Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Zela, Arber"'
Autor:
Arango, Sebastian Pineda, Janowski, Maciej, Purucker, Lennart, Zela, Arber, Hutter, Frank, Grabocka, Josif
Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pretrained models are available. On the other hand, ensembles of neural n
Externí odkaz:
http://arxiv.org/abs/2410.19889
Autor:
Mueller, Andreas, Siems, Julien, Nori, Harsha, Salinas, David, Zela, Arber, Caruana, Rich, Hutter, Frank
Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data. Traditionally, training GAMs involves iterative learning algorithms, such as splines, boosted trees, or
Externí odkaz:
http://arxiv.org/abs/2410.04560
Autor:
Arango, Sebastian Pineda, Janowski, Maciej, Purucker, Lennart, Zela, Arber, Hutter, Frank, Grabocka, Josif
Ensemble methods are known for enhancing the accuracy and robustness of machine learning models by combining multiple base learners. However, standard approaches like greedy or random ensembles often fall short, as they assume a constant weight acros
Externí odkaz:
http://arxiv.org/abs/2410.04520
Autor:
Sukthanker, Rhea Sanjay, Zela, Arber, Staffler, Benedikt, Klein, Aaron, Purucker, Lennart, Franke, Joerg K. H., Hutter, Frank
The increasing size of language models necessitates a thorough analysis across multiple dimensions to assess trade-offs among crucial hardware metrics such as latency, energy consumption, GPU memory usage, and performance. Identifying optimal model c
Externí odkaz:
http://arxiv.org/abs/2405.10299
Autor:
Sukthanker, Rhea Sanjay, Zela, Arber, Staffler, Benedikt, Dooley, Samuel, Grabocka, Josif, Hutter, Frank
Pareto front profiling in multi-objective optimization (MOO), i.e. finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives like neural network training. Typically, in MOO neural architecture search (NAS
Externí odkaz:
http://arxiv.org/abs/2402.18213
Autor:
White, Colin, Safari, Mahmoud, Sukthanker, Rhea, Ru, Binxin, Elsken, Thomas, Zela, Arber, Dey, Debadeepta, Hutter, Frank
In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural archite
Externí odkaz:
http://arxiv.org/abs/2301.08727
Zero-cost proxies (ZC proxies) are a recent architecture performance prediction technique aiming to significantly speed up algorithms for neural architecture search (NAS). Recent work has shown that these techniques show great promise, but certain as
Externí odkaz:
http://arxiv.org/abs/2210.03230
Autor:
Elsken, Thomas, Zela, Arber, Metzen, Jan Hendrik, Staffler, Benedikt, Brox, Thomas, Valada, Abhinav, Hutter, Frank
The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering. As a consequence, neural architecture search (NAS), which aims at automatically designing neural network architectures in a data-dri
Externí odkaz:
http://arxiv.org/abs/2202.07242
Autor:
Mehta, Yash, White, Colin, Zela, Arber, Krishnakumar, Arjun, Zabergja, Guri, Moradian, Shakiba, Safari, Mahmoud, Yu, Kaicheng, Hutter, Frank
The release of tabular benchmarks, such as NAS-Bench-101 and NAS-Bench-201, has significantly lowered the computational overhead for conducting scientific research in neural architecture search (NAS). Although they have been widely adopted and used t
Externí odkaz:
http://arxiv.org/abs/2201.13396
Ensembles of CNN models trained with different seeds (also known as Deep Ensembles) are known to achieve superior performance over a single copy of the CNN. Neural Ensemble Search (NES) can further boost performance by adding architectural diversity.
Externí odkaz:
http://arxiv.org/abs/2107.04369