Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Franke, Joerg K. H."'
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
Accurate RNA secondary structure prediction is vital for understanding cellular regulation and disease mechanisms. Deep learning (DL) methods have surpassed traditional algorithms by predicting complex features like pseudoknots and multi-interacting
Externí odkaz:
http://arxiv.org/abs/2401.05351
Regularization is a critical component in deep learning training, with weight decay being a commonly used approach. It applies a constant penalty coefficient uniformly across all parameters. This may be unnecessarily restrictive for some parameters,
Externí odkaz:
http://arxiv.org/abs/2311.09058
Many Self-Supervised Learning (SSL) methods aim for model invariance to different image augmentations known as views. To achieve this invariance, conventional approaches make use of random sampling operations within the image augmentation pipeline. W
Externí odkaz:
http://arxiv.org/abs/2310.03940
Autor:
Koehler, Gregor, Wald, Tassilo, Ulrich, Constantin, Zimmerer, David, Jaeger, Paul F., Franke, Jörg K. H., Kohl, Simon, Isensee, Fabian, Maier-Hein, Klaus H.
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initi
Externí odkaz:
http://arxiv.org/abs/2309.07513
Experimental screening and selection pipelines for the discovery of novel riboswitches are expensive, time-consuming, and inefficient. Using computational methods to reduce the number of candidates for the screen could drastically decrease these cost
Externí odkaz:
http://arxiv.org/abs/2307.08801
The field of RNA secondary structure prediction has made significant progress with the adoption of deep learning techniques. In this work, we present the RNAformer, a lean deep learning model using axial attention and recycling in the latent space. W
Externí odkaz:
http://arxiv.org/abs/2307.10073
Our world is ambiguous and this is reflected in the data we use to train our algorithms. This is particularly true when we try to model natural processes where collected data is affected by noisy measurements and differences in measurement techniques
Externí odkaz:
http://arxiv.org/abs/2205.13927
After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This question poses a challenging problem, as developer adjustments can chan
Externí odkaz:
http://arxiv.org/abs/2010.13117
Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters. This sensitivity can par
Externí odkaz:
http://arxiv.org/abs/2009.01555