Orthogonality constraints naturally appear in many machine learning problems, from Principal Components Analysis to robust neural network training. They are usually solved using Riemannian optimization algorithms, which minimize the objective function while enforcing the constraint. However, enforcing the orthogonality constraint can be the most time-consuming operation in such algorithms. Recently, Ablin and Peyré (2022) proposed the Landing algorithm, a method with cheap iterations that does not enforce the orthogonality constraint but is attracted towards the manifold in a smooth manner. In this article, we provide new practical and theoretical developments for the landing algorithm. First, the method is extended to the Stiefel manifold, the set of rectangular orthogonal matrices. We also consider stochastic and variance reduction algorithms when the cost function is an average of many functions. We demonstrate that all these methods have the same rate of convergence as their Riemannian counterparts that exactly enforce the constraint. Finally, our experiments demonstrate the promise of our approach to an array of machine-learning problems that involve orthogonality constraints.

Stochastic gradient descent samples uniformly the training set to build an unbiased gradient estimate with a limited number of samples. However, at a given step of the training process, some data are more helpful than others to continue learning. Importance sampling for training deep neural networks has been widely studied to propose sampling schemes yielding better performance than the uniform sampling scheme. After recalling the theory of importance sampling for deep learning, this paper reviews the challenges inherent to this research area. In particular, we propose a metric allowing the assessment of the quality of a given sampling scheme; and we study the interplay between the sampling scheme and the optimizer used.

Protein language models (pLMs), pre-trained via causal language modeling on protein sequences, have been a promising tool for protein sequence design. In real-world protein engineering, there are many cases where the amino acids in the middle of a protein sequence are optimized while maintaining other residues. Unfortunately, because of the left-to-right nature of pLMs, existing pLMs modify suffix residues by prompting prefix residues, which are insufficient for the infilling task that considers the whole surrounding context. To find the more effective pLMs for protein engineering, we design a new benchmark, Secondary structureE InFilling rEcoveRy, SEIFER, which approximates infilling sequence design scenarios. With the evaluation of existing models on the benchmark, we reveal the weakness of existing language models and show that language models trained via fill-in-middle transformation, called ProtFIM, are more appropriate for protein engineering. Also, we prove that ProtFIM generates protein sequences with decent protein representations through exhaustive experiments and visualizations.

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Natixis in Portugal is fully integrated in the global organization of Natixis, a French multinational financial services firm specialized in Asset & Wealth Management, Corporate & Investment Banking, Insurance and Payments. A subsidiary of Groupe BPCE, Natixis counts nearly 16.000 employees across 38 countries. Based in Porto, Natixis Centre of Expertise mission is to transform traditional banking by developing innovative solutions for the bank's business, operations and work culture worldwide, as a key driver of the company's culture of agility and innovation. Teams of IT and Banking Support Activities work in an integrated, inclusive and transversal way, supporting all the business lines and country platforms. Natixis in Portugal is the best combination of a "start-up mindset" with a large, solid structure.

Artificial intelligence (AI) language models like ChatGPT, BLOOM, and OPT-175B are a hot topic of conversation in academic circles. What are they? Should they be allowed in educational settings? Will they make us dumber? Will their use lead to widespread cheating? Can we use them to promote critical thinking and writing skills? How? To answer these questions, let's ask ChatGPT.

ChatGPT, the powerful new artificial intelligence tool from OpenAI that can answer questions, chat with humans, and generate text, has dominated headlines in the past few months. The tool is advanced enough to pass law school exams (though with fairly low scores), but it has also veered into strange conversations and has shared misinformation. It also highlights an important area that companies using or thinking about using AI need to confront: how to embrace AI in a way that doesn't harm humans. "Leadership involves absolutely centering the human and being rigorous before releasing into the wild things that affect these humans," saidRenée Richardson Gosline, a senior lecturer and principal research scientist at MIT Sloan. "Having the courage and ethics to say we want to cultivate a system and a relationship with our customers whereby we don't simply always extract, but we also share value -- that's what leads to loyalty in the long term."

Every week I get dozens of emails about ChatGPT and student education. As a professor of law focused on technology and a mom of three kids I am fielding messages from my kids' schools, from my workplace, and from colleagues near and far. They share a sense of panic about the AI chatbot that is able to almost magically--and instantly--generate schoolwork that students for centuries produced through laborious research and writing. How will students learn to research and write? How will we assess them?

The realm of computer vision and artificial intelligence is a fascinating one, filled with incredible possibilities and groundbreaking applications. When I first delved into this world, I was brimming with excitement and eager to explore the depths of this technology, hoping to create something awe-inspiring. However, I soon realized that my journey was not going to be an easy one. My journey began with a series of visits to GitHub repositories, hoping to find answers to my computer vision questions. But instead of finding clarity, I found myself lost in a maze of code, unable to decipher the enigma of each repository.

Artificial intelligence (AI) is a rapidly growing field that has captured the attention of scientists, engineers, business leaders, and policymakers worldwide. It refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making. AI has the potential to transform various industries and sectors, including healthcare, transportation, education, manufacturing, and finance, among others. In this article, we will explore the importance of artificial intelligence in the future and its potential benefits and challenges. One of the most significant advantages of artificial intelligence is its ability to automate routine and repetitive tasks, allowing humans to focus on more complex and creative work.