Παρακολούθηση
Andreas Kirsch
Andreas Kirsch
Άγνωστη συνεργασία
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα google.com - Αρχική σελίδα
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Παρατίθεται από
Παρατίθεται από
Έτος
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
A Kirsch, J van Amersfoort, Y Gal
Advances in Neural Information Processing Systems (NeurIPS), 7024-7035, 2019
7162019
Deep Deterministic Uncertainty: A New Simple Baseline
J Mukhoti, A Kirsch, J van Amersfoort, PHS Torr, Y Gal
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
266*2023
Prioritized training on points that are learnable, worth learning, and not yet learnt
S Mindermann, JM Brauner, MT Razzak, M Sharma, A Kirsch, W Xu, ...
International Conference on Machine Learning (ICML), 15630-15649, 2022
1512022
Plex: towards reliability using pretrained large model extensions (2022)
D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ...
URL https://arxiv. org/abs/2207.07411, 0
132*
Prediction-Oriented Bayesian Active Learning
F Bickford Smith, A Kirsch, S Farquhar, Y Gal, A Foster, T Rainforth
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023
62*2023
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ...
Transactions on Machine Learning Research (TMLR), 2023
48*2023
Causal-bald: Deep bayesian active learning of outcomes to infer treatment-effects from observational data
A Jesson, P Tigas, J van Amersfoort, A Kirsch, U Shalit, Y Gal
Advances in Neural Information Processing Systems (NeurIPS) 34, 30465-30478, 2021
352021
Sampling methods
M Hanke, A Kirsch
Handbook of mathematical methods in imaging, 2009
232009
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities
A Kirsch, Y Gal
Transactions on Machine Learning Research (TMLR), 2022
222022
Unpacking information bottlenecks: Unifying information-theoretic objectives in deep learning
A Kirsch, C Lyle, Y Gal
Workshop Uncertainty & Robustness in Deep Learning at Int. Conf. on Machine …, 2020
22*2020
A Note on "Assessing Generalization of SGD via Disagreement"
A Kirsch, Y Gal
Transactions on Machine Learning Research (TMLR), 2022
202022
Black-Box Batch Active Learning for Regression
A Kirsch
Transactions on Machine Learning Research, 2023
82023
Does Deep Learning on a Data Diet reproduce? Overall yes, but GraNd at Initialization does not
A Kirsch
Transactions on Machine Learning Research, 2023
72023
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions
A Kirsch
arXiv preprint arXiv:2401.04305, 2024
62024
A Practical & Unified Notation for Information-Theoretic Quantities in ML
A Kirsch, Y Gal
arXiv preprint arXiv:2106.12062, 2021
52021
CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training
D Brandfonbrener, H Zhang, A Kirsch, JR Schwarz, S Kakade
arXiv preprint arXiv:2406.10670, 2024
42024
Turning up the heat: Min-p sampling for creative and coherent llm outputs
M Nguyen, A Baker, C Neo, A Roush, A Kirsch, R Shwartz-Ziv
arXiv preprint arXiv:2407.01082, 2024
22024
MDP environments for the OpenAI Gym
A Kirsch
arXiv preprint arXiv:1709.09069, 2017
22017
Min P Sampling: Balancing Creativity and Coherence at High Temperature
M Nguyen, A Baker, A Kirsch, C Neo
arXiv e-prints, arXiv: 2407.01082, 2024
12024
Bridging the Data Processing Inequality and Function-Space Variational Inference
A Kirsch
The Third Blogpost Track at ICLR 2024, 2024
12024
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