   MODELS 2024 : ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems
COMIT 2024   8th International Conference on Computer Science and Information Technology