Kryptonite-N: Machine Learning Strikes Back
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Albus, Bailey, Nathan, Sumerfield, Will, Kim, Kira |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
by: Li, Albus Yizhuo, et al.
Published: (2026)
by: Li, Albus Yizhuo, et al.
Published: (2026)
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
by: Jain, Abhinav, et al.
Published: (2024)
by: Jain, Abhinav, et al.
Published: (2024)
Attackers Strike Back? Not Anymore -- An Ensemble of RL Defenders Awakens for APT Detection
by: Benabderrahmane, Sidahmed, et al.
Published: (2025)
by: Benabderrahmane, Sidahmed, et al.
Published: (2025)
TBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning
by: Sam, Kira
Published: (2024)
by: Sam, Kira
Published: (2024)
Learning to Move Like Professional Counter-Strike Players
by: Durst, David, et al.
Published: (2024)
by: Durst, David, et al.
Published: (2024)
Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
by: Hu, Yaowei, et al.
Published: (2024)
by: Hu, Yaowei, et al.
Published: (2024)
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
by: Rangwani, Harsh, et al.
Published: (2024)
by: Rangwani, Harsh, et al.
Published: (2024)
The Expert Strikes Back: Interpreting Mixture-of-Experts Language Models at Expert Level
by: Herbst, Jeremy, et al.
Published: (2026)
by: Herbst, Jeremy, et al.
Published: (2026)
Can Machines Learn the True Probabilities?
by: Kim, Jinsook
Published: (2024)
by: Kim, Jinsook
Published: (2024)
An Unsupervised Machine Learning Approach for Ground-Motion Spectra Clustering and Selection
by: Bond, R. Bailey, et al.
Published: (2022)
by: Bond, R. Bailey, et al.
Published: (2022)
SFO: Learning PDE Operators via Spectral Filtering
by: Koren, Noam, et al.
Published: (2026)
by: Koren, Noam, et al.
Published: (2026)
Back to the Drawing Board for Fair Representation Learning
by: Pouget, Angéline, et al.
Published: (2024)
by: Pouget, Angéline, et al.
Published: (2024)
Back to the Future: Look-ahead Augmentation and Parallel Self-Refinement for Time Series Forecasting
by: Kim, Sunho, et al.
Published: (2026)
by: Kim, Sunho, et al.
Published: (2026)
To FP8 and Back Again: Quantifying Reduced Precision Effects on LLM Training Stability
by: Lee, Joonhyung, et al.
Published: (2024)
by: Lee, Joonhyung, et al.
Published: (2024)
Quantifying Generalisation in Imitation Learning
by: Gavenski, Nathan, et al.
Published: (2025)
by: Gavenski, Nathan, et al.
Published: (2025)
Establishing Central Sensitization Inventory Cut-off Values in patients with Chronic Low Back Pain by Unsupervised Machine Learning
by: Zheng, Xiaoping, et al.
Published: (2023)
by: Zheng, Xiaoping, et al.
Published: (2023)
Minimax Strikes Back
by: Cohen-Solal, Quentin, et al.
Published: (2020)
by: Cohen-Solal, Quentin, et al.
Published: (2020)
Learning Admissible Heuristics for A*: Theory and Practice
by: Futuhi, Ehsan, et al.
Published: (2025)
by: Futuhi, Ehsan, et al.
Published: (2025)
Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Mining
by: Li, Pochun
Published: (2024)
by: Li, Pochun
Published: (2024)
Reinforcement Learning via Auxiliary Task Distillation
by: Harish, Abhinav Narayan, et al.
Published: (2024)
by: Harish, Abhinav Narayan, et al.
Published: (2024)
Understanding Foundation Models: Are We Back in 1924?
by: Smeaton, Alan F.
Published: (2024)
by: Smeaton, Alan F.
Published: (2024)
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
by: Liu, Yuansan, et al.
Published: (2024)
by: Liu, Yuansan, et al.
Published: (2024)
Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control
by: Lawrence, Nathan P., et al.
Published: (2025)
by: Lawrence, Nathan P., et al.
Published: (2025)
CoMind: Towards Community-Driven Agents for Machine Learning Engineering
by: Li, Sijie, et al.
Published: (2025)
by: Li, Sijie, et al.
Published: (2025)
Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning
by: Gavenski, Nathan, et al.
Published: (2026)
by: Gavenski, Nathan, et al.
Published: (2026)
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
by: Wang, Xin, et al.
Published: (2022)
by: Wang, Xin, et al.
Published: (2022)
Machine Unlearning in Contrastive Learning
by: Wang, Zixin, et al.
Published: (2024)
by: Wang, Zixin, et al.
Published: (2024)
Simple Lifelong Learning Machines
by: Dey, Jayanta, et al.
Published: (2020)
by: Dey, Jayanta, et al.
Published: (2020)
On Leakage in Machine Learning Pipelines
by: Sasse, Leonard, et al.
Published: (2023)
by: Sasse, Leonard, et al.
Published: (2023)
Reinforcement Learning for Machine Learning Engineering Agents
by: Yang, Sherry, et al.
Published: (2025)
by: Yang, Sherry, et al.
Published: (2025)
Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning
by: Shan, Zikang, et al.
Published: (2026)
by: Shan, Zikang, et al.
Published: (2026)
Research Program: Theory of Learning in Dynamical Systems
by: Hazan, Elad, et al.
Published: (2025)
by: Hazan, Elad, et al.
Published: (2025)
Imitation Learning Datasets: A Toolkit For Creating Datasets, Training Agents and Benchmarking
by: Gavenski, Nathan, et al.
Published: (2024)
by: Gavenski, Nathan, et al.
Published: (2024)
Looping Back to Move Forward: Recursive Transformers for Efficient and Flexible Large Multimodal Models
by: Xu, Ruihan, et al.
Published: (2026)
by: Xu, Ruihan, et al.
Published: (2026)
TraM : Enhancing User Sleep Prediction with Transformer-based Multivariate Time Series Modeling and Machine Learning Ensembles
by: Kim, Jinjae, et al.
Published: (2024)
by: Kim, Jinjae, et al.
Published: (2024)
Towards Generalisable Imitation Learning Through Conditioned Transition Estimation and Online Behaviour Alignment
by: Gavenski, Nathan, et al.
Published: (2026)
by: Gavenski, Nathan, et al.
Published: (2026)
Learning to Answer from Correct Demonstrations
by: Joshi, Nirmit, et al.
Published: (2025)
by: Joshi, Nirmit, et al.
Published: (2025)
Generalized Back-Stepping Experience Replay in Sparse-Reward Environments
by: Lyu, Guwen, et al.
Published: (2024)
by: Lyu, Guwen, et al.
Published: (2024)
Application-Driven Innovation in Machine Learning
by: Rolnick, David, et al.
Published: (2024)
by: Rolnick, David, et al.
Published: (2024)
Information-Theoretic Foundations for Machine Learning
by: Jeon, Hong Jun, et al.
Published: (2024)
by: Jeon, Hong Jun, et al.
Published: (2024)
Similar Items
-
Variational Routing: A Scalable Bayesian Framework for Calibrated Mixture-of-Experts Transformers
by: Li, Albus Yizhuo, et al.
Published: (2026) -
Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
by: Jain, Abhinav, et al.
Published: (2024) -
Attackers Strike Back? Not Anymore -- An Ensemble of RL Defenders Awakens for APT Detection
by: Benabderrahmane, Sidahmed, et al.
Published: (2025) -
TBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning
by: Sam, Kira
Published: (2024) -
Learning to Move Like Professional Counter-Strike Players
by: Durst, David, et al.
Published: (2024)