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Improving Baselines in the Wild
Kazuki Irie · Imanol Schlag · Róbert Csordás · Jürgen Schmidhuber
Event URL: https://openreview.net/forum?id=9vxOrkNTs1x »

We share our experience with the recently released WILDS benchmark which is a collection of ten datasets dedicated to developing models and training strategies which are robust to domain shifts. From a handful of experiments, we find a couple of critical observations which we believe are of general interest for any future work on WILDS. Our study focuses on two datasets: iWildCam and FMoW. We show that (1) conducting separate cross-validation for each evaluation metric is crucial for both datasets (2) a weak correlation between validation and test performance might make model development difficult for iWildCam (3) minor changes in the training of hyper-parameters improve the baseline by a relatively large margin (mainly on FMoW) (4) there is a strong correlation between certain domains and certain target labels (mainly on iWildCam). To the best of our knowledge, no prior work on these datasets has reported these observations despite their obvious importance.

Author Information

Kazuki Irie (Swiss AI Lab, IDSIA (USI & SUPSI))
Imanol Schlag (IDSIA)
Róbert Csordás (IDSIA)
Jürgen Schmidhuber (Swiss AI Lab, IDSIA (USI & SUPSI); NNAISENSE; KAUST)

Since age 15 or so, the main goal of professor Jürgen Schmidhuber has been to build a self-improving Artificial Intelligence (AI) smarter than himself, then retire. His lab's Deep Learning Neural Networks based on ideas published in the "Annus Mirabilis" 1990-1991 have revolutionised machine learning and AI. By the mid 2010s, they were on 3 billion devices, and used billions of times per day through users of the world's most valuable public companies, e.g., for greatly improved (CTC-LSTM-based) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion LSTM-based translations per day), Apple's Siri and Quicktype on all iPhones, the answers of Amazon's Alexa, and numerous other applications. In 2011, his team was the first to win official computer vision contests through deep neural nets, with superhuman performance. In 2012, they had the first deep NN to win a medical imaging contest (on cancer detection). All of this attracted enormous interest from industry. His research group also established the fields of mathematically rigorous universal AI and recursive self-improvement in metalearning machines that learn to learn (since 1987). In 1990, he introduced unsupervised adversarial neural networks that fight each other in a minimax game to achieve artificial curiosity (GANs are a special case). In 1991, he introduced very deep learning through unsupervised pre-training, and neural fast weight programmers formally equivalent to what's now called linear Transformers. His formal theory of creativity & curiosity & fun explains art, science, music, and humor. He also generalized algorithmic information theory and the many-worlds theory of physics, and introduced the concept of Low-Complexity Art, the information age's extreme form of minimal art. He is recipient of numerous awards, author of over 350 peer-reviewed papers, and Chief Scientist of the company NNAISENSE, which aims at building the first practical general purpose AI. He is a frequent keynote speaker, and advising various governments on AI strategies.

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