Using either framework will reduce the headache that is from-scratch development and will allow the engineer/developer to build faster, and create solutions which are more easily explained thanks to respective profilers and TensorBoard. Simply put: there are too many unofficial tutorials covering the same topics and the code quality varies too greatly; these tutorials are noise and can be ignored. TensorFlow, Keras, and PyTorch all offer awesome, free tutorials; and the docs are organized into a textbook like manner. Familiarizing with a study framework like SQ3R, KWT, or PQRST will aid in using the docs to learn about AI/ML while also learning to use the framework. If needing Python standard examples, the site RealPython seems to be reliable and consistent; however, a docs-first research method is best.

Domain generalization asks for models trained on a set of training environments to perform well on unseen test environments. Recently, a series of algorithms such as Invariant Risk Minimization (IRM) has been proposed for domain generalization. However, Rosenfeld et al. (2021) shows that in a simple linear data model, even if non-convexity issues are ignored, IRM and its extensions cannot generalize to unseen environments with less than $d_s+1$ training environments, where $d_s$ is the dimension of the spurious-feature subspace. In this paper, we propose to achieve domain generalization with Invariant-feature Subspace Recovery (ISR). Our first algorithm, ISR-Mean, can identify the subspace spanned by invariant features from the first-order moments of the class-conditional distributions, and achieve provable domain generalization with $d_s+1$ training environments under the data model of Rosenfeld et al. (2021). Our second algorithm, ISR-Cov, further reduces the required number of training environments to $O(1)$ using the information of second-order moments. Notably, unlike IRM, our algorithms bypass non-convexity issues and enjoy global convergence guarantees. Empirically, our ISRs can obtain superior performance compared with IRM on synthetic benchmarks. In addition, on three real-world image and text datasets, we show that ISR-Mean can be used as a simple yet effective post-processing method to increase the worst-case accuracy of trained models against spurious correlations and group shifts.

Contrastive learning aims to extract distinctive features from data by finding an embedding representation where similar samples are close to each other, and different ones are far apart. We study generalization in contrastive learning, focusing on its simplest representative: Siamese Neural Networks (SNNs). We show that Double Descent also appears in SNNs and is exacerbated by noise. We point out that SNNs can be affected by two distinct sources of noise: Pair Label Noise (PLN) and Single Label Noise (SLN). The effect of SLN is asymmetric, but it preserves similarity relations, while PLN is symmetric but breaks transitivity. We show that the dataset topology crucially affects generalization. While sparse datasets show the same performances under SLN and PLN for an equal amount of noise, SLN outperforms PLN in the overparametrized region in dense datasets. Indeed, in this regime, PLN similarity violation becomes macroscopical, corrupting the dataset to the point where complete overfitting cannot be achieved. We call this phenomenon Density-Induced Break of Similarity (DIBS). We also probe the equivalence between online optimization and offline generalization for similarity tasks. We observe that an online/offline correspondence in similarity learning can be affected by both the network architecture and label noise.

「お父さんAIスケッチ」で使われている、TensorFlowとPython3で画像生成（GAN：Generative Adversarial Network。敵対的生成ネットワーク）に挑戦。TensorFlowによる開発手順も確認できます。

One of the worst things is that it can perpetuate systematic discrimination and unfairness. Specifically, this can mean women not getting hired due to biased training data. People of color being denied employment, loan consideration, housing, and other benefits because the data is biased. In one pandemic-era example, certain students unable to sit for exams were excluded from university simply because they came from a historically poorly performing high school, despite their own excellent personal records. The algorithm generating proxy test scores was inherently biased.

The graph represents a network of 1,000 Twitter users whose tweets in the requested range contained "fashiontech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 27 January 2022 at 06:58 UTC. The requested start date was Thursday, 27 January 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 11-day, 20-hour, 0-minute period from Friday, 14 January 2022 at 22:59 UTC to Wednesday, 26 January 2022 at 19:00 UTC.

We propose RemixIT, a simple and novel self-supervised training method for speech enhancement. The proposed method is based on a continuously self-training scheme that overcomes limitations from previous studies including assumptions for the in-domain noise distribution and having access to clean target signals. Specifically, a separation teacher model is pre-trained on an out-of-domain dataset and is used to infer estimated target signals for a batch of in-domain mixtures. Next, we bootstrap the mixing process by generating artificial mixtures using permuted estimated clean and noise signals. Finally, the student model is trained using the permuted estimated sources as targets while we periodically update teacher's weights using the latest student model. Our experiments show that RemixIT outperforms several previous state-of-the-art self-supervised methods under multiple speech enhancement tasks. Additionally, RemixIT provides a seamless alternative for semi-supervised and unsupervised domain adaptation for speech enhancement tasks, while being general enough to be applied to any separation task and paired with any separation model.

In recent years there has been a growing interest in the estimation of causal effects using machine learning algorithms, particularly in the field of economics (Athey, 2018). The newly emerging synthesis of machine learning methods with causal inference has a large potential for a more comprehensive estimation of causal effects (Lechner, 2018). On the one hand, it enables a more flexible estimation of average effects which are of main interest in microeconometrics (Imbens & Wooldridge, 2009). On the other hand, it advances the estimation beyond the average effects and allows for a systematic analysis of effect heterogeneity (Athey & Imbens, 2017). Both of these aspects contribute to a better description of the causal mechanisms and thus to a possibly more efficient treatment allocation (Zhao, Zeng, Rush, & Kosorok, 2012; Kitagawa & Tetenov, 2018; Athey & Wager, 2021; Nie, Brunskill, & Wager, 2021). Hence, applied empirical researchers can greatly benefit from the usage of machine learning methods ranging from evaluation of public policies and business decisions to designing personalized interventions (Andini, Ciani, de Blasio, D'Ignazio, & Salvestrini, 2018; Bansak et al., 2018). Machine learning estimators as such are, however, primarily designed for prediction problems and thus cannot be used directly for causal inference. Therefore, new approaches for the estimation of causal parameters using machine learning emerged (see Athey & Imbens, 2019, for an overview). In particular, the development of the so-called meta-learners have received considerable attention (see e.g.

When software providers talk about the technologies they say "democratize" AI, they also talk a lot about "guardrails." That's because the rapidly evolving world of AI tools is still more like a republic governed by the machine-learning elite. Although no-code and low-code AI tools promise to give everyone a chance to build business analytics models or simple applications that use AI to complete tedious tasks, the amateurs whom no-code AI companies refer to as "citizen data scientists" are often required to play with the bumper rails up. That's because toolmakers and management are worried about the risks inherent in allowing just anyone to create sophisticated AI systems. "As you go into low-code and actually more the no-code environment, then there are guardrails as to what you can and can't do," said Ed Abbo, president and chief technology officer at C3 AI, which provides software designed to help people with zero coding experience build machine learning models.

Intel, Dell and the American Association of Community Colleges launched the AI Incubator Network, which will build labs at 10 colleges around the U.S. Colleges can apply for $10,000 each to equip the physical lab and build artificial intelligence training programs. Applications are due Feb. 25. Michelle Johnston Holthaus, executive vice president and general manager at Intel, said in a press release that this specialized program is a starting point for the next generation of U.S. technologists, engineers and inventors. This latest workforce development project is part of Intel's AI for Workforce program that launched last year. Brian Gonzalez, executive director of government, partnerships and initiatives at Intel, said the new program makes AI accessible to novice learners.